A Study and Design of Dissipative Physiological
Homeostasis with its Analysis in Cancer Disease
Linked to E.Coli Environment
A Thesis
Submitted by
Sudheer Patil
for the award of the degree
of
DOCTOR OF PHILOSOPHY
DEPARTMENT OF ELECTRONICS AND INSTRUMENTATION
ENGINEERING
Dr. MGR Educational and Research Institute
(Deemed University)
N.H. 4, Periar E.V.R. Road,
Maduravoyal, Chennai – 600 095
January 2011
ii
BONAFIDE CERTIFICATE
Certified that this thesis titled “A Study and Design of Dissipative
Physiological Homeostasis with its Analysis in Cancer Disease Linked to E.Coli
Environment” is the bona fide work of Mr. SUDHEER PATIL
who carried out
the research under my supervision. Certified further, that to the best of my knowledge
the work reported herein does not form part of any other thesis or dissertation of the
basis of which a degree or award was conferred on an earlier occasion on this or any
other candidate.
iii
DECLARATION
I declare that the thesis entitled “A STUDY AND DESIGN OF
DISSIPATIVE PHYSIOLOGICAL HOMEOSTASIS WITH ITS ANALYSIS IN
CANCER DISEASE LINKED TO E.COLI ENVIRONMENT” submitted by me for
the degree of Doctor of Philosophy is the record of research work carried out by me in
the Department of Computer Science and Engineering, under the guidance and
supervision of Dr. T.K.BASAK, and that any part of the thesis has not formed the
basis for the award of any degree, diploma, associateship, fellowship or other titles in
this university or any other university or other Institution of higher Learning.
iv
ACKNOWLEDGEMENT
I thank God & My Parents for guiding me and taking care of me all the time.
My life is so blessed because of them. First and foremost, I would like to thank and
express my sincere gratitude to my research supervisor, Dr.T.K.Basak, for giving me
the opportunity to work with him and for his continuous support, encouragement,
invaluable guidance, many fruitful discussions and valuable contributions throughout
the research. I am benefited a lot from his guidance, wisdom and most of all his
infinite patience and understanding, without which this thesis work may not have
been completed at all. My deepest gratitude’s are due to Dr.P.Arvindan, Dean,
Research, and Dr.Cyrilraj, Head of the dept. of CS&E, and Dr. S.Ravi, head of the
dept. of E&CE Dr. MGR, Education Research Institute (Deemed University), Chennai
for their interesting ideas and suggestion that enable me to break though the problems
in the research work. I would like to thank president & council members of HKE’s
Society, Gulbarga, for their support and encouragement for carrying out my research
work. I would also like to take this opportunity to thank our ex-principal,
Dr.V.D.Mytri & principal Dr.L.S.Biradar for his kind cooperation during this
period of research work. I would like to thank Prof.B.A.Patil, PDACEG and all my
friends who have encouraged me in carry out my research work. Lastly, but not least,
words cannot express my deepest gratitude to my Wife, Smt. Chandrakala & My
Children, Diksha, Shreya & Mallikarjun for their love, support, patience and for
being a source of inspiration throughout the period of this research work.
SUDHEER PATIL
v
ABSTRACT
The dissipative phase in homeostasis initiates the onset of entropy
conservation of the subject in all respect of physiological system.
The thesis is organized in to 10 chapters, the relevance and importance are
clearly elaborated for the assessment of the thesis.
The chapter 1 contains the introduction to the physiological dissipative
homeostasis whose significance and importance are linked with a control system
model. The control system model incorporate negative feedback for dissipative
homeostasis, however, it is to be mentioned here that dissipative phase requires the
proper coordination’s with the transduction phases linked with the respective
homeostasis for initiation of entropy transaction in every living subjects irrespective
of its origin.
In chapter 2, literature survey based on the work done linked to dissipative
E.coli environment has been done thoroughly and it’s relevance in the context of the
thesis is also presented.
In chapter 3, the motivation and object of the present work are explained.
In chapter 4*, the iron homeostasis of E.coli linked with the dissipative stage
has been discussed, with the mentioning of appropriate reference. It is to be noted that
this dissipative model of iron homeostasis is very important to prevent the cellular
damage, which may cause or lead to initiation of serious diseases of which most
important is tumor or cancer.
-------------------------------------------------------------------------------------------------* A part of this chapter is published in
•
“A topological control system model of dissipative phenomena w.r.t. iron homeostasis linked to E.coli
environment”, communicated to international conference on “Biomedical and Pharmaceutical
Engineering”, ICBPE-2011 at Singapore.
vi
In chapter 5**, a novel concept of electrostrictive energy linked with
capacitance relaxation phenomenon (T.K.Basak, U.S. patent No. 5691178, 1997) has
been introduced.
It is to be noted that with the progression of cancer the dielectric property of
the membrane with cancer gradually decays with the concomitant decline of
electrostrictive energy.
This dissipative phenomenon in respect of the electrostrictive energy can also
be analyzed in quantum mechanical algorithm, since this dissipation is not a
continuous it varies in steps.
In chapter 6***, the metastasis has been linked with capacitance relaxation
phenomenon and its realization has been made with the dissipative phase of the E.coli
environment. For this realization a broad based analysis has been made with artificial
neural network (ANN). With this analysis it may be possible to explore the apoptosis
of cancer cell linked with the metastasis in simulated E.coli environment. In this
respect, the application of cancer research in the E.coli environment is to be
encouraged.
In chapter 7****, the dissipative model of hydrophobicity with the antiporter
linked with the E.coli environment has been discussed. This dissipative model is
related to the loss of surface of surface charge in the pH environment of the E.coli.
The phenomenon of hydrophobicity is correlated with the coupling of proteins during
transcription phase of mRNA in the nucleotide of the cell, which is to be explored
effectively in the cancer research.
-------------------------------------------------------------------------------------------------** A part of this chapter is reviewed and selected for publication in
• “A Model Linked to E.coli Related to Electrostrictive Energy in Cancer Cells”, International Journal of
Sensors and Transducers, vol.113, issue 2, February-2010, pp.158-166.
*** A part of this chapter is reviewed and selected for publication in
• “ANN analysis of E.coli Environment Related to the Stages of Cancer”, “International Journal of
Medicine and Medical-Sciences (JMMS)”.
**** A part of this chapter is reviewedand selected for publication in
• “A Model of Antiporter Linked to Hydrophobicity of E.coli”, international journal “African Journal of
Microbiology”.
vii
Now, in the chapter 8*****, the dynamic of human body have been discussed
with respect to dissipative phase related to the thermal energy. It is to be noted that
the correlation of the dynamics of human brain with human body is also of great
importance.
It has also been reviewed and discussed about the automated diagnosis method
for the tumor. So in chapter 9******, a review on an automated bone marrow
diagnosis using advanced image processing application is carried out.
Finally, the conclusion and future possible scope of work are discussed in
chapter 10.
-------------------------------------------------------------------------------------------------***** A part of this chapter is published in
•
“Analysis of human brain dynamics in coherence with human body with regards to dissipative
phase/model of thermal energy”, selected and published in proceedings of International Conference on
Systemics, Cybernetics and Informatics, ICSCI-2011, January 05-08, 2011, pp 97-100.
****** A part of this chapter is published in the proceedings
•
“Automated bone marrow diagnosis using advanced image processing applications”, selected and
published in proceedings of International Conference at REC, Bhalki, Karnataka, 29-30 october-2010,
pp.346-349.
viii
TABLE OF CONTENTS
Abstract
Table of Contents
List of Tables
List of Figures
List of Symbols, Abbreviations or Nomenclature (optional)
CHAPTER 1
INTRODUCTION
1.1
01 – 57
Homeostat
01
1.1.1
Requirement of homeostat
02
1.1.2
Concept of transduction phase and modified
02
homeostat
1.1.3
1.2
Biofeedback Homeostat
03
Homeostasis
04
1.2.1
Control Mechanism
14
1.2.1.1
15
Positive feedback and Negative
feedback
1.2.2
Homeostatic imbalance
17
1.2.3
Classification and varieties of Homeostasis
17
1.2.3.1
Temperature Homeostasis
18
1.2.3.2
Iron Homeostasis
19
1.2.3.3
Energy Homeostasis
19
1.2.3.4
Sugar Homeostasis
20
1.2.3.5
Fluid Homeostasis
20
1.2.3.6
Calcium Homeostasis
20
1.2.3.7
Acid-base Homeostasis
21
1.2.3.8
Fluid balance Homeostasis
21
ix
1.3
1.2.3.9
Hemostasis Homeostasis
21
1.2.3.10
Sleep Homeostasis
22
1.2.3.11
Extracellular fluid Homeostasis
22
The Dissipative Model/Phase Homeostasis
22
1.3.1
The theory of dissipative structure
22
1.3.2
Dissipative structure in physiological systems
23
1.3.3
Order in a non-equilibrium state
23
1.3.4
Entropy and sustainability of dissipative
25
systems
1.3.5
Homeostasis and the behaviour of open
25
systems
1.4
CHAPTER 2
Symtoms and Stages of Cancer
27
1.4.1
Cancer Staging
28
1.4.2
Considerations in Staging
29
1.4.3
Overall Stage Grouping
29
1.5
E.Coli (Escherichia Coli)
29
1.6
Capacitance Relaxation Phenomena
30
1.7
pH Homeostat and Cellular Signaling Homeostat
31
REVIEW OF LITERATURE
33 – 44
2.1
Motivation
33
2.2
Literature Survey
33
2.3
Concluding Remark
44
CHAPTER 3
MOTIVATION AND OBJECTIVE OF THE CURRENT
WORK
CHAPTER 4
IRON HOMEOSTASIS OF ESCHERICHIA COLI
(E.COLI) MEDIATED BY GENETIC REGULATION-A
DISSIPATIVE PHENOMENA
4.1
Introduction
4.2
Mathematical Model of the Network Controlling Iron
Flow
45
46 – 63
46
48
x
4.3
Construction of a Mathematical Model of the Iron
56
Network
4.4
Kinetic Responses of the Network to Rapid Changes N
57
the Available Extracellular Iron Concentration
4.5
CHAPTER 5
Discussion
61
4.5.1
Response to iron perturbations
61
4.5.2
Regulation of Fe–S cluster assembly
62
4.5.3
Role of small RNA regulation
62
4.5.4
Conclusion
63
A DISSIPATIVE MODEL IN THE E.COLI
64 – 73
ENVIRONMENT RELATED TO ELECTROSTRICTIVE
ENERGY IN CANCER CELL
CHAPTER 6
5.1
Introduction
64
5.2
Modelling and Simulation
69
5.3
Conclusions
73
ANALYSIS OF E-COLI ENVIRONMENT RELATED TO
74 – 109
CANCER WITH ANN
6.1
6.2
Introduction
74
6.1.1
Artificial Neural Network
74
6.1.2
Network function
74
6.1.3
ANN dependency graph
75
6.1.4
Multilayered Network Architecture
76
6.1.5
Back Propagation Learning Algorithm
77
ANN as a Classifier
78
6.2.1
ANN Based Classifier
79
6.2.2
Existing System for Diagnosis of Cancer
80
Stages
6.2.3
6.3
Drawbacks of the Existing System
Proposed ANN Model
80
80
xi
6.4
6.3.1
System Architecture
81
6.3.2
Data set Preparation
81
6.3.3
Diagnosis
82
Implementation of Ann Based Classifier
82
6.4.1
Implementation in DTREG
82
6.4.1.1
Input Layer
83
6.4.1.2
Hidden Layer
83
6.4.1.3
Output Layer
84
6.4.1.4
Multilayer Perceptron Architecture
84
6.4.1.5
Training Multilayer Perceptron
84
Networks
6.5
Selecting the Number of Hidden Layers
85
6.5.1
85
Deciding how many neurons to use in the
hidden layers
6.5.2
Finding a globally optimal solution
85
6.5.3
Converging to the Optimal Solution –
86
Conjugate Gradient
6.5.4
6.6
6.7
6.8
6.9
Avoiding over fitting
86
ANN Implementation with MATLAB
87
6.6.1
87
Back propagation learning algorithm
Algorithm
89
6.7.1
Square Error Performance Function
89
6.7.2
Computation of neuronal signals
90
6.7.3
Computation of error gradients
90
6.7.4
Weight Updates
93
Experimental Results
94
6.8.1
Results of Ann as a Classifier in DTREG
99
6.8.2
ANN as a classifier in MATLAB
102
6.8.3
Weight matrices
105
Results of Classification in MATLAB
106
6.9.1
107
Performance Analysis
xii
6.10
CHAPTER 7
Conclusion
A DISSIPATIVE MODEL OF ANTIPORTER LINKED TO
107
109 – 114
HYDROPHOBICITY IN pH MEDIUM
CHAPTER 8
7.1
Introduction
109
7.2
A Mat Lab Model & Simulation
112
7.3
Conclusion and Discussion
114
ANALYSIS OF THERMAL ENERGY DISSIPATION OF
115 – 125
BRAIN WITH REGARDS TO HUMAN BODY-A STUDY
8.1
Introduction and Definitions
115
8.2
The Mind in Neuroscience as Dissipative Homeostat
115
8.3
The Conventional Model
119
8.4
The Dissipative Phase/Model
121
8.4.1
Coherent States
121
8.4.2
Phase transitions
122
8.5
8.6
CHAPTER 9
Result and Observations
122
8.5.1
Observation in dynamic environment
122
8.5.2
The thermal connection
123
8.5.3
Classicality and attractor landscapes
124
Conclusions
AUTOMATED BONE MARROW DIAGNOSIS USING
125
126 – 141
ADVANCED IMAGE PROCESSING APPLICATIONS-A
REVIEW
9.1
Introduction
126
9.1.1
Digital image processing fundamentals
126
9.1.2
Basics of Bone marrow cancer
132
9.2
Types of Cells
134
9.3
Medical Image Processing
134
9.3.1
134
Medical Imaging
xiii
9.3.2
9.4
Image Segmentation
135
Proposed Methods
136
9.4.1
Cell Segmentation
136
9.4.2
Futures Extraction
137
9.4.3
The geometrical features
138
9.4.4
Statistical Features Process
138
9.4.5
Identification Process
139
9.4.6
Classification Process
139
9.5
Results and Observations
140
9.6
Conclusions
140
CHAPTER 10 CONCLUSION AND SCOPE FOR FUTURE WORK
141 – 142
10.1
Conclusion
141
10.2
Scope of Future Work
142
REFERENCES
143
CURRICULAM VITAE
163
AUTHOR’S PUBLICATIONS
164
xiv
LIST OF TABLES
NO.
CAPTION
PAGE NO.
4.1
Parameters of the Fe–S channel that were used in the
simulations
53
6.1
Result of ANN implementation in DTREG
99
6.2
Display of Model size
100
6.3
Miscalassification of Training Data
101
6.4
Miscalassification of Validation Data
101
6.5
Weight matrix displaying weights between input and
hidden layer
105
6.6
Input and Output Simulations in MATLAB
106
6.7
Performance Analysis
107
xv
LIST OF FIGURES
No.
Figure caption
Page no.
1.1
Basic block diagram of various homeostats
03
1.2
Block diagram representation of biofeedback homeostat
03
1.3
Block Diagram of Negative Feedback Homeostat control
system.
08
1.4
Block Diagram of Positive Feedback Homeostat control
system.
15
1.5
An open system’s entropy production and dissipation
24
1.6
Homeostasis in an open system at t and t+s. Adapted from
Flood and Carson (1993)
26
1.7
Block Diagram of pH homeostat
31
4.1
Schematic model of iron flow control. Deep yellow
arrowheads indicate iron flow; green arrows indicate positive
effects on iron flow. Red lines indicate inhibition of
transcription, while blue lines indicate inhibition of translation
(via mRNA degradation). Iron pools distinguished in the
model are shown in deep yellow
54
4.2
Effects of perturbations in the available extracellular iron on
the loosely bound iron pool (top), on Fe–S cluster assembly
(middle), and on iron flow to the RyhB-regulated Fe-proteins
(bottom). Extracellular iron concentration was decreased from
17 to 0.2 mM at 0 time. At the 250 min time point the 17 mM
iron concentration was restored. Simulations were performed
at different initial distributions of the regulated iron fluxes,
indicated by the ratio of the iron flow to the RyhB-regulated
Fe-proteins at 17 mM extracellular iron (% RyhB flow). The
flux parameters for our standard simulation (red curve, see
also Figure 4.3) are the following: bin ¼ 3215 mM, bN ¼ 200
mM, bR ¼ 2530 mM, bI ¼ 58 and bS ¼ 34
58
xvi
4.3
Effect of the initial distribution of the regulated iron fluxes on
the steady state flows at decreased iron availability.
Simulations were performed at different initial distributions of
the regulated iron fluxes, indicated by the ratio of the iron
flow to the RyhB-regulated Fe-proteins at 17 mM
extracellular iron (RyhB outflow/Total regulated outflow).
Steady state iron flows to Fe–S cluster assembly (red) and to
the RyhB-regulated Fe-proteins (blue) at 0.2 mM extracellular
iron were plotted. Values are normalized to the corresponding
flows at 17 mM extracellular iron. By using the initial
distribution of fluxes corresponding to the maximum of the
red curve as a standard condition for simulations (see also
Figure 4.2).
59
4.4
The amount of isc transcript as a function of iron
concentration. RyhB and iscR RNA levels at low iron
concentration were used as a reference (¼1) to normalize
RyhB and isc RNA levels, respectively. The black curve
shows the level of iscSUA transcript which is regulated by
both the Fe–S–IscR complex and the small RNA RyhB. The
normalized RyhB level is shown in red. The blue curve
represents the amount of the IscR protein
60
5.1
Status of Oxidant/antioxidant of E. Coli Trps II with Respect
to the pH of its Environment.
65
5.2
Generalized model of metastasis at apostosis.
69
5.3
Oxidant/antioxidant response for .15PU changes in
Electrostrictive energy (ratio peak above 0.12).
70
5.4
Oxidant/antioxidant response for .15PU changes in
Electrostrictive energy (ratio peak below 0.12)
70
5.5
Oxidant/antioxidant response for .15PU changes in
Electrostrictive energy. (ratio peak above 0.05)
71
5.6
Oxidant/antioxidant response for .15PU changes in
Electrostrictive energy (ratio peak around 0.035)
71
5.7
Oxidant/Antioxidant ratio Vs pH
72
5.8
Mean Value of Oxidant/Antioxidant response Vs pH
73
6.1
ANN Dependency Graph
75
xvii
6.2
Generic architectre of a feedforward neural network
76
6.3
Block Diagram of Existing System
80
6.4
Block diagram of proposed system
81
6.5
Multilayer perceptron network
83
6.6
Oxidant/antioxidant ratio of E. Coli w.r.t. pH of its
Environment
95
6.7
Status of Electrostrictive energy in cancer cells
95
6.8
Capacitance relaxation in cancer cells
96
6.9
Capacitance relaxation- metastasis curve related to pH
96
6.10
Oxidant/ antioxidant ratio Vs Ph
98
6.11
Graph of Model Size Vs Error Rate
101
6.12
Graph of sumerror Vs iterations
102
6.13
Snapshot showing sumerror with increase in iterations
103
6.14
Snapshot displaying weight matrices at 2000th iteration
104
6.15
Snapshot displaying classification
105
6.16
weight matrix displaying weights between hidden and output
layer
106
7.1
Model of Lipid Peroxidation due to Na+/H+ antiporter
112
7.2
Lipid Peroxidation Vs pH due to 0.2 p.u change in Na+/H+
Antiporter
112
7.3
Lipid Peroxidation Vs pH due to 0.4 p.u change in Na+/H+
Antiporter
113
7.4
Lipid Peroxidation Vs pH due to 0.6 p.u change in Na+/H+
Antiporter
113
8.1
The sharp spikes (gray, De(t)) show the rate of change in
spatial AM pattern.
123
xviii
8.2
Thermal energy dissipation performance characteristic of
entropy variation with time. This corresponds to the choice of
a privileged direction in time-evolution called arrow of time.
124
9.1
Fundamental steps in digital image processing
129
9.2
The exemplary image of the bone marrow smear of the acute
leukemia patient containing different blast cells.
135
9.3
The segmented image of the bone marrow smear
137
9.4
The distribution of cell locations in plane formed by 11th
Markov feature corresponding to cytoplasm and nucleus.
140
9.5
The distribution of cell locations in the plane formed by 2
geometrical features
140
xix
LIST OF SYMBOLS
Symbols
Interpretation
ds
Change in entropy
dis
Change in entropy within the system
des
Change in entropy external to the system
α, β, γ
Cell region with different dispersions
Fur
Ferric uptake regulator
Fel
Loosely bound iron
τg
Dilution by cell division
Ptr
Overall iron transportation
τm
Passive degradation and dilution of the mRNA
Fa
Free energy function
Sa
Entropy operator
β (t)
Time constant
PKC
Protein Kinase C
KI
Binding constant of the Fe-S-IscR complex to its operator site at
the isc promoter region
KFS
Dissociation constant of the Fe-S-IscR
KFSP
Loosely bound iron concentration at which Fe-S incorporation
into proteins is half-maximal
βFeS
Scaling parameter for Fe-S incorporation into proteins
γ1
Scaling parameter for the formation of the RyhB-iscSUA mRNA
complex
αIsc
Parameters for Isc protein production
1
CHAPTER 1
INTRODUCTION
1.1
HOMEOSTAT
It’s a bio-controller which helps to regulate the various physiological
processes of human body to maintain equilibrium under the external or internal
disturbance or variations.
A self-organizing system that indicates the ability of living organisms to
maintain certain quantities (for example, temperature) within physiologically
permissible limits (Homeostasis).
The English scientist W.R.Ashby, the inventor of the homeostat, constructed
it in 1948 as a device consisting of four magnetic systems having crossed reverse
connections [vakhnenko A. G.-1962, Ashby W. R.-1959, 1968.]. Each connection is
regulated with the aid of a ring rheostat with taps that switch over when the magnets
shift, providing the system with several hundred thousand different states. When
there is an unstable state the magnets shift, randomly changing the pattern of
homeostat connections: a new position is sought in which a state of equilibrium with
the environment, given some randomness in its internal construction (for example,
given changing parameters, connections with the environment, and partial breakage).
A Dissipative homeostat in contrast to Conservative Homeostat signifies a
decreasing output response due to Dissipative nature of human body. A Dissipative
homeostat may be lost for a specific time period in the environment of the body
because with the change the environment of the body a dissipative homeostat can
become conservative.
2
A Dissipative homeostat is very important in for normal regulation of the
body function.
Homeostasis is the set of processes by which constant or ‘static’ conditions
are maintained within the internal environment of a subject [Guyton & Hall-2003];
and homeostat is a controller dedicated for the sustenance of the process.[ Neil J
Schroeder and John Cunninghum-2000, Basak TK-1992]
1.1.1
Requirement of homeostat:
1. Projection of the physiological activities in the concerned area of the model
under normal and abnormal condition of the subject
2. For normal condition of the subject homeostatic regulation is necessary and
this can be reflected through different homeostatic regulation mechanism
namely the temperature regulation, pH regulation and others.
1.1.2 Concept of transduction phase and modified homeostat:
Under the abnormal condition the homeostatic regulation of a subject is
present both in the normal and abnormal condition. Transduction phase is the
characteristic, which is dependent on the psychosomatic status of the subject for
which there are both normal and abnormal phases associated with it which is
included in the feedback path of the homeostat. [Basak Tapas K-2005, 1990, 1991,
1992, 1993, 1994, 1996 & 2001]
With the transduction phases it is possible to obtain both the conservative and
dissipative systems. In normal and abnormal condition, the subject undergoes
different transduction phases. A dissipative system diverges from its original state; it
may undergo successive stages during which the response decreases exponentially,
with the characteristic features of a normal physiological system. A conservative
system, in contrast, has an output characterized by exponentially rising phases. These
phases are illustrated in the output response of the homeostat discussed later.
3
The block diagram of various types of homeostats is given below.
Fig 1.1 Basic block diagram of various homeostats
1.1.3
Biofeedback Homeostat:
The block diagram of biofeedback homeostat is as follows:
Brain
Output
Input from sensory
organ
+ −
Biofeeback
Homeostat
*Transduction
Phase
Fig 1.2 Block diagram representation of biofeedback homeostat
4
The brain plays a very important role in biofeedback homeostasis. For
biofeedback activity of brain is controlled by Catecholamine interactions.
Catecholamines are excitatory or inhibitory neurotransmitters or hormonal agents.
The catecholamine neuro-hormones are epinephrine, norepinephrine, dopamine
and serotonin. Stimulation of sympathetic and parasympathetic nervous system
causes large quantities of epinephrine and norepinephrine to be released into the
circulating blood, which carries them to all tissues of the body. The transduction
phase of the homeostat is so designed that there is coordination between
catecholamine and receptor proliferation caused by epinephrine and norepinephrine.
The output from the biofeedback homeostat focusses normal as well as
abnormal condition of the subject depending upon the nature of transduction
phase.[Basak Tapas K-2005]
1.2
HOMEOSTASIS
It could be understood as a biological process which maintains equilibrium of
various physiological parameters of body by using relevant homeostat under external
or internal disturbance or variations which try to disturb the equilibrium.
Any self regulating process by which a biological or mechanical system
maintains stability while adjusting to changing conditions. Systems in dynamic
equilibrium reach a balance in which internal change continuously compensates for
external change in a feedback control process to keep conditions relatively uniform.
An example is temperature regulation mechanically in a room by a thermostat or
biologically in the body by a complex system controlled by the hypothalamus, which
adjusts breathing and metabolic rates, blood vessel dilation, and blood-sugar level in
response to changes caused by factors including ambient temperature, hormones, and
disease [W. B. Cannon-1926].
The maintenance of metabolic equilibrium within an animal by a tendency to
compensate for disrupting changes [Cannon WB-1929].
5
In higher animals, the maintenance of an internal constancy and an
independence of the environment [Cannon WB-1932].
The relatively constant conditions within organisms or the physiological
processes by which such conditions are maintained in the face of external variation
[Karl Ludwig von Bertalanffy-1970].
Similar homeostatic controls are used to keep factors such as temperature and
blood pressure nearly constant despite changes in an organism’s activity level or
surroundings. Such systems operate by detecting changes in the variable that the
system is designed to hold constant and initiating some action that offsets any
change. All incorporate a sensor within the system that responds when the actual
condition differs from the desired one, a device to ensure that any action taken will
reduce the difference between actual and desired, and an effector to take the needed
action as directed. The crucial aspect is that information is fed back from effector to
sensor and action is taken to reduce any imbalance hence the term negative feedback
[Bhagavan, N. V.-2002].
Blood pressure is, at least on a moment-to-moment basis, regulated by a
system for which the sensors are stretch-sensitive cells located in the neck arteries
that carry blood from hear to brain. An increase in blood pressure triggers sensor
activity; their signal passes to the brain; and, in turn, the nerve supplying the heart(
the vagus) is stimulated to release a chemical (acetylcholine) that causes the heart to
beat more slowly which decreases blood pressure[Ann M O'Hara-2006].
The volume of the blood is subject to similar regulation. Fluid (mainly
plasma) moves between the capillaries and the intercellular fluid in response to
changes in pressure in the capillaries. A decrease in blood volume is detected by
sensors at the base of the brain; the brain stimulates secretion of substances that
cause contraction of tiny muscles surrounding the blood vessels that lead into the
capillaries. The resulting arteriolar constriction reduces the flow of blood to, and the
6
pressure within, the capillaries, so fluid moves from intercellular space into
capillaries, thus restoring overall blood volume [Wyatt, James K-1999].
Body temperature in mammals is regulated by a sensor that consists of cells
within the hypothalamus of the brain. Several effectors are involved, which vary
among animals. These include increasing heat production through nonspecific
muscle activity such as shivering; increasing heat loss through sweating, panting, and
opening more blood vessels in the skin (vasodilation); and decreasing heat loss
through thickening of fur (piloerection) and curling up. Humans sweat, but they
retain only a vestige of piloerection (“goose flesh”).
While the homeostatic mechanisms described involve the neural and
endocrine systems of mammals, it is clear that such arrangements pervade systems
from genes to biological communities, and that they are used by the simplest and the
most complex organisms.
Organisms of every kind develop, mature, and even shift physiological states
periodically between day and night, with seasons, or as internal rhythms. Thus
organisms cannot be considered constant except over short periods. However, all
such changes appear to involve the same basic sensing of the results of the past
activity of the system and the adjusting of future activity in response to that
information. Development of an organism from a fertilized egg is far from a direct
implementation of a genetic program; probably no program could anticipate all the
variation in the external context in which an organism must somehow successfully
develop.
Homeostasis (Greek: staying the same) is a fundamental idea in our
understanding of the workings of the body. The concept had its origin in the 1870s,
when the French physiologist Claude Bernard showed that, although the
concentration of sugar in the blood could be raised or lowered by a number of
processes, the net effect of these processes was to keep the concentration of sugar
within certain limits. Bernard extended the idea to other constituents of blood — for
7
which he had less evidence — and in a timeless phrase referred to the constancy of
the internal environment (‘le milieu intérieur’): ‘La fixité du milieu intérieur est la
condition de la vie libre, independante.’
Bernard contrasted this constancy with that of the changeable world that
surrounded the animal (‘le milieu extérieur’). He likened the protective function of
the internal milieu to that of a greenhouse, though to us this may seem rather an odd
analogy. The constitution of the internal milieu (extracellular fluids, including blood
and lymph) has been suggested to represent some primal sea in which vertebrates
have evolved. It is a likeable hypothesis, but one which is rather difficult to test.
Bernard's proposal attracted little contemporary attention, which was hardly
surprising, for it was about 50 years ahead of its time. But during the period 1915-35
two American physiologists, W. B. Cannon (1871-1945) and L. J. Henderson (18781942), revived it. Cannon was particularly concerned with demonstrating the
importance of the autonomic nervous system in maintaining the constancy of the
milieu intérieur: he realized that the constancy of blood pressure was an essential part
of the maintenance. It was Cannon who actually coined the word ‘homeostasis’, and
in his Wisdom of the body (1932) he described how several of the body's systems
were involved in homeostatic mechanisms.
Cannon's fellow professor at Harvard, L. J. Henderson, analysed the way in
which the body maintained the hydrogen ion concentration of body fluids (usually
expressed as pH) within narrow limits. There is a short-term pH homeostasis which
is a property of blood itself: a bicarbonate-buffering system. If this is not adequate,
the kidneys cope with any larger deviation. Henderson published his findings in a
classic work, Blood: a study in general physiology (1928). The kidneys are,
incidentally, the homeostatic organs par excellence: every renal activity is involved
in maintaining the internal milieu, whether it is the concentration of ions in blood,
blood volume, blood pressure itself, or the excretion of alien substances.
8
How do the body's systems actually maintain the constancy? The most
conspicuous mechanism is generally known as ‘negative feedback’, illustrated below
in fig1.3.
Fig 1.3 Block Diagram of Negative Feedback Homeostat control system.
As an example, blood glucose concentration could be the ‘regulated variable’
in the diagram. The control system for the variable is the hormone insulin, whose
main action is to accelerate the entry of glucose into many of the cells of the body,
thereby lowering its plasma concentration. Insulin is released from cells in the Islets
of Langerhans of the pancreas (the controller), the most important stimulus for its
release being a rise in blood glucose concentration, as occurs after a meal
(‘disturbance’ in the diagram). The reason for this being a ‘negative’ feedback
system is that the action of insulin, by lowering the blood sugar, tends to remove the
stimulus for its own release. Negative feedback is a ubiquitious principle in
engineering and electronics.
It is clear from this example that the mechanism does not keep glucose
concentration (the regulated variable) at a fixed level. The level oscillates, because
there are delays in both arms of the system — it takes a finite time for insulin to
lower blood glucose concentrations, and also for elevated glucose concentrations to
increase the production of insulin from the pancreas.
9
Another regulated variable is carbon dioxide. The control of a constant partial
pressure of carbon dioxide (PCO2) in blood is a very precise feedback loop, and its
control system is the act of breathing. The body produces the gas constantly, adding
it to blood. The CO2 sensor in this system consists of neurons in the medullary
respiratory centre of the brain; the control system consists of motor nerves passing
from the brain to the diaphragm and intercostal muscles. These nerves stimulate the
act of breathing, which transfers carbon dioxide from blood into the lungs, lowers the
blood PCO2, and temporarily removes the stimulus to the medullary respiratory
centre. Because the body is still producing carbon dioxide, the blood PCO2 begins to
rise again, the medullary receptors are stimulated, and the cycle repeats itself. A
CO2-sensitive electrode inserted into an artery shows small, regular oscillations
whose frequency corresponds precisely to the act of breathing.
The speed of response of the carbon dioxide loop is far greater than that of
the glucose loop, a difference that derives from nervous compared with hormonal
mechanisms: the PCO2 varies by only about 10% around its average level, whereas
glucose varies by about 40%. The concentration ranges of some other constituents of
blood provide us with clues about the nature of the relevant homeostatic
mechanisms. Sodium ions (135-145 mmol/litre) and chloride ions (95-105
mmol/litre) have narrow ranges; this is the result of a mixture of nervous and
hormonal mechanisms; the range is wider for potassium (3.5-5.0 mmol/litre) which is
adjusted by hormonal action in the kidneys. By contrast, the hormones that provide
the control systems regulating these variables show far wider concentration ranges in
blood, according to the changes in secretion rates stimulated by disturbances in the
variable they control. Thus ACTH (adrenocorticotrophic hormone) has a range of
3.3-15.4 pmol/litre, aldosterone 100-500 pmol/litre, and insulin 0-15 mUnits/ml
(unfed) and 15-100 Units/ml (after food).
Homeostasis can itself be reset or entrained by higher nervous centres. The
diurnal variations shown by ACTH and cortisol demonstrate high concentrations
between midnight and midday (cortisol concentration 280-700 mmol/litre) and
midday and midnight (cortisol 140-280 mmol/litre). Similarly, on a longer time-
10
scale, the changes seen in the female reproductive cycle represent a 28-day cycle of
entrainment. On a longer time-scale still, the growth and development of the child
must represent the ultimate homeostatic entrainment by the brain. We might envisage
old age as representing a genetically programmed deterioration of homeostasis.
Claude Bernard's intuition about ‘le milieu intérieur’ has come a very long
way in a century. The mechanisms of homeostasis are so ubiquitous, their patterns so
subtly intertwined, that we are tempted to produce a teleological question, and ask
why. What is so useful to the organism about this precision? We do not have to look
far, because the workings of every cell in the body depend on the maintenance of a
negative potential inside the cell. In turn, this negative potential depends upon the
relative concentrations of ions inside and outside the cell: a high sodium
concentration in the extracellular fluid, and a high potassium concentration inside the
cell, the gradients across the cell wall being maintained by ionic pumps within the
cell membrane. But these pumps could not begin to control this gradient if the ionic
concentrations in blood (extracellular fluid) were not kept within narrow limits in the
first place. The subject comes into sharp focus when we consider the situation in the
heart, which is very dependent on a constant plasma potassium level, within the
range of 3.5-5.0 mmol/litre. The elevation of this value by 1-2 mmol/litre constitutes
a medical emergency: the excitable components of the heart begin to conduct
nervous impulses spontaneously and, without treatment, death soon follows from
uncoordinated contraction of different parts of the ventricles (ventricular fibrillation).
It soon becomes clear that the body's function involves countless homeostatic
mechanisms, both within and outside cells. Not only are the mechanisms ubiquitous,
but careful analyses often shows two or more feedback loops apparently serving the
same function; a good example is the elaborate relationship that exists between the
control of blood pressure and plasma volume. Perhaps the apparent redundancy
provides the organism with back-up systems that improve evolutionary survival
value. Improvement or not, such duplication makes the understanding of disease
processes very much more difficult to disentangle.
11
The maintenance of a constant physical or chemical state. Many processes in
the body are under homeostatic control: deviations of output from a normal level (set
point or norm) activate corrective mechanisms to bring the level back to normal.
Temperature regulation is an example of a homeostatic mechanism. The usual
set point for the core temperature is 37 degrees Celsius (37°C): body temperatures
above this norm result in sweating and an increase in blood flow to the skin to cool
the body; low body temperatures result in an increase in basal metabolic rate (more
fuel is burnt by the liver) and shivering to generate heat.
Other, homeostatic mechanisms include those controlling blood glucose
levels, blood acidity, and hormone secretions. There are also suggestions that
percentage fat composition and body weight have similar control systems (see
adipostat and set point theory).
The main concept of homeostasis is the principle of negative feedback
control, which was developed for military purposes in the Second World War. It is
the basis of cybernetics, whose founding fathers were Norbert Wiener, Ross Ashby,
and Grey Walter. Producing stability in dynamic systems by negative feedback has,
however, a history back to James Watt's governor for the automatic regulation of
steam engines as their load varies, and the still earlier controls for windmills. There
are even hints of the notion of feeding the output of a system back to its input for
maintaining stability in some ancient Greek devices, described by Hero in the 1st
century
AD,
especially for maintaining constant supply for water-clocks by a float
and needle valve, as in modern carburettors.
The term 'homeostasis' was coined some years before cybernetics, by the
American physician Walter B. Cannon, in his germinal book Wisdom of the Body,
1932. It is interesting that Cannon stated the basic idea of feedback as a fundamental
physiological principle before it was properly recognized by engineers, though it had
been used as it were implicitly, without recognition or understanding. Cannon
explained the regulation of body temperature by mechanisms such as perspiring
12
when the body is too hot and shivering when it is too cold, as maintaining the body's
equilibrium by feedback signals from what is needed to how what is needed can be
attained. It is now clear that this is an extremely important principle for almost all
physiological processes, and also for the guiding of skilled behavior.
Homeostasis (from Greek: ὅµοιος, hómoios, "similar"; and στάσις, stásis,
"standing still"; defined by Claude Bernard and later by Walter Bradford Cannon in
1926, 1929 and 1932) is the property of a system, either open or closed, that
regulates its internal environment and tends to maintain a stable, constant condition.
Typically used to refer to a living organism, the concept came from that of milieu
interieur that was created by Claude Bernard and published in 1865. Multiple
dynamic equilibrium adjustment and regulation mechanisms make homeostasis
possible.
With regards to any given life system parameter, an organism may be a
conformer or a regulator. On one hand, regulators try to maintain the parameter at a
constant level over possibly wide ambient environmental variations. On the other
hand, conformers allow the environment to determine the parameter. For instance,
endothermic animals (mammals and birds) maintain a constant body temperature,
while exothermic animals (almost all other organisms) exhibit wide body
temperature variation.
Behavioral adaptations allow endothermic animals to exert some control over
a given parameter. For instance, reptiles often rest on sun-heated rocks in the
morning to raise their body temperature. Regulators are also responsive to external
circumstances, however: if the same sun-baked boulder happens to host a ground
squirrel, the animal's metabolism will adjust to the lesser need for internal heat
production.
An advantage of homeostatic regulation is that it allows an organism to
function effectively in a broad range of environmental conditions. For example,
ectotherms tend to become sluggish at low temperatures, whereas a co-located
13
endotherm may be fully active. That thermal stability comes at a price since an
automatic regulation system requires additional energy. One reason snakes may eat
only once a week is that they use much less energy to maintain homeostasis.
Most homeostatic regulation is controlled by the release of hormones into the
bloodstream. However, other regulatory processes rely on simple diffusion to
maintain a balance.
Homeostatic regulation extends far beyond the control of temperature. All
animals also regulate their blood glucose, as well as the concentration of their blood.
Mammals regulate their blood glucose with insulin and glucagon. The human body
maintains glucose levels constant most of the day, even after a 24-hour fast. Even
during long periods of fasting, glucose levels are reduced only very slightly. Insulin,
secreted by the beta cells of the pancreas, effectively transports glucose to the body's
cells by instructing those cells to keep more of the glucose for their own use. If the
glucose inside the cells is high the cells will convert it to the insoluble glycogen to
prevent the soluble glucose interfering with cellular metabolism. Ultimately this
lowers blood glucose levels, and Insulin helps to prevent hyperglycemia. When
insulin is deficient or cells become resistant to it, diabetes occurs. Glucagon, secreted
by the alpha cells of the pancreas, encourages cells to break down stored glycogen or
convert non-carbohydrate carbon sources to glucose via gluconeogenesis, thus
preventing hypoglycemia. The kidneys are used to remove excess water and ions
from the blood. These are then expelled as urine. The kidneys perform a vital role in
homeostatic regulation in mammals, removing excess water, salt, and urea from the
blood. These are the body's main waste products.
Another homeostatic regulation occurs in the gut. Homeostasis of the gut is
not fully understood but it is believed that Toll-like receptor (TLR) expression
profiles contribute to it. Intestinal epithelial cells exhibit important factors that
contribute to homeostasis: 1) they have different cellular distribution of TLR’s
compared to the normal gut mucosa. An example of this is how TLR5 (activated by
flagellin) can redistribute to the basolateral membrane, which is the perfect place
14
where flagellin can be detected. 2) The enterocytes express high levels of TLR
inhibitor Toll-interacting protein (TOLLIP). TOLLIP is a human gene that is a part
of the innate immune system and is highest in a healthy gut; it correlates to luminal
bacterial load. 3) Surface enterocytes also express high levels of Interleukin-1
receptor (IL-1R) -containing inhibitory molecule. IL-1R are also referred to as single
immunoglobulin IL-1R (SIGIRR). Animals deficient in this are more susceptible to
induced colitis, implying that SIGIRR might possibly play a role in tuning mucosal
tolerance towards commensal flora. Nucleotide-binding oligomerisation domain
containing 2 (NOD2) is suggested to have an effect on suppressing inflammatory
cascades based on recent evidence.[ It is believed to modulate signals transmitted
through TLRs, TLR3, 4, and 9 specifically. Mutation of it has resulted in Crohn's
disease. Excessive T-helper 1 responses to resident flora in the gut are controlled by
inhibiting the controlling influence of regulatory T cells and tolerance-inducing
dendritic cells.
Sleep timing depends upon a balance between homeostatic sleep propensity,
the need for sleep as a function of the amount of time elapsed since the last adequate
sleep episode, and circadian rhythms that determine the ideal timing of a correctly
structured and restorative sleep episode.
1.2.1 Control Mechanism
All homeostatic control mechanisms have at least three interdependent
components for the variable being regulated: The receptor is the sensing component
that monitors and responds to changes in the environment. When the receptor senses
a stimulus, it sends information to a control center, the component that sets the
range at which a variable is maintained. The control center determines an appropriate
response to the stimulus. In most homeostatic mechanisms the control center is the
brain. The control center then sends signals to an effector, which can be muscles,
organs or other structures that receive signals from the control center. After receiving
the signal, a change occurs to correct the deviation by either enhancing it with
positive feedback or depressing it with negative feedback.
15
1.2.1.1 Positive feedback and Negative feedback
‘positive
feedback’
Fig 1.4 Block Diagram of Positive Feedback Homeostat control system.
Positive feedback is a mechanism by which an output is enhanced, such as
protein levels. However, in order to avoid any fluctuation in the protein level, the
mechanism is inhibited stochastically (I), therefore when the concentration of the
activated protein (A) is past the threshold ([I]), the loop mechanism is activated and
the concentration of A increases exponentially if d[A]=k [A]
Positive feedback mechanisms are designed to accelerate or enhance the
output created by a stimulus that has already been activated.
Unlike negative feedback mechanisms that initiate to maintain or regulate
physiological functions within a set and narrow range, the positive feedback
mechanisms are designed to push levels out of normal ranges. To achieve this
purpose, a series of events initiates a cascading process that builds to increase the
effect of the stimulus. This process can be beneficial but is rarely used by the body
due to risks of the acceleration's becoming uncontrollable.
One positive feedback example event in the body is blood platelet
accumulation, which, in turn, causes blood clotting in response to a break or tear in
the lining of blood vessels. Another example is the release of oxytocin to intensify
the contractions that take place during childbirth.
16
Negative feedback (fig.1.3) mechanisms consist of reducing the output or
activity of any organ or system back to its normal range of functioning. A good
example of this is regulating blood pressure. Blood vessels can sense resistance of
blood flow against the walls when blood pressure increases. The blood vessels act as
the receptors and they relay this message to the brain. The brain then sends a
message to the heart and blood vessels, both of which are the effectors. The heart rate
would decrease as the blood vessels increase in diameter (or vasodilation). This
change would cause the blood pressure to fall back to its normal range. The opposite
would happen when blood pressure decreases, and would cause vasoconstriction.
Another important example is seen when the body is deprived of food. The
body would then reset the metabolic set point to a lower than normal value. This
would allow the body to continue to function, at a slower rate, even though the body
is starving. Therefore, people who deprive themselves of food while trying to lose
weight would find it easy to shed weight initially and much harder to lose more after.
This is due to the body readjusting itself to a lower metabolic set point to allow the
body to survive with its low supply of energy. Exercise can change this effect by
increasing the metabolic demand.
Another good example of negative feedback mechanism is temperature
control. The hypothalamus, which monitors the body temperature, is capable of
determining even the slightest of variation of normal body temperature (37 degrees
Celsius). Response to such variation could be stimulation of glands that produces
sweat to reduce the temperature or signaling various muscles to shiver to increase
body temperature.
Both feedbacks are equally important for the healthy functioning of one's
body. Complications can arise if any of the two feedbacks are affected or altered in
any way.
17
1.2.2
Homeostatic imbalance
Many diseases are a result of disturbance of homeostasis, a condition known
as homeostatic imbalance. As it ages, every organism will lose efficiency in its
control systems. The inefficiencies gradually result in an unstable internal
environment that increases the risk for illness. In addition, homeostatic imbalance is
also responsible for the physical changes associated with aging. Even more serious
than illness and other characteristics of aging is death. Heart failure has been seen
where nominal negative feedback mechanisms become overwhelmed and destructive
positive feedback mechanisms then take over.
Diseases that result from a homeostatic imbalance include diabetes,
dehydration, hypoglycemia, hyperglycemia, gout, and any disease caused by a toxin
present in the bloodstream. All of these conditions result from the presence of an
increased amount of a particular substance. In ideal circumstances, homeostatic
control mechanisms should prevent this imbalance from occurring, but, in some
people, the mechanisms do not work efficiently enough or the quantity of the
substance exceeds the levels at which it can be managed. In these cases, medical
intervention is necessary to restore the balance, or permanent damage to the organs
may result.
1.2.3
Classification and varieties of Homeostasis
The Dynamic Energy Budget theory for metabolic organization delineates
structure and (one or more) reserves in an organism. Its formulation is based on three
forms of homeostasis:
•
Strong homeostasis, whereas structure and reserve do not change in
composition. Because the amount of reserve and structure can vary, this
allows a particular change in the composition of the whole body (as explained
by the Dynamic Energy Budget theory).
•
Weak homeostasis, wherein the ratio of the amounts of reserve and structure
becomes constant as long as food availability is constant, even when the
organism grows. This means that the whole body composition is constant
during growth in constant environments.
18
•
Structural homeostasis, wherein the sub-individual structures grow in
harmony with the whole individual; the relative proportions of the individuals
remain constant.
Human body homeostasis refers to the body's ability to physiologically
regulate its inner environment to ensure its stability in response to fluctuations in the
outside environment and the weather. The liver, the kidneys, and the brain
(hypothalamus, the autonomic nervous system and the endocrine system) help
maintain homeostasis. The liver is responsible for metabolizing toxic substances and
maintaining carbohydrate metabolism. The kidneys are responsible for regulating
blood water levels, re-absorption of substances into the blood, maintenance of salt
and ion levels in the blood, regulation of blood pH, and excretion of urea and other
wastes.
An inability to maintain homeostasis may lead to death or a disease, a
condition known as homeostatic imbalance. For instance, heart failure may occur
when negative feedback mechanisms become overwhelmed and destructive positive
feedback mechanisms take over. Other diseases which result from a homeostatic
imbalance include diabetes, dehydration, hypoglycemia, hyperglycemia, gout and
any disease caused by the presence of a toxin in the bloodstream. Medical
intervention can help restore homeostasis and possibly prevent permanent damage to
the organs.
1.2.3.1 Temperature Homeostasis
Human thermoregulation
Humans are warm-blooded, maintaining a near-constant body temperature.
Thermoregulation is an important aspect of human homeostasis. Heat is mainly
produced by the liver and muscle contractions. Humans have been able to adapt to a
great diversity of climates, including hot humid and hot arid. High temperatures pose
serious stresses for the human body, placing it in great danger of injury or even
death. In order to deal with these climatic conditions, humans have developed
physiologic and cultural modes of adaptation.
19
Temperature may enter a circle of positive feedback, when temperature
reaches extremes of 45°C (113°F), at which cellular proteins denature, causing the
active site in proteins to change, thus causing metabolism stop and ultimately death.
1.2.3.2 Iron Homeostasis
Human iron metabolism
Iron is an essential element for human beings. The control of this necessary
but potentially toxic substance is an important part of many aspects of human health
and disease. Hematologists have been especially interested in the system of iron
metabolism because iron is essential to red blood cells. In fact, most of the human
body's iron is contained in red blood cells' hemoglobin, and iron deficiency is the
most common cause of anemia.
When body levels of iron are too low, then hepcidin in the duodenal
epithelium is decreased. This causes an increase in ferroportin activity, stimulating
iron uptake in the digestive system. An iron surplus will stimulate the reverse of this
process.
In individual cells, an iron deficiency causes responsive element binding
protein (IRE-BP) to bind to iron responsive elements (IRE) on mRNAs for
transferring receptors, resulting in increased production of transferring receptors.
These receptors increase binding of transferring to cells, and therefore stimulating
iron uptake.
1.2.3.3 Energy Homeostasis
Energy balance
Energy balance is the homeostasis of energy in living systems. It is measured
with the following equation:
Energy intake = internal heat produced + external work + storage.
It generally uses the energy unit Calorie (or kilogram calorie), which equals
the energy needed to increase the temperature of 1 kg of water by 1 °C. This is about
4.184 kJ.
20
1.2.3.4 Sugar Homeostasis
Blood sugar regulation
Blood glucose is regluated with two hormones, insulin and glucagon, both
released from the pancreas.
When blood sugar levels become too high, insulin is released from the
pancreas. Glucose, or sugar, is stored in body cells as glycogen, lowering the blood
sugar levels. On the other hand, when blood sugar levels become too low, glucagon
is released. It promotes the release of glycogen, converted back into glucose. This
increases blood sugar levels.
If the pancreas is for any reason unable to produce enough of these two
hormones, diabetes results.
1.2.3.5 Fluid Homeostasis
Osmoregulation
Osmoregulation is the active regulation of the osmotic pressure of bodily
fluids to maintain the homeostasis of the body's water content; that is it keeps the
body's fluids from becoming too dilute or too concentrated. Osmotic pressure is a
measure of the tendency of water to move into one solution from another by osmosis.
The higher the osmotic pressure of a solution the more water wants to go into the
solution.
The kidneys are used to remove excess ions from the blood, thus affecting the
osmotic pressure. These are then expelled as urine.
1.2.3.6 Calcium Homeostasis
Calcium regulation in the human body
When blood calcium becomes too low, calcium-sensing receptors in the
parathyroid gland become activated. This results in the release of PTH, which acts to
increase blood calcium, e.g. by release from bones (increasing the activity of bone-
21
degrading cells called osteoclasts). This hormone also causes calcium to be
reabsorbed from urine and the GI tract.
Calcitonin, released from the C cells in the thyroid gland, works the opposite
way, decreasing calcium levels in the blood by causing more calcium to be fixed in
bone.
1.2.3.7 Acid-base Homeostasis
The kidneys maintain acid-base homeostasis by regulating the pH of the
blood plasma. Gains and losses of acid and base must be balanced. The study of the
acid-base reactions in the body is acid base physiology.
1.2.3.8 Fluid balance Homeostasis
The body's homeostatic control mechanisms, which maintain a constant
internal environment, ensure that a balance between fluid gain and fluid loss is
maintained. The hormones ADH (Anti-diuretic Hormone, also known as
vasopressin) and Aldosterone play a major role in this.
•
If the body is becoming fluid-deficient, there will be an increase in the
secretion of these hormones (ADH), causing fluid to be retained by the
kidneys and urine output to be reduced.
•
Conversely, if fluid levels are excessive, secretion of these hormones
(aldosterone) is suppressed, resulting in less retention of fluid by the kidneys
and a subsequent increase in the volume of urine produced.
•
If you have too much Carbon dioxide (CO2) in the blood, it can cause the
blood to become acidic. People respirate heavily not due to low oxygen (O2)
content in the blood, but because they have too much CO2.
1.2.3.9 Hemostasis Homeostasis
Hemostasis is the process whereby bleeding is halted. A major part of this is
coagulation. Platelet accumulation causes blood clotting in response to a break or
tear in the lining of blood vessels. Unlike the majority of control mechanisms in
human body, the hemostasis utilizes positive feedback, for the more the clot grows,
22
the more clotting occurs, until the blood stops. Another example of positive feedback
is the release of oxytocin to intensify the contractions that take place during
childbirth.
1.2.3.10 Sleep Homeostasis
Sleep timing depends upon a balance between homeostatic sleep propensity,
the need for sleep as a function of the amount of time elapsed since the last adequate
sleep episode, and circadian rhythms which determine the ideal timing of a correctly
structured and restorative sleep episode.
1.2.3.11 Extracellular fluid Homeostasis
The kidneys, by regulating the blood composition, also controls the
extracellular fluid homeostasis.
1.3
THE DISSIPATIVE MODEL/PHASE HOMEOSTASIS
The theory of dissipative homeostasis upon which the current work is based
can be treated as the open system with incorporating negative feedback closed loop
system model extended with a capability to continuously impose a revolutionary
change or transformation.
1.3.1
The theory of dissipative structure
Pioneered by the Brussels school of thought in the 1970s (Prigogine, 1976;
Nicolis and Prigogine, 1977, 1989; Prigogine and Stengers, 1984), this theory is
firmly rooted in physics and chemistry. Nevertheless, it was later applied to urban
spatial evolution (Allen and Sanglier, 1978, 1979a, 1979b, 1981), organisational
change and transformation (Gemmill and Smith, 1985; Leifer, 1989; Macintosh and
Maclean, 1999), changes in small groups and group dynamics (Smith and Gemmill,
1991), and political revolutions and change in political systems (Artigiani, 1987a,
1987b; Byeon, 1999).
23
1.3.2
Dissipative structure in physiological systems
The most prominent example of dissipative structure in a physical system is
convection in a liquid (Nicolis and Prigogine, 1977; Jantsch, 1980; Prigogine and
Stengers, 1984). If cooking oil is heated in a shallow pan, the following macroscopic
changes occur. Firstly, while the temperature of liquid is relatively uniform, heat is
transmitted through the body of liquid by means of conduction in which the
molecules’ heat energy (molecular vibration) is transmitted to neighbouring
molecules via collision without major change of position. We can say that the system
is still in a thermodynamic equilibrium. Next, as the pan is heated further, the
temperature gradient between the upper and lower portion of the oil in the pan
becomes more pronounced and thermal non-equilibrium increases. At a certain
temperature gradient, convection starts and heat is then transferred by the bulk
movement of molecules. Evidently, however, the surrounding environment at first
suppresses the smaller convection streams, but beyond a certain temperature
gradient, the fluctuations are reinforced rather than suppressed. The system moves
into a dynamic regime, switching from conduction to convection, and a new
macroscopic order called ‘Benard cells’ (i.e. a pattern of regular hexagonal cells that
appear on the surface of liquid) emerges, caused by a macroscopic fluctuation and
stabilised by an exchange of energy with the environment. Such a structure is called
a hydrodynamic dissipative structure, and is a version of spatial structure (Haken,
1980).
1.3.3
Order in a non-equilibrium state
As mentioned earlier, open systems make an effort to avoid a transition into
thermodynamic equilibrium by a continuous exchange of materials and energy with
the environment. By doing this, a negative entropy condition can be maintained. It
has been understood for a long time that entropy is a quantification of randomness,
uncertainty, and disorganization, and negative entropy therefore corresponds to
(relative) order, certainty, and organization (Bertalanffy, 1973; Kramer and De
Smith, 1977; Nicolis and Prigogine, 1977; Prigogine and Stengers, 1984; Miller,
1978; Van Gigch, 1978, 1991; Flood and Carson, 1993). However, the mechanics
24
underlying this idea had not been clear until it was explained in the work of Nicolis
and Prigogine (1977), Prigogine and Stengers (1984), and Jantsch (1980) in the
theory of dissipative structure and order that exists in the non-equilibrium condition.
According to the theory of dissipative structure, an open system has a
capability to continuously import free energy from the environment and, at the same
time, export entropy. As a consequence, the entropy of an open system can either be
maintained at the same level or decreased (negative entropy), unlike the entropy of
an isolated system (i.e. one that is completely sealed off from its environment),
which tends to increase toward a maximum at thermodynamic equilibrium. This
phenomenon can be represented in quantitative terms as follows (Nicolis and
Prigogine, 1977; Jantsch, 1980; Prigogine and Stengers, 1984). According to the
second law of thermodynamics, in any open system, change in entropy dS in a
certain time interval consists of entropy production due to an irreversible process in
the system (an internal component) diS and entropy flow due to exchange with the
environment (an external component) deS. Thus, a change in entropy in a certain
time interval can be represented as dS = deS + diS (where diS > 0). However, unlike
diS, the external component (deS) can be either positive or negative. Therefore, if deS
is negative and as numerically large as, or larger than, diS, the total entropy may
either be stationary (dS = 0) or decrease (dS < 0). In the former case, we can say that
the internal production of entropy and entropy exported to the environment are in
balance. An open system in a dissipative structure sense can be viewed as shown in
Figure1.5 below.
The system
diS
The environment
d eS
Fig. 1.5 An open system’s entropy production and dissipation.
25
It can be concluded that order in an open system can be maintained only in a
non-equilibrium condition. In other words, an open system needs to maintain an
exchange of energy and resources with the environment in order to be able to
continuously renew itself.
1.3.4
Entropy and sustainability of dissipative systems
The internal structure and development of dissipative systems, as well as the
process by which they come into existence, evolve, and expire, are governed by the
transfer of energy from the environment. Unlike isolated systems (or closed systems
in a broader sense), which are always on the path to thermal equilibrium, dissipative
systems have a potential to offset the increasing entropic trend by consuming energy
and using it to export entropy to their environment, thus creating negative entropy or
negentropy, which prevents the system from moving toward an equilibrium state. A
negentropic process is, therefore, the foundation for growth and evolution in
thermodynamic systems.
For dissipative systems to sustain their growth, they must not only increase
their negentropic potential, but they must also eliminate the positive entropy that
naturally accumulates over time as systems are trying to sustain themselves. The
buildup of the system’s internal complexity as it grows is always accompanied by the
production of positive entropy (diS > 0), which must be dissipated out of the system
as waste or low-grade energy. Otherwise, the accumulation of positive entropy in the
system will eventually bring it to thermodynamic equilibrium, a state in which the
system cannot maintain its order and organization (Harvey and Reed, 1997).
1.3.5
Homeostasis and the behaviour of open systems
It is necessary for many systems to maintain their equilibrium in changing
environments or disturbances, otherwise they cannot function properly or their goals
cannot be attained. In living systems, the process of self-maintenance or
‘homeostasis’ is essential to ensure their survival and viability. The term homeostasis
is referred to by Flood and Carson (1993) as a process by which a system preserves
its existence through the maintenance of its dynamic equilibrium. This equilibrium is
26
termed ‘homeostatic equilibrium’ (Van Gigch, 1978). Thus, a mature organism as an
open system appears to be unchanged over a period of time because there is a
continuous exchange and replacement of matter, energy, and information between
the system and the environment. Homeostasis can be explained mathematically as
follows (Flood and Carson, 1993): If we define x(t) as the state vector at time t and
x(t+s) as the state vector at time t+s, the preservation of the system’s condition over a
relatively short period of time can be represented by a statement: x(t) = x(t+s), which
means that at t+s, the identity of the organism may appear to be unchanged; however,
the actual materials that constitute the organism at time t will be partially or entirely
replaced by time t+s. This can be shown graphically as in Figure1.6 below.
Fig. 1.6 Homeostasis in an open system at t and t+s. Adapted from Flood and Carson
(1993).
Homeostasis is not only one of the most important properties of any living
organism, but is also readily applicable to human or work organizations treated as
open systems. The organization needs to recruit new employees to replace those who
retire; it also needs raw materials, energy, and information for use in its processes
27
and operations to maintain a steady state. In fact, an organization that appears
externally static and unchanged to outside observers is internally in a state of flux, in
a state of dynamic equilibrium.
Another significant aspect of an open system in a state of dynamic
equilibrium is that it relies on feedback mechanisms to remain in that state. Based on
Boulding’s system hierarchy, which classifies the system according to its
complexity, it is not surprising to find that properties exhibited by systems lower in
the hierarchy are also found in those higher in the hierarchy because the latter are
built on the former (Boulding, 1956). Therefore, a system that is classified as an open
system would possess all the qualities that belong to the system at a cybernetic (or
self-regulated systems) level. The behaviour of open systems is, to a great extent,
determined by the feedback mechanisms present in them. There are two types of
feedback that operate in most systems, namely negative and positive. Negative
feedback reduces or eliminates the system’s deviation from a given norm, so a
negative feedback mechanism tends to neutralize the effect of disturbance from the
environment so the system can maintain its normal course of operation. On the other
hand, positive feedback amplifies or accentuates change, which leads to a continuous
divergence from the starting state. Positive feedback works together with negative
feedback in living systems (e.g. in organisms, and organizations too, both types of
feedback are present during growth even though the net result is positive). However,
the operation of positive feedback alone will eventually result in the system’s
disintegration or collapse. Negative feedback plays the key role in the system’s
ability to achieve a steady state, or homeostasis.
1.4
SYMTOMS AND STAGES OF CANCER
Cancer is a group of diseases in which the cells are aggressive, grow and
divide in an uncontrolled manner, invading normal tissues and organs and eventually
spreading to other locations of the body (Metastasis). The fundamental abnormality
resulting in the development of cancer is the continuous unregulated proliferation of
normal cells.
28
The most important issue in cancer pathology is the distinction between
benign and malignant tumors. A tumor (a swelling) is any abnormal proliferation of
cells, which may be either benign or malignant. A benign tumor remains confined to
its original location, neither invading the surrounding normal tissues nor spreading to
distant body sites. A malignant tumor, however, is capable of both invading
surrounding normal tissue and spreading throughout the body via the circulatory or
lymphatic systems. Cancer cells sometimes can travel to other parts of the body
where they begin to grow and replace normal tissues. This process is called
metastasis. Only malignant tumors are referred to as cancer which is dangerous,
whereas benign tumor can usually be removed surgically.
The development of cancer is a multi step process in which cells gradually
become malignant through a progressive series of alternations. At the cellular level,
the development of cancer is viewed as a multi step process involving mutation and
selection for cells with progressively increasing capacity for proliferation, survival,
invasion, and metastasis. The first step in the process, tumor initiation, is the result of
a genetic alteration (mutation) leading to abnormal proliferation of a single cell. Cell
proliferation then leads to the outgrowth of derived tumor cells. Hormones,
particularly estrogens along with their receptors, are also the tumor promoters in the
development of some human cancers.
1.4.1
Cancer Staging
The stage of a cancer is a descriptor (usually numbers I to IV) of how much
the cancer has spread. The stage often takes into account the size of a tumor, how
deeply it has penetrated, whether it has invaded adjacent organs, and whether it has
spread to distant organs. Staging of cancer is important because the stage at diagnosis
is the most powerful predictor of survival, and treatments are often changed based on
the stage.
29
1.4.2
Considerations in Staging
Correct staging is critical because treatment is directly related to disease
stage. Thus, incorrect staging would lead to improper treatment, and material
diminution of patient survivability. Correct staging, however, can be difficult to
achieve.
1.4.3
Overall Stage Grouping
Overall Stage Grouping is also referred to as Roman Numeral Staging. This
system uses numerals I, II, III, and IV to describe the progression of cancer.
Stage I – Cancer is localized to one part of the body.
Stage II – Cancer is advanced.
Stage III – Cancer is more advanced, generally to the neighboring organs.
Stage IV – Cancer spread to other organs or throughout the body. This is the last
stage of cancer, not even responsive to chemotherapy.
1.5
E.COLI (ESCHERICHIA COLI)
E.Coli (Escherichia coli) is a bacteria that is commonly found in lower
intestine of warm blooded organisms. E.Coli is a common type of bacteria that can
get into food, like beef and vegetables. E.Coli normally lives inside your intestines,
where it helps your body break down and digest the food you eat. Unfortunately,
certain types (called strains) of E.Coli can get from the intestines into the blood.
E.Coli survives over a wide pH range.
E.Coli related archaeabacteria in lipid peroxidation is influenced by the
asymmetry of the lipid and the more the lipid peroxidation less is the relaxation time.
Asymmetry is linked with pH gradient mediated by lipid peroxidation. Relaxation of
polar head is related archaeabacteria. Na+/H+ antiporters cause enhancement of lipid
peroxidation. Antiporters maintain alkaline environment where as lipid peroxidation
initiated by antiporters maintains acidic pH homeostasis of the fluid of the E. Coli
archaeabacteria. Antiporters initiate lipid peroxidation which sustains pH gradient in
the environment of archaeabacteria.
30
Thus the asymmetry of polar head of archeobacteria (E.Coli) is sustained in
its pH environment mediated by the antiporters linked with the electrochemical
gradient across its membrane.
1.6
CAPACITANCE RELAXATION PHENOMENA
The capacitance relaxation phenomena of the normal cells and the cancer
cells, experimentally verified, are given below. The capacitance of a parallel-plate
capacitor constructed of two parallel plates of area A separated by a distance d is
given by the equation
C = k εo A /d
(1.1)
where C is the capacitance in farads, ε is the permittivity of the insulator used
(or ε0 for a vacuum) , A is the area of each plate in square meters, k is the dielectric
constant of the material between the plates, (air=1), d is the separation between the
plates, measured in meters.
In human body the cells have both intracellular and extra cellular fluids
separated by a cell membrane which is formed by lipids and minerals. The cell
membrane can be considered as a dielectric (either insulator or conductor). In healthy
cells the cell membrane behaves like an insulator that restricts the free movement of
charger ions and electrons across the membrane except through the specialised
portions and channels. However in the presence of a varying electric field across the
cell membrane, the dielectric constant exhibits variation as a function of frequency of
the electric field across the membrane. The process of change in capacitance as a
function of frequency is governed by Debye & Cole Relaxation Model. The variation
in capacitance is governed by three regions namely α, β and γ dispersions. In the
case of cell membranes, the electric field sets up dipoles across the cell membrane.
The dipoles have movements of counter ionic orientation and polarisation. This leads
to capacitive behaviour of cells at α region. Debye dispersion is the dissipation of
energy as the field passes through the medium.
31
1.7
PH HOMEOSTAT AND CELLULAR SIGNALING
HOMEOSTAT
pH is a measure of hydrogen ion concentration in body fluids. The pH value
of body fluids is an important parameter which is involved in different homeostatic
processes and it indicates the status of cancer cells in relation with pH of the blood.
pH value of blood is controlled by pH homeostasis along with its transduction phase
(feedback path) where the cell signaling homeostat interacts with other homeostats.
The pH homeostat incorporates all the features of acid base buffer systems
regulated by the kidney. The acid base buffer system and the respiratory system keep
the hydrogen ion concentration changing which affects the major organs of the body.
The pH homeostat is typically modeled in the below Figure 1.7.
Fig. 1.7 Block Diagram of pH homeostat
The kidneys can eliminate the excess acid or base from the body. The kidneys
regulate extra cellular fluid hydrogen ion concentration by secretion of hydrogen
ions, re-absorption of filtered bicarbonate ions and production of new bicarbonate
ions. The kidneys are the most powerful of the acid base regulatory systems. The
normal pH of the arterial blood is 7.4 whereas the pH of the venous blood and
interstitial blood is about 7.35 because of extra amount of carbon dioxide released
from the tissues to form carbonic acid in these fluids. A person is considered to have
acidosis when the pH falls below 7.0 and to have alkalosis when the pH rises above
32
7.0. The lower limit of pH at which a person can live more than a few hours is about
6.8 and the upper limit is about 8.0.
Tumors induce blood vessel growth (angiogenesis) through Vascular
Endothelial Growth Factor (VEGF). Over-expression of VEGF causes increased
permeability in blood vessels in simulating angiogenesis. Malignant cells exhibit
capacitance Relaxation phenomena and it has been correlated with VEGF. In the
process of pH homeostasis influenced by Ca and NO the cell signaling pathway is
modulated by NRF2 that tends to reduce the oxidative stress due to VEGF. This is
achieved through VEGF mRNA levels mediated through the increase in expression
of intracellular GSH.
The capacitance relaxation data is used as an input to calcium (Ca) and NO
homeostats. The transduction phase of pH homeostat is linked with cellular gene
expression. For example NRF2 mediated cellular gene expression causes antioxidant
response element for which the pH homeostat is responsible. The phenomena
involving the various factors NRF2, VEGF due to capacitance relaxation and pH
homeostat can be simulated in MATLAB 7.0.
33
CHAPTER 2
REVIEW OF LITERATURE
2.1
MOTIVATION
Analysis and design of any work should be backed by thorough
understanding already done or proposed in this direction .existing works in the
direction of analysis of cancer in E.coli environment, implementation of different
advance computational technique like ANN, image processing etc. are quite
important . Hence, this chapter will present the works that have been carried out in
the mentioned area.
2.2
LITERATURE SURVEY
The medical modeling due to proliferation of catecholamine by not using
Artificial Neural Network (ANN) was first proposed by T.K.Basak etal [Basak Tapas
K, Halder Suman, Kumar Madona, Sharma Renu and Midya Bijoylaxmi-2005] by
suitable designing of homeostat and transduction phase.
Cardiovascular property changes in chronic hemodialysis patients was first
introduced by Ikram H etal first.[Dinel J., Hicklin Lee ,M. Ellis-2005] Foley RN etal
[Weihua Z, Tsan R, Schroit AJ, Fidler IJ (2005)] focussed on the impact of anemia
on cardiomyopathy.
Kell D.B etal [Kell D.B., and Harris C.M.-1985] first observed the
consequences of diffusional motions of lipids and membranes. Gliozzi etal [Gliozzi
A, Bruno S, Basak TK, Rosa MD, Gambacorta A-1986] in first proposed that
transduction phase reflects the topological asymmetry of cellular organization, which
shows a relaxation jump associated with hydrophobic linkages among polar heads.
34
Tian H etal [Tian H, Habecker B, Guidry G, Gurtan A, Rios M, RofflerTarlov S, Landis SC-2000] first proposed that the sympathetic innervation of sweat
glands results from a developmental change in transmitter phenotype (from
catecholaminergic to cholinergic), making parasympathetic stimulation also possible.
Krizsan-Agbas etal [Krizsan-Agbas D, Zhang R, Marzban F, Smith-1998] showed
that noradrenergic enhancement is diminished as cholinergic neurotransmission
becomes established. They also proved that the fall of blood pressure and pulse rate
during parasympathetic stimulation (discussed later) is due to the combined effects of
adrenergic and muscarinic receptors.
T.K. Basak[Basak TK-1991] first introduced that there is entropy
transduction in photo induction. T.K. Basak [Basak TK-1992] established that there
is catecholamine interaction in blood pressure transduction. T.K.Basak etal [Basak
TK,-1993] showed the effect of sensory hormone on blood pressure transduction.
T.K. Basak etal [Basak TK,-1994] showed effect of pH in blood pressure
transduction. T.K. Basak [Basak TK,-1996] highlighted pH dependent transduction
in renal function regulation.
Macdougall IC etal [Macdougall IC, Lewis NP, SaundersbMJ-1990] showed
the cardiorespiratory effects of erythropoietin on renal anemia. Silberberg J etal
[Silberberg J, Racine M , Barre P-1990] discusses the proposed regression of Left
Ventricular Hypertrophy in dialysis patients following correction of anemia with
recombinant human erythropoietin. Wizemann V etal [Wizemann V, Kanfmann J,
Kramer W-1992] analyzed the effect of erythropoietin on ischemia tolerance in
anemic hemodialysis patients.
Bojana B. etal [Bojana B. Belesslin- Cokic, Xiaobing Yu, Babette B.
Weksler, Alan N.Schechter, and Constance Tom Noguchi-2004] first proposed that
Erythropoietin (EPO) stimulate the proliferation and angiogenesis of endothelial cells
through nitric oxide synthase. Effect of erythropoietin on
Blood pressure was
reported by Raine AEG etal.[ Raine AEG , Roger SD-1991] Nissenson AR etal
35
[Nissenson AR , Besarab A , Bolton-1997] proposed target haematocrit during
erythropoietin therapy. Association between recombinant human erythropoietin
and quality of life and exercise capacity of patients receiving hemodialysis was
studied by Canadian erythropoietin study group. [Br Med J-1990] Correction of the
anemia of end stage renal disease with recombinant human erythropoietin was first
proposed by Eschbach JW etal. [Eschbach JW. Egrie JC , Dowing MR-1987]
Eckardt K-U [Eckardt K-U-1994] first addressed oxygen dependent control of
erythropoiesis and its failure in renal diseases.
Fidler IJ [Fidler IJ-2003] proposed the pathogenesis of cancer metastasis.
Langley RR etal [Langley RR, Ramirez KM, Tsan RZ, Van Arsdall M, Nilsson MB,
Fidler IJ-2003] explained the role of tissue-specific microvascular endothelial cell
lines from H-2k b-tsA58 mice for studies of angiogenesis and metastasis. Yokoi K
etal [Yokoi K, Sasaki T, Bucana CD, Fan D, Baker CH, Kitadai Y, Kuwai T,
Abbruzzese JL, Fidler IJ-2005] discussed the role of VEGFR signaling combined
with Gemcitabine for the therapy of human pancreatic carcinoma. Kim SJ etal [Kim
SJ, Uehara H, Yazici S, He J, Langley RR, Mathew P, Fan D, Fidler IJ-2005]
discussed the therapeutic effects of STI571 and Paclitaxel against experimental bone
metastasis of human prostate cancer. In 2005 Dinel J etal [Dinel J., Hicklin Lee ,M.
Ellis-2005] discussed the role of VEGF pathway in tumor and angiogenesis. T.K.
Basak etal [T.K.Basak, K.Bhattacharya, S. Halder, S.Murugappan, V.Cyril Raj, T.
Ravi, G. Gunasekaran and P. Shaw-2009] first proposed that capacitance relaxation
associated with α dispersion it is possible to determine the signalling pathway in
tumour growth and angiogenesis.
Awada M.S. etal [Awayda M.S., Van Driessche W., Helman S.I -1999]
showed capacitance dependence of the apical membrane of frog skin. In order to find
dielectric relaxations. P. Shaw etal [Shaw P., Basak T.K., Ghosh N.C.-2006] first
established the capacitance relaxation phenomena in Cartilaginous Membrane. In
that year P. Shaw etal [Shaw P., Basak T.K. & Ghosh N.C.-2006] also proposed a
novel Method for detecting malignancies in membrane.
36
Suet Pin Liew [Liew SP-2001] established in his Ph.D. thesis for Queensland
University, Brisbane entitled, “Monitoring galvanic skin responses in functional
magnetic resonance imaging” that the Galvanic skin response (GSR) due to
cholinergic receptor follows an exponential rise followed by exponential decay.
Tarvainen M etal [Tarvainen M, Koistinen A, Valkonen-Korhonen M, Partanen J,
Karjalainen P-2001] first analyzed the principal component of galvanic skin response
which is acetylcholine (Which is a neuro hormone) hormone-regulated phenomenon.
Nitric oxide metabolism in erythropoietin-induced hypertension were studied
by Ni Z etal. [Ni Z, Wang XQ, Vaziri ND-1998] Cellular calcium and magnesium
metabolism for the treatment of hypertension and related metabolic disorders was
proposed by Resnick LM. [Resnick LM-1992]
Frey N etal [Frey N , Mckinsey , T. A-2000] first proposed decoding
calcium signals involved In cardiac growth and function. Calcium – dependent
regulator of cell division, differentiation and death was addressed by Mckinsey, T.A
etal. [Mckinsey, T. A , Zhang , C. L and Olson , E. N-2002] Role of the calcium
sensing – receptor in health and disease was reported by S I Girgis CPD.[ S I
Girgis CPD-2004]
Lack of effect on calcium homeostasis and bone turnover were studied by A
Whybro etal. [A Whybro, H Jagger, M Barker and R Eastel-1998] The calcium
regulating hormone and the calcium metabolism was studied by C.C. Capen.
[C.C.Capen-1985] Long-term effects of sevelamer hydrochloride on the calcium and
phosphate product and lipid profile of haemodialysis patients was first proposed by
Glenn M. Chertow etal. [Glenn M. Chertow, Steven K. Burke, Maureen A. Dillon,
Eduardo Slatopolsky -1999]
Truyen Nguyen etal [Truyen Nguyen, Philip J. Sherratt, H.-C. Huang, Chung
S. Yang, and Cecil B-2003] introduced increased protein stability enhances Nrf2mediated transcriptional activation of the antioxidant response element.
37
Gorr. Thomas A etal [Gorr. Thomas A., Cahn Joshua D., Yamagata Hideo
and Bunn H. Franklin-2004] first proposed hypoxia-induced synthesis of
Hemoglobin in the crustacean daphnia magna. Oxygen dependent regulation of
hypoxia-inducible factors by prolyl and asparaginyl hydroxylation were introduced
by Lando David etal. [Lando David, Gorman Jeffrey J., Whitelaw Murray L. and
Peet Daniel J-2003]. Characterization of a Hypoxia-inducible Factor (HIF-1α) from
Rainbow Trout was first reported by Soitamo Arto J etal. [Soitamo Arto J., Rabergh
Christina M.I., Gassmann Max, Sistonen Lea, and Nikinmaa Mikko -2001].The
effect of targeting platelet-derived growth factor receptor on endothelial cells were
introduced by Kim SJ etal. [Kim SJ, Uehara H, Yazici S, He J, Langley RR, Mathew
P, Fan D, Fidler IJ-2005]. Weihua Z etal [Weihua Z, Tsan R, Schroit AJ, Fidler IJ2005] first proposed initiation of endothelial cell sprouting by apoptic cells.
Neil J Schroeder etal [Neil J Schroeder and John Cunninghum-2000]
proposed that Both PTH and calcitriol regulate circulating calcium and phosphate
concentrations through their action on target organs, namely the kidney, bone, and
intestine. They also proved that PTH and calcitriol regulate one another's production,
and additionally are both regulated separately by extracellular calcium and
phosphate.
The vascular endothelial growth factor is dependent on α dispersion. It is well
established that it is one of the key regulator of the tumour growth and meta- static
dissemination for which molecular basis of tumour angiogenesis has been of keen
interest in the field of cancer research.
Quackenbush,J.[ Quackenbush,J.-2002] have described microarray data
normalization and transformation. Holloway, A.J. [Holloway, A.J., van Laar, R.K.,
Tothill, R.W. and Bowtell, D.D.-2002] have discussed options available—from start
to finish—for obtaining data from DNA microarrays II. Hartwell, L.H. [Hartwell,
L.H., Hopfield, J.J., Leibler, S. and Murray, A.W.-1999] have discribed from
molecular to modular cell biology.
38
Covert, M.W. and Palsson, B.O [Covert, M.W.-Palsson, B.O.-2002] have
proposed transcriptional regulation in constraints-based metabolic models of
Escherichia coli. J. Biol. Shen-Orr, S.S. [Shen-Orr, S.S., Milo, R., Mangan, S. and
Alon, U.-2002] have proposed network motifs in the transcriptional regulation
network of Escherichia coli. Herrgard, M.J. [Herrgard, M.J., Covert, M.W. and
Palsson, B.O.-2004] proposed reconstruction of microbial transcriptional regulatory
networks.
Krishna,S [ Krishna,S., Andersson,A.M., Semsey,S. and Sneppen,K.-2006]
analyzed structure and function of negative feedback loops at the interface of genetic
and metabolic networks. Andrews, S.C. [Andrews, S.C., Robinson, A.K. and
Rodriguez-Quinones,F.-2003] have proposed bacterial iron homeostasis. FEMS
Microbiol. Rev., 27, 215–237. Ernst, J.F [Ernst, J.F., Bennett, R.L. and Rothfield,
L.I.-1978] have described constitutive expression of the iron-enterochelin and
ferrichrome uptake systems in a mutant strain of Salmonella typhimurium.
Hantke, K. [Hantke, K.-1981] have discussed regulation of ferric iron
transport in Escherichia coli K12: isolation of a constitutive mutant. Wilderman,
P.J.[Wilderman,P.J., Sowa,N.A., FitzGerald,D.J., FitzGerald,P.C., Gottesman,S.,
Ochsner,U.A. and Vasil,M.L.-2004] have done identification of tandem duplicate
regulatory small RNAs in Pseudomonas aeruginosa involved in iron homeostasis.
Puig,S. [Puig,S., Askeland,E. and Thiele,D.J.-2005] have proposed
coordinated remodeling of cellular metabolism during iron deficiency through
targeted mRNA degradation. Masse´, E. and Arguin, M.[ Masse´,E.-Arguin,M.:
2005] have proposed ironing out the problem: new mechanisms of iron homeostasis.
Nunoshiba, T. [Nunoshiba, T., Obata, F., Boss,A.C., Oikawa,S., Mori,T.,
Kawanishi,S. and Yamamoto,K.-1999] have described role of iron and superoxide
for generation of hydroxyl radical, oxidative DNA lesions, and mutagenesis in
Escherichia coli.
39
Abdul-Tehrani,H.[Abdul-Tehrani,H., Hudson,A.J., Chang,Y.S., Timms,A.R.,
Hawkins,C., Williams,J.M., Harrison,P.M., Guest,J.R. and Andrews,S.C. -1999]
have proposed ferritin mutants of Escherichia coli are iron deficient and growth
impaired, and fur mutants are iron deficient. Djaman,O.[Djaman,O., Outten,F.W. and
Imlay,J.A.-2004] have analyzed repair of oxidized iron-sulfur clusters in Escherichia
coli. Thulasiraman,P.[Thulasiraman,P., Newton,S.M., Xu,J., Raymond,K.N., Mai,C.,
Hall,A., Montague,M.A. and Klebba,P.E.-1998] have described selectivity of ferric
enterobactin binding and cooperativity of transport in gram-negative bacteria.
Outten, F.W.[Outten,F.W., Djaman,O. and Storz,G.-2004] have proposed a
suf operon requirement for Fe–S cluster assembly during iron starvation in
Escherichia coli. Park, S. and Imlay, J.A. [Park, S. and Imlay, J.A.-2003] have
analyzed high levels of intracellular cysteine promote oxidative DNA damage by
driving the fenton reaction. Zheng, M. [Zheng, M., Doan, B., Schneider, T.D. and
Storz,G.-1999] have analyzed OxyR and SoxRS regulation of fur.
Mills, S.A. and Marletta, M.A.[Mills,S.A., Marletta,M.A.:2005] have
proposed metal binding characteristics and role of iron oxidation in the ferric uptake
regulator from Escherichia coli. Hamed, M.Y.[Hamed,M.Y.-1993] have analyzed
binding of the ferric uptake regulation repressor protein (Fur) to Mn(II), Fe(II),
Co(II), and Cu(II) ions as co-repressors: electronic absorption, equilibrium, and 57Fe
Mossbauer studies. Bagg, A. and Neilands,J.B. [ Bagg,A. , Neilands,J.B.:1987]
Ferric uptake regulation protein acts as a repressor, employing iron (II) as a cofactor
to bind the operator of an iron transport operon in Escherichia coli.
Himmetoglu Solen [Himmetoglu Solen, Dincer Yildiz, Ersoy Yeliz E,
Bayraktar Baris, Celik Varol, Akcay Tulay-2009] have proposed DNA Oxidation
and Antioxidant Status in Breast Cancer. Marika Crohns [Marika Crohns, Seppo
Saarelainen, Hannu Kankaanranta, Eeva Moilanen, Hannu Alho, and Pirkko
Kellokumpu-Lehtinen-2009] have discribed Local and systemic oxidant/antioxidant
status before and during lung cancer radiotherapy. Zuofa Z.[ Zuofa Z., Hang, Jie Jin
40
and Liangen Shi – 2008] have focused Antioxidant Activity of the Derivatives of
Polysaccharide Extracted from a Chinese Medical Herb.
Jing Zhou [Jing Zhou, Nan Hu, Ya-Lin Wu, Yuan-jiang Pan, Cuirong Sun2008] have described preliminary studies on the chemical characterization and
antioxidant properties of acidic polysaccharides from Sargassum fusiforme. Gliozzi,
A.[ Gliozzi, A., Bruno, S., Basak, T. K., Rosa, M. D. and Gambacorta-1986] have
foucused on organization and dynamics of bipolar lipids from sulfobus solfataricus in
bulk phases and in monolayer membranes.
S. Tajdoost [S. Tajdoost, T. Farboodnia and R. Heidary-2007] have explained
amiloride inhibition of Vacuolar Na+/H+ enhance salt stress in Zea mays L.
Seedlings. Priya C. Kadam and David R. Boone [Priya C. Kadam-David R. Boone:
1996] have analyzed influence of pH on Ammonia Accumulation and Toxicity in
Halophilic, Methylotrophic Methanogens. G. Dennis Sprotts [G. Dennis Sprotts,
Kathleen M. Shaw, and Ken F. Jarrellj-1985] have focused on Methanogenesis and
the K+ Transport System Are Activated by Divalent Cations in Ammonia-treated
Cells of Methanospirillurn Hungatei.
Stefano Franceschini [Stefano Franceschini, Pierpaolo Ceci, Flaminia
Alaleona, Emilia Chiancone and Andrea Ilari-2006] have proposed antioxidant Dps
protein from the thermophilic cyanobacterium Thermosynechococcus elongates An
intrinsically stable cage-like structure endowed with enhanced stability. Orna
Amster-Choder and Andrew Wright [Orna Amster-Choder-Andrew Wright,
BglG:1997] have analyzed the Response Regulator of the Escherichia coli bgl
Operon, Is Phosphorylated on a Histidine Residue.
Marialuisa Sensi[Marialuisa Sensi, Gabriella Nicolini, Marina Zanon, Chiara
Colombo, Alessandra Molla, Ilaria Bersani, Raffaella Lupetti, Giorgio Parmiani, and
Andrea Anichini1-2005] have proposed immunogenicity without Immunoselection:
A Mutant but Functional Antioxidant Enzyme Retained in a Human Metastatic
Melanoma and Targeted by CD8+ T Cells with a Memory Phenotype. T. K. Basak
[T. K. Basak, T. Ramanujam, V. Cyrilraj, G. Gunshekharan, Asha Khanna, Deepali
41
Garg, Poonam Goyal, Arpita Gupta-2009] have proposed pH Homeostasis of a
Biosensor in Renal Function Regulation Linked with UTI. Madhavan R. Budha
[Madhavan R. Budha, Kim M. Keery and Brian R. Crane-2004] have analyzed an
unusual tryptophanyl tRNA synthetase interacts with nitric oxide synthase in
Deinoccocus radiodurans.
Madhavan R. Buddha[Madhavan R. Buddha, Tao Tao, Ronald J. Parry, and
Brian R. Crane-2004] have explored regioselective Nitration of Tryptophan by a
Complex between Bacterial Nitric-oxide Synthase and Tryptophanyl tRNA
Synthetase, T. K. Basak [T. K. Basak, T. Ramanujam, J. C. Kavitha, Poonam Goyal,
Deepali Garg, Arpita Gupta, Suman Halder-2009] have focused pH Homeostasis
Linked with Capacitance Relaxation Phenomena and Electrostrictive Energy in
Cancer Cells.
Basak T. K.[ Basak T. K., Ramanujam T., Halder S., Cyrilraj V., Ravi T.,
Kulshreshtha Prachi Mohan-2008] have focused on pH homeostasis and cell
signaling pathway reflected in capacitance relaxation phenomena. Basak T. K.
[Basak T. K.-2007] have described electrical Engineering Materials. B. Djavan [B.
Djavan, M. Remzi, A. Zlotta, C. Seitz, P. Snow, and M. Marberger-Feb 2002] have
focused on novel Artificial Neural Network for Early Detection of Prostate Cancer.
D. F. Brougham[D. F. Brougham, G. Ivanova, M. Gottschalk, D. M. Collins, A. J.
Eustace, R. O'Connor, and J. Havel-July 2010] have proposed Artificial Neural
Networks for Classification in Metabolomic Studies of Whole Cells Using H Nuclear
Magnetic Resonance.
Ephram Nwoye[Ephram Nwoye, Li C. Khor, Satnam S. Dlay and Wai L.
Woo-2006] have analyzed a Novel Fast Fuzzy Neural Network Backpropagation
Algorithm for Colon Cancer Cell Image Discrimination. Gliozzi, A. [Gliozzi, A.,
Bruno, S., Basak, T. K., Rosa, M. D. and Gambacorta-1986] have foucused
Organization and dynamics of bipolar lipids from sulfobus solfataricus in bulk
phases and in monolayer membranes. Jing Zhou [Jing Zhou, Nan Hu, Ya-Lin Wu,
Yuan-jiang Pan, Cuirong Sun.-2008] have analyzed preliminary studies on the
42
chemical characterization and antioxidant properties of acidic polysaccharides from
Sargassum fusiforme.
Marika Crohns[Marika Crohns, Seppo Saarelainen, Hannu Kankaanranta,
Eeva Moilanen, Hannu Alho, and Pirkko Kellokumpu-Lehtinen-2009] have analyzed
Local and systemic oxidant/antioxidant status before and during lung cancer
radiotherapy. Marialuisa Sensi[Marialuisa Sensi, Gabriella Nicolini, Marina Zanon,
Chiara Colombo, Alessandra Molla, Ilaria Bersani, Raffaella Lupetti, Giorgio
Parmiani, and Andrea Anichini1,-2005] Immunogenicity without Immunoselection:
A Mutant but Functional Antioxidant Enzyme Retained in a Human Metastatic
Melanoma and Targeted by CD8+ T Cells with a Memory Phenotype.
R.N.G. Naguib [R.N.G. Naguib, and F.C. Hamdy-1997] have proposed
prognostic neuroclassification of prostate cancer patients. Stefano Franceschini
[Stefano Franceschini, Pierpaolo Ceci, Flaminia Alaleona, Emilia Chiancone and
Andrea Ilari-2006] have discribed antioxidant Dps protein from the thermophilic
cyanobacterium Thermosynechococcus elongates An intrinsically stable cage-like
structure endowed with enhanced stability. Shivamurthy B. [Shivamurthy B., Basak
T. K., Prabhuswamy M. S., Siddaramaiah, Tripathi Himangshu, Deopa-May 2008]
have analyzed S. S. Influence of Quartz Fillers in Dielectric Composites on
Electrostrictive.
J. Li and L. A. McLandsborough [J. Li and L. A. McLandsborough-1999]
have analyzed the effects of the surface charge and hydrophobicity of Escherichia
coli on its adhesion to beef muscle. Guzman-Casado M [Guzman-Casado M, ParodyMorreale A, Robic S, Marqusee S, Sanchez-Ruiz JM-2003] have proposed energetic
evidence for formation of a pH-dependent hydrophobic cluster in the denatured state
of Thermus thermophilus ribonuclease H.
Gliozzi, A. [Gliozzi, A., Bruno, S., Basak, T. K., Rosa, M. D. and
Gambacorta-1986] have analyzed organization and dynamics of bipolar lipids from
sulfobus solfataricus in bulk phases and in monolayer membranes, System Appl.
Orna Amster-Choder and Andrew Wright [Orna Amster-Choder and Andrew
43
Wright, BglG – 1997] have analyze the Response Regulator of the Escherichia coli
bgl Operon, Is Phosphorylated on a Histidine Residue.
F. Hamadi1 [F. Hamadi1, H. Latrache1, A. El Ghmari, M. El Louali, M.
Mabrrouki, N. Kouider -2004] have focused effect of pH and ionic strength on
hydrophobicity and electron donor and acceptor characteristics of Escherichia coli
and Staphylococcus aureus. T. K. Basak [T. K. Basak, T. Ramanujam, V. Cyrilraj, G.
Gunshekharan, Asha Khanna, Deepali Garg, Poonam Goyal, Arpita Gupta-2009]
have focuse pH Homeostasis of a Biosensor in Renal Function Regulation Linked
with UTI. Hyunjung N Kim [Hyunjung N Kim, Scotta .Bradford, and Sharonl.
Walker-2009] have proposed escherichia coli O157:H7 Transport in Saturated
Porous Media: Role of Solution Chemistry and Surface Macromolecules.
Lashley K [Lashley K-1948] have focused the Mechanism of Vision, XVIII,
Effects of Destroying the Visual ‘Associative Areas’ of the Monkey. Pribram K H
[Pribram K H 1971] have analyzed languages of the Human body. Pribram K H
[Pribram K H-1991] have analyzed human body and Perception. Bach-y-Rita P
[Bach-y-Rita P-1995] have explained nonsynaptic Diffusion Neurotransmission and
Late Human body Reorganization. Amit D J [Amit D J – 1989] have analyzed
modeling Human body Function: The World of Attractor Neural Networks. Roland
P E [Roland P E -1993] have described human body Activation. Freeman W J
[Freeman W J -1975] have focused mass Action in the Nervous System.
S. Haykin [S. Haykin-1999] have explained neural networks, comprehensive
foundation. H. Hengen [H. Hengen, S. Spoor, M. Pandit] have focused analysis of
blood & bone marrow smears. K. Lewandowski [K. Lewandowski, A. Hellmann2001] have proposed Hematology atlas. O. Lezoray [O. Lezoray, H. Cardot] have
described cooperation of color pixel classification schemes and color watershed. O.
L. Mangasarian [O. L. Mangasarian, P. Lagrangian-2001] have proposed Support
Vector Machines. P. Soile, [P. Soile-2003] Morphological image analysis, principles
and applications. V. Vapnik [V. Vapnik – 1998] have proposed Statistical Learning
Theory.
44
2.3
CONCLUDING REMARK
From the literature review it is quite clear that a lot of work is done with
respect to physiological homeostats in cancer disease under E.coli environment. But
analysis and design of dissipative physiological homeostasis in cancer diseases
linked to E.coli environment is little reported, so the study, analysis and research
work with respect to physiological homeostasis in cancer diseases in E.coli
environment is carried out here. This approach is followed in the present research
work to meet the goal of the study. The objective and the approach of the work is
more clearly elaborated in the chapters.
45
CHAPTER 3
MOTIVATION AND OBJECTIVE OF THE CURRENT
WORK
In the beginning of the thesis dissipative homeostasis in physiological system
has been discussed thoroughly and its relevance in respect of entropy transaction of
the living subject has also been mentioned. And this analysis has motivated the
research scholar to take the subject matter into the E.coli environment. It is to be
noted that the E.coli environment is linked to a complex physiological process with
different parameters, the most important of which is the pH of the E.coli
environment.
The present work has been encompassed this E.coli environment with the
dissipative homeostat for the assessment of its relevance linked with angiogenesis
and growth factor regulation in tumor.
The objective of this research work and outcome of the results is expected to
help the society in general and people concerned with cancer in particular to
understand, analyze and diagnose the tumor with the best possible approach and
facility in time to come.
46
CHAPTER 4
IRON HOMEOSTASIS OF ESCHERICHIA COLI (E.coli)
MEDIATED BY GENETIC REGULATION-A
DISSIPATIVE PHENOMENA
4.1
INTRODUCTION
Iron homeostasis in E-coli is dissipative & can initiate inhibition of diseases
like leukemia, cancer (Basak T.K.-2005, 2008, 2009, 2009, and 2010), hypoxia &
others. In this respect the iron homeostasis in E-coli environment is very crucial for
maintaining the normal status of the subjects. It is possible to build a mathematical
model that controls iron uptake and usage in the bacterium Escherichia coli to
explore the dynamic flow of iron. With the simulation of sudden decrease or increase
in the extracellular iron level on intracellular iron distribution, it is possible to focus
on the roles of the small RNA RyhB and the Fe–S cluster assembly systems in the
optimal redistribution of iron flows. In this respect Fe–S cluster assembly is crucial
to prevent the accumulation of toxic levels of free intracellular iron when the
environment suddenly becomes iron rich.
When there is sudden rise of iron level in the body of the subject, it leads to
dissipative process of iron homeostasis in E.coli environment. This dissipative
process in the iron homeostasis is controlled by two modular parts namely,
i. Iron sulphur pool.
ii. Associated iron used by non essential proteins.
In the post-genomic era the primary focus of system biology studies is to
understand the complex molecular networks coordinating cellular processes. Recent
advances in data collection and analysis [Quackenbush,J.-2002, Holloway,A.J.-2002,
Hartwell,L.H.,-1999] have allowed the construction of genome-scale databases that
47
can be utilized to reconstruct regulatory networks [Covert,M.W.-2002,Shen-Orr,S.S.2002,]. However, there are major challenges in building quantitative predictive
models of molecular networks of whole cells [Herrgard,M.J.-2004]. One of the main
challenges is to incorporate the interplay between the many important metabolites
and the transcription factors, which in turn governs other proteins that manipulate
these metabolites [Krishna,S.-2006].
Here, the iron homeostatic system of Escherichia coli as a model is used to
study the control of a large flux via a small buffer, a common process in biology,
engineering and communications.
Iron is an essential trace element for most organisms. It is a highly versatile
prosthetic component present in many key enzymes of major biological processes
[Andrews,S.C.-2003]. Iron is the second most abundant metal in the Earth’s crust,
but it is highly insoluble under aerobic conditions at neutral pH. Furthermore, cells
growing under aerobic conditions have to face the toxicity of excessive intracellular
iron levels that generate hydroxyl radicals through the Fenton reaction. For these
reasons, complex regulatory networks have evolved to keep free intracellular iron
within a narrow margin, allowing the incorporation of the metal into iron-using
enzymes and minimizing damage to the cell. In addition, iron acquisition is a crucial
limiting factor for pathogenic bacteria to colonize the host. Recent progress in
understanding regulation of iron homeostasis and quantification of several
underlying molecular interactions provides an exciting opportunity for modeling
studies.
Bacterial iron homeostasis is best understood in E.coli. When intracellular
iron level is high, the protein Fur (Ferric uptake regulator) represses transcription
initiation of iron uptake genes [Ernst,J.F.-1978, Hantke,K.-1981]. Fur also represses
a small RNA (sRNA), named RyhB, which facilitates degradation of the mRNAs
encoding for Fe-using proteins. Thus, RyhB is derepressed by Fur under low iron
condition and degrades _20 mRNAs involved in iron metabolism. This mechanism
48
shares similarities with Pseudomonas aeruginosa [Wilderman,P.J.-2004] and
Saccharomyces cerevisiae [Puig,S.-2005] [for areview see[Masse´,E.-2005]]
Using a systems approach, the analysis of iron uptake and usage in
exponentially growing E.coli cells under aerobic conditions, to build a mathematical
model to study the design architecture and dynamic behavior of the underlying
biological network. This focuses primarily on the response to changes in iron
availability. Those elements of the iron homeostatic machinery that are involved in
responses to specific conditions, e.g. redox stress, or iron storage during the
transition to stationary phase, are not included in the model.
4.2
MATHEMATICAL MODEL OF THE NETWORK
CONTROLLING IRON FLOW
The concentration of loosely bound iron (Fel) can be expressed as a
difference of iron transport and usage (Equation 1).
Fe out
dFe1 β in
=
× Ptr ×
dt
τg
Fe out + K m
−
βN
Fe1
×
τ g Fe1 + K cut
−
βR
Fe1
× mRNA ×
τg
Fe1 + K cut
−
β
β1
Fe 3
× 1sc × 2 1 2 − s × Suf x Fe1.
τg
Fe1 + K 1sc τ g
…1
The β values are used to set the size of the terms. βin (¼ 3215 mM) is
determined by the iron uptake of fur mutants [Nunoshiba,T.-1999], while bN (¼ 200
mM) is set by the iron content of iron-starved cells [Andrews,S.C.-2003, AbdulTehrani,H.-1999]. The parameter τg [¼ 25/ln2 min, [Djaman,O.-2004]] represents
dilution by cell division, which occurs on the timescale of one cell generation. The
loosely bound iron pool contains free iron and iron associated with Fur. The first
49
term represents iron influx. It is proportional to Ptr, which is a collective
representation of the iron transport machinery. Iron transport in our model follows
the
kinetics described
for
the transport
of
iron–enterobactin
complexes
[Thulasiraman,P.-1998] (Km ¼0.394 mM, Vmax ¼ 6 · 104/min/cell). For smaller
amounts of extracellular iron, Feout, the influx is proportional to extracellular iron.
However, at high extracellular iron concentration the iron transport machinery
becomes saturated and independent of Feout.
The negative terms represent irreversible fluxes into different pools. The
second term represents iron incorporation into proteins that are not regulated by
RyhB. The model assumes that the production rate of these proteins is independent
of iron concentration. In the model, iron incorporation into these proteins depends on
iron concentration only at very low levels of loosely bound iron, represented by Kcut
(¼ 0.1 mM). The third term models iron incorporation into RyhB-regulated ironusing proteins. Thus, it is proportional to the concentration of RyhB-regulated
mRNAs (mRNA). The iron-dependence of iron incorporation into these proteins is
assumed to be similar to that of the second term. The last two terms represent iron
incorporation into Fe–S clusters. Fe–S clusters are assembled by either the isc (fourth
term) or the suf (fifth term) system. The iron dependence of the Isc mediated Fe–S
cluster formation has been chosen to be linear for large Fel and becomes cubic when
Fel falls below KIsc (¼ 2 mM). The reason for using such a formula is that the isc
system does not work efficiently at low iron concentrations [Outten,F.W.-2004]. In
contrast, Fe–S cluster formation by the suf system is proportional to Fel regardless of
the size of the loosely bound iron pool. It is assumed that the concentration of
cysteine [0.1–0.2 mM, [Park,S-2003]] is not a limiting factor in Fe–S cluster
assembly. For a given bR value, bI and bS were obtained using Equation 1 to fit the
steady state condition of wild type and fur mutant cells.
The dynamics of several variables (Ptr, mRNA, Suf and Isc) in the equation
for Fel also need to be modeled. The dynamics of the transport variable are given by
the following equation:
50
dPtr
=
dt
1/ τg
1 + (FeFur / K t ) −
Ptr
τg
...2
The first term represents the production of transport machinery, which is
repressed by Fe-bound Fur (FeFur). Kt (¼ 0.55 mM) is the Fe–Fur concentration
corresponding to the half-maximal production of the transport machinery. The
second term represents the reduction of Ptr owing to dilution by cell division, which
occurs on the timescale of one cell generation. The level of FeFur depends on Fel as
well as the total amount of the Fur protein inside the cell [Fur ¼ 5 mM, [Zheng,M.1999]]. The binding of iron to Fur is assumed to happen at much shorter timescales
than transcription and translation, and to be in equilibrium. Fur has two Fe binding
sites, thus FeFur is obtained by solving the following equation:
K 2FeFur =
(Fe1 − 2FeFur )2 × (Fur − FeFur )
FeFur
...3
where KFeFur is the dissociation constant of the Fe–Fur complex. The
reported values for KFeFur range from 1.2 to 55 mM [Mills,S.A.-2005,Hamed,M.Y.1993,Bagg,A.-1987]. KFeFur ¼ 20 mM was used in this model.
The dynamics of the RyhB-regulated mRNAs encoding Fe-proteins are given
by the following:
dmRNA 1 mRNA γ
= −
− × R × mRNA.
dt
τg
τm
τg
…4
The first term represents the production of the mRNAs, which is not
regulated. In the second term τm (¼ 5/ln2 min) represents the passive degradation
and dilution of the mRNAs. The last term represents RyhB-mediated degradation (R
represents RyhB concentration). In this term g (¼ 150) is a scaling parameter for the
formation of the RyhB–mRNA complex. This unknown parameter does not influence
steady state behavior but sets timescale for reaching steady state after iron depletion.
The RyhB dynamics are given by
51
α R / τg
dR
R
γ
=
−
− × R × mRNA .
dt 1 + FeFur / K F τ R τ g
…5
The first term is the production of RyhB, which is repressed by Fe–Fur. In
this term, aR is the ratio of the unregulated levels of RyhB and RyhB-regulated
mRNAs, a parameter which influences the flux into non-essential proteins when iron
is limited. By setting aR ¼ 4, for which the RyhB-regulated iron flow drops _50%
when extracellular iron is decreased from 17 mM to 0.5 · Km.KF (¼ 0.02 mM) is the
binding constant of Fe–Fur to its operator site [Mills,S.A.-2005]. In the second term
τR (¼ 25/ln2 min) represents the passive degradation and dilution of RyhB. The third
term represents the active degradation of the RyhB–mRNA complexes, where both
RNAs are degraded by RNaseE [Masse´,E.-2003]. Using this formula it is assume
that the amount of RyhB degrading with the isc mRNA and mRNAs for Fe–S
proteins is insignificant. In Equations 2 and 4 and also later in Equation 6 we express
production rates in units of a maximum roduction rate, as the concentrations in
themselves are not important here. However the ratio of maximum production rate of
RyhB, to maximum production rate of mRNA is important, and parameterized in
term of aR in Equation 5.
Suf levels are determined by the following equation:
dSuf
1
=
dt
τg

 Suf
0. 7
−
×  0.3 +
1 + FeFur / K F  τ g

…6
This equation incorporates the observation that low level expression of the
suf operon was observed in log phase cells in the presence of high Fe–Fur, and suf
promoter activity increased _3-fold in fur mutants [Patzer,S.I.-1999]. Because 50 nM
Fe–Fur gives complete protection of the Fur binding site in the suf promoter region
in vitro, the basal promoter activity observed in vivo is likely not to be due to
incomplete binding of Fe–Fur. The repressor of the isc operon, IscR, and the proteins
of the isc system that catalyze Fe–S cluster assembly are expressed from the same
52
promoter. The isc promoter is repressed by the Fe–S–IscR complex. The
concentration of this complex (FeSIscR) is the solution to the following equation:
K FS =
(IscR − FeSIscR )× (FeS − FeSIscR )
FeSIscR
…7
The small RNA RyhB mediates the active degradation of the isc mRNA.
However, the 50 region of the isc transcript encoding IscR is not affected by RyhB.
IscR levels are governed by the following equation:
1µM × α Isc / τg
dIscR
IscR
=
−
dt
1 + FeSIscR / K1
τg
…8
The level of the other Isc proteins is given by
dIsc α Isc × mRNA Isc Isc
=
−
,
dt
τg
τg
…9
where the iscSUA mRNA level (mRNAIsc) depends on the level of the Fe–S–IscR
complex and the level of RyhB:
1 / τg
dmRNA Isc
mRNA ISC γ1
=
−
− × R × mRNA Isc
dt
1 + FeSIscR / K1
τm
τg
…10
Finally, the Fe–S level (FeS) is given by
dFeS βs
β
Fe3
β
FeS
FeS
= × Suf × Fe1 + 1 × Isc × 2 1 2 − FeS ×
−
. …11
dt
τg
τg
Fe1 + K1sc τg FeS + K FSP
τg
In this equation FeS is produced by a term identical to two of the negative
terms in Equation 1, and depleted by two terms representing incorporation of Fe–S
clusters into proteins and dilution, respectively. Although RyhB is known to regulate
some proteins containing Fe–S clusters, the model uses a constant source of Fe–S
using proteins, assuming that the proportion of RyhB regulated Fe–S proteins is
insignificant.
53
Table 4.1: Parameters of the Fe–S channel that were used in the simulations
Parameter
Binding constant of the Fe-S-IscR complex to its
operator site at the isc promoter region
Dissociation constant of the Fe-S-IscR
KI
KFS
KFSP
βFeS
γ1
αIsc
Description
Loosely bound iron concentration at which Fe-S
incorporation into proteins is half-maximal
Scaling parameter for Fe-S incorporation into proteins
Value
0.089 µM
75µM
29µM
594µM
Scaling parameter for the formation of the RyhBiscSUA mRNA complex
Parameters for Isc protein production
6.12
82
It is seen that present data are consistent with viewing the regulation of the
Fe–S channel as a sub-module of the overall iron flow regulation. A sub-module
which is essential for making Fe–S, and which in addition supply some additional
stability to the steady state behavior of the system at fixed external conditions.
Overall it is found that the model is robust to the unknown scale of self regulation of
the Fe–S pool, as well as moderately robust to the relative weights on the two steps
in the IscR self regulation. This robustness is found by analyzing model behavior
with respect to parameter choices for the Fe–S system. In particular it is found that
(i) One can fit all known data by adjusting KI from 0.089 to 0.89 mM, and
simultaneously making Fe–S–IscR binding 5 times weaker, making 10 times more
IscR and increasing the threshold KFSP from 0.29 to 2.9 mM. In effect such an
adjustment makes the Fe–S pool larger, and further stabilize the steady state of the
overall system (e.g. against reduction in RyhB production).
(ii) Also in the sub part of the Fe–S system defined by the negative feedback loop
(IscRS Fe–S–IscRSFe–S–Iscr-Operator!IscR), one may weaken complex formation
by, for example, a factor of 5 while strengthening operator binding by a factor of 1.8,
while still fitting the activity of the isc promoter for wt, fur, iscR and iscS mutants.
Also, such a weakening of Fe–S–IscR binding stabilizes the steady state against a
substantial reduction in RyhB production (aR can be lowered from 4 to 0.5).
54
The parameters of the Fe–S channel that were used in the simulations are
summarized in Table 4.1.
The E.coli iron network in this section describes those elements and
interactions of the E.coli iron homeostatic system that is used for the construction of
the mathematical model. The map of the regulatory network, illustrated with the iron
flow, is presented in Figure4.1.
When growing exponentially under aerobic conditions, an E.coli cell contains
_1.2 · 106 atoms of iron [Nunoshiba,T.-1999]. However, only _1% of these atoms
(_104) can be found in the free or loosely bound state [Keyer,K.-1996]. The size of
the available intracellular iron pool is sensed by the ferric uptake regulator (Fur)
protein. Fur is a dimeric protein present in _5000 copies in log phase cells
[Zheng,M.-1999]. Fur, when bound by iron (Fe–Fur), represses transcription of
several genes for iron uptake [Ernst,J.F.-1978,Hantke,K.-1981] by binding to a 19 bp
sequence (Fur box) in their promoter region. The affinity of Fe–Fur to the 19 bp
consensus binding site has been determined [Mills,S.A.-2005]
Fig. 4.1: Schematic model of iron flow control. Deep yellow arrowheads indicate
iron flow; green arrows indicate positive effects on iron flow. Red lines indicate
inhibition of transcription, while blue lines indicate inhibition of translation (via
mRNA degradation). Iron pools distinguished in the model are shown in deep
yellow.
55
(Kd ¼ 20 nM). Fur preferentially binds Fe2+, however, the reported binding
affinities vary [Kd of 1.2 · 10_6 to 5.5 · 10_5 M, [Mills,S.A.-2005,Hamed,M.Y.1993,Bagg,A.-1987]]. Derepression of iron transport in fur mutants results in about a
2.4-fold increase in intracellular iron concentration , and about a 7-fold increase in
the size of the loosely bound iron pool [Keyer,K.-1996]. Fe–Fur can also indirectly
activate some genes encoding iron-using proteins [Hantke,K.-2001]. A close
observation of the mechanism of Fur activation revealed that Fe–Fur represses
transcription of a small regulatory RNA, RyhB, which promotes degradation of
several mRNAs encoding for non-essential iron-using proteins [Masse´,E2005,Masse´,E.-2002]. RyhB expression increases 40-fold in cells grown in iron
depleted LB compared with normal LB [Vassinova,N.-2000]. When expressed,
RyhB binds its target mRNAs and recruits the RNA degradosome, which
simultaneously degrades both RyhB and the mRNA. Conversely, in iron-replete
conditions, active Fur represses RyhB and allows the mRNA targets to be expressed.
However, this mechanism of indirect Fur activation is not universal in
bacteria. Fe–Fur can directly activate transcription by binding close to promoter
regions of genes encoding for iron-using (in Neisseria meningitidis) or iron storage
(in Helicobacter pylori) proteins [Delany,I.-2001,2004].
In metalloproteins of E.coli, iron can be present as an isolated ion (e.g. in
SodB), or can be coordinated with a non-protein organic compound (e.g. in
hemoproteins) or a non-metallic ion (e.g. in iron–sulfur proteins). In the model it
distinguishes iron (Fe) and iron–sulfur (Fe–S) proteins. Proteins belonging to either
of these groups can be independent of or regulated by the sRNA RyhB. However, in
the model it is assume that the fraction of RyhB-regulated Fe–S proteins is
negligible.
Assembly of iron–sulfur clusters in E.coli is divided into two systems, isc and
suf [Frazzon,J.-2003]. Although these two systems have overlapping functions, they
act as distinct complexes. Single operons for both the isc and the suf systems are
derepressed under iron starvation, however, by different mechanisms. The suf operon
(encoded by sufABCDSE) is repressed by Fe–Fur. Expression of the suf operon is
56
relatively weak in log phase cells growing in LB, and is increased about 3-fold in fur
mutants. Binding of Fe–Fur to the suf promoter region has also been demonstrated in
vitro. In contrast to suf, the isc operon (encoded by iscRSUA) is regulated by a
feedback mechanism including Fe–S. Excess amount of Fe–S clusters is sensed by
the IscR repressor. Fe–S clusters form a complex with IscR, which then binds to the
isc promoter to inhibit isc transcription [Schwartz,C.J.-2001]. The isc promoter is
derepressed _17-fold in iscR mutants and _11-fold in iscS mutants, which are
defective in Fe–S synthesis. About 38-fold repression of the isc promoter activity
was observed in vitro in the presence of Fe–S cluster containing IscR. In contrast to a
microarray study that reported no effect of fur mutation on iscRSUA transcript
[McHugh,J.P.-2003], recent results demonstrated that the isc transcript is subject to
partial RyhB-mediated degradation .Indeed, RyhB seems to acts specifically on
iscSUA transcript, located downstream of iscR.
The physiological roles of the suf and isc systems are divergent. In cells
grown in LB during log phase the isc system plays the housekeeping role. Despite
the induction of the isc operon under iron starvation conditions, Fe–S biosynthesis by
the isc system is not efficient when iron availability is limited. However, the suf
system is adapted for Fe–S biosynthesis under iron starvation conditions. It can
efficiently compete for the limited free iron pool, and is probably more successful in
protecting sulfane sulfure from loss when iron is unavailable. It is that most of the
Fe–S clusters are assembled by the isc system under the conditions that are
simulated.
4.3
CONSTRUCTION OF A MATHEMATICAL MODEL OF
THE IRON NETWORK
A mathematical model is built based on a system of differential equations that
describes the dynamics of the network controlling iron flow. The model is described
in Materials and Methods. The model was designed to simulate the effect of
perturbations in the level of available extracellular iron on the iron flow in cells
during exponential growth in complete media. Therefore two major components of
iron homeostasis, redox stress response and storage, are omitted from the model.
57
Most probably iron storage is negligible under the conditions that is simulated,
because iron content of logarithmic phase cells is not affected by mutations in ftnA
and bfr, encoding iron storage proteins ferritin and bacterioferritin, respectively. In
E.coli, ferritin is responsible for iron accumulation and storage, <1% of the total
cellular iron is bound to bacterioferritin [Bauminger,E.R.-1980]. However, ftnA is
expressed at very low level in logarithmic phase cells grown in LB, and induced in
the post-exponential growth phase under iron rich conditions [Abdul-Tehrani,H.1999, Bishop,R.-1997]. Here it is used a constant 25 min generation time in the
simulations; the model was not designed to simulate changes in cellular growth rate
upon changes in iron availability.
4.4
KINETIC RESPONSES OF THE NETWORK TO RAPID
CHANGES N THE AVAILABLE EXTRACELLULAR IRON
CONCENTRATION
In order to achieve steady state intracellular iron concentration during
exponential growth, every cell must maintain a regular flow of iron into each pools
of the metal (Figure4.1). Iron influx approaches its maximum (1.2 · 106 Fe
atoms/cell generation) at _1 mM extracellular iron concentration. At this influx the
loosely bound iron pool in each cell contains _104 Fe atoms (_0.8%). In this model
there is a constant iron flow to proteins that are not regulated by RyhB (‘other
proteins’). Here it is defined the size of this pool as the iron content of iron-starved
cells [2 · 105 atoms]. Iron flows to the RyhB-regulated proteins and to Fe–S cluster
containing proteins are regulated according to iron availability. Iron flow to the
RyhB-regulated proteins is proportional to the expression of these proteins, while
Fe–S cluster assembly is proportional to both the concentration of loosely bound iron
and the level of the Isc and Suf enzymes.
By simulating the effect of rapid changes in the available extracellular iron
concentration on the iron flow in wildtype cells. In Figure(4.2) it is shown the effect
of decreasing (dissipative) the extracellular iron concentration from 17 (iron
concentration in LB) to 0.2 mM (50% of Km for iron transport) at 0 time point. The
58
performance of the simulations at different ratios of the iron flows to the RyhBregulated proteins and to the Fe–S clusters because there are no experimental
measurements available. The decreased iron influx resulted in a similar drop in the
loosely bound iron pool, regardless of the size of the RyhB-regulated iron pool
(Figure4.2). However, the proportion of the RyhB-regulated iron pool greatly affects
the allocation of the limited amount of available iron.
Fig. 4.2: Effects of perturbations in the available extracellular iron on the loosely bound iron
pool (top), on Fe–S cluster assembly (middle), and on iron flow to the RyhB-regulated Feproteins (bottom). Extracellular iron concentration was decreased from 17 to 0.2 mM at 0
time. At the 250 min time point the 17 mM iron concentration was restored. Simulations
were performed at different initial distributions of the regulated iron fluxes, indicated by the
ratio of the iron flow to the RyhB-regulated Fe-proteins at 17 mM extracellular iron (%
RyhB flow). The flux parameters for our standard simulation (red curve, see also Figure 4.3)
are the following: bin ¼ 3215 mM, bN ¼ 200 mM, bR ¼ 2530 mM, bI ¼ 58 and bS ¼ 34.
59
At lower sizes of the RyhB-regulated iron pool, Fe–S cluster assembly
(predominantly by the isc system) was maintained close to the original level, while
iron flow to the RyhBregulated Fe-proteins decreased more dramatically (Figures 4.2
and 4.3). Redirection of iron to the Fe–S channel upon iron limitation was most
efficient when initially _40% of the regulated iron flow was allocated to
RyhBregulated proteins at 17 mM extracellular iron concentration (Figure 4.3). This
maximum did not depend on the extent of the drop in the extracellular iron
concentration (data not shown). To explore how this effect is achieved, we studied
the level of isc mRNA at different iron concentrations. Transcription of the iscRSUA
operon is negatively regulated by the Fe–S–IscR complex. However, RyhB mediates
active degradation of the 30 region of the mRNA containing iscSUA. The effect of
this dual regulation on the isc transcript level is shown in Figure 4.4. The model
predicts highest expression of the Isc proteins at intermediate iron availability.
Fig. 4.3: Effect of the initial distribution of the regulated iron fluxes on the steady
state flows at decreased iron availability. Simulations were performed at different
initial distributions of the regulated iron fluxes, indicated by the ratio of the iron flow
to the RyhB-regulated Fe-proteins at 17 mM extracellular iron (RyhB outflow/Total
regulated outflow). Steady state iron flows to Fe–S cluster assembly (red) and to the
RyhB-regulated Fe-proteins (blue) at 0.2 mM extracellular iron were plotted. Values
are normalized to the corresponding flows at 17 mM extracellular iron. By using the
initial distribution of fluxes corresponding to the maximum of the red curve as a
standard condition for simulations (see also Figure 4.2).
60
Fig. 4.4: The amount of isc transcript as a function of iron concentration. RyhB and
iscR RNA levels at low iron concentration were used as a reference (¼1) to
normalize RyhB and isc RNA levels, respectively. The black curve shows the level
of iscSUA transcript which is regulated by both the Fe–S–IscR complex and the
small RNA RyhB. The normalized RyhB level is shown in red. The blue curve
represents the amount of the IscR protein.
When the level of available extracellular iron is further decreased, RyhB reduces the
amount of the iscSUA transcript which is the initiation of dissipative process.
However, the iscSUA transcript is only partially degraded by RyhB, despite the fact
that the isc system is not functional during iron starvation conditions. The predicted
level of the Isc proteins is higher at lower iron concentrations compared to when iron
is not limiting.
When the 17 mM extracellular iron concentration was rapidly restored at the
250 min. time point (Figure4.2), a sudden increase in iron influx (_2.6 · 106 Fe
atoms/cell generation) was obtained owing to the increased expression of the iron
acquisition genes at 0.2 mM extracellular iron concentration. This pulse was rapidly
directed into Fe–S clusters by the isc system, and thus the pulse in the size of the
loosely bound iron pool is damped. This effect mostly depends on the capacity of the
isc system, higher proportion of the RyhB-regulated iron pool results in higher levels
of loosely bound iron.
61
4.5
DISCUSSION
4.5.1 Response to iron perturbations
The basic principle of bacterial iron homeostasis is to maintain the
intracellular pool of free iron at a level that supplies sufficient iron for iron-using
proteins but is not toxic to the cell. To achieve this balance, iron acquisition and
consumption is regulated by a complex network. The network contains two
transcriptional regulators, Fur and IscR, responding to the concentration of free iron
and Fe–S clusters, respectively. The system gives a complex response to restricted
iron influx. The lower level of intracellular free iron results in lower Fe–Fur
concentration, enhancing the expression of the iron acquisition genes, the suf genes
and RyhB. RyhB inhibits the production of non-essential iron-using proteins to
increase the iron pool available for essential iron-using proteins [Masse´,E.-2005].
It is simulated to see how the size of the RyhB-regulated iron flow could
affect intracellular iron distribution when extracellular iron levels are perturbed.
These simulations suggest that the system’s response to iron perturbations depends
significantly on the size of the RyhB-regulated iron flow (Figures 4.2 and 4.3). There
is an ‘optimal’ distribution of intracellular iron flows, at which the system can best
fulfill the requirements of iron homeostasis. At this ‘optimal’ distribution (red curves
in Figures 4.2 and 4.3), the system can efficiently maintain iron flow to Fe–S cluster
assembly when iron influx is limited, by down regulating the synthesis of iron-using
proteins. Furthermore, the size of the loosely bound iron pool is efficiently restored
without reaching toxic levels when iron suddenly becomes available. Conversely,
when a large portion of iron flow is regulated by RyhB, Fe–S cluster assembly
becomes more sensitive to iron availability. Simulations also predict accumulation of
higher levels of loosely bound iron during the transition from low to high iron
conditions.
62
4.5.2 Regulation of Fe–S cluster assembly
At the ‘optimal’ distribution of iron flows discussed above, the level of the
Isc proteins can be efficiently adjusted by the combined action of RyhB and IscR, to
meet the cell’s needs at different availabilities of iron. Reduced iron influx results in
lower levels of loosely bound iron, therefore reduced Fe–S cluster assembly. Because
the level of Fe–S-IscR is also reduced, the expression of the Isc proteins is increased
to restore the optimal rate of Fe–S cluster assembly (Figures 4.3 and 4.4). In iron
starvation conditions, the isc operon is fully derepressed; however, the Fe–S cluster
assembly by the isc system becomes inefficient. As the level of loosely bound iron
decreases, RyhB becomes derepressed, and mediates partial degradation or
dissipation of the 30 region of the isc transcript, encoding IscS, IscU and IscA. The
predicted level of Isc proteins is _3-fold higher in iron starvation conditions
compared with when iron is not limiting. This dynamic simulations pinpoint the
advantage of the elevated level of the Isc proteins in iron-restricted cells. In these
cells the expression of the iron acquisition genes is derepressed, allowing iron influx
at high capacity when the environment suddenly becomes iron-rich. It is suggested
that during this transition period the isc system assembles the incoming iron into Fe–
S clusters, thus preventing the intracellular free iron level from reaching toxic levels.
It is also suggested that the advantage in IscR not being regulated by RyhB is that the
higher level of IscR allows faster shut-down of isc transcription at the transition from
low to high iron availability. After this transition period, the reduced level of the Isc
proteins allows the redirection of iron flow to the newly synthesized RyhB-regulated
iron-using proteins.
4.5.3 Role of small RNA regulation
The advantages of small RNA regulation over transcriptional regulation have
been described recently [Masse´, E.-2003,2005]. One possible advantage of small
RNA regulation in iron flow control is the suboperonic discoordination of protein
expression by mediating selective degradation of parts of a polycistronic mRNA
[Adhya,S.-2003]. This control mechanism may allow selective inhibition of synthesis
of iron-using proteins produced from operons also encoding proteins that do not
require iron for their function.
63
The example of the isc operon shows that transcriptional and small RNA
regulation can be combined in an efficient way to adjust protein levels according to
the levels of different metabolites. This system requires less complexity at the
promoter region compared to an analogous arrangement based on the combination of
transcriptional repression and activation [Semsey,S.-2006].
4.5.4 Conclusion
Here it is analyzed that the iron regulation network as a case study of how a
large flux can be regulated through a small buffer. In exponentially growing cells the
network partitions the incoming iron flux into three parts, an essential part, a Fe–S
pool, and a part associated to Fe usage by non-essential proteins. Although the usage
governed through the latter two modules appears largely interconnected (Figure4.1),
the modeling shows that the relative strengths of the interactions indeed allow us to
understand iron regulation in terms of a dissipative modular structure with two
competing sub-systems. In effect, the organization of the network is hierarchical,
with free Fe guiding two regulatory modules, and with all feedback going through
changes in Fe due to irreversible absorption of Fe in either of the two pathways. It is
further seen that the Fe–S pathway responds very fast to changes in iron availability,
a feature which effectively protects the cell against Fe poisoning under sudden iron
bursts. On the other hand, the fact that Fe–S absorption is also shut down under iron
depletion puts strain on the non-essential usage. To avoid using Fe in non-essential
proteins, the cell needs to stop production of non-essential proteins as fast as
possible. This may be the reason for using small RNA-mediated post transcriptional
regulation, which indeed short-circuits the genetic regulation upon sudden iron
dissipation, by removing all mRNA for non-essential iron-using proteins. However,
small RNA regulation in iron homeostasis is not universal in bacteria. The model
provides a framework to study the possible advantages of dissipative, transcriptional
and small RNA regulated post-transcriptional control of non-essential iron-using
proteins.
64
CHAPTER 5
A DISSIPATIVE MODEL IN THE E.COLI
ENVIRONMENT RELATED TO ELECTROSTRICTIVE
ENERGY IN CANCER CELL
5.1
INTRODUCTION
This chapter has focused on a new concept in respect of the status of
oxidant/antioxidant in cancer cell following radiation therapy. And in this respect a
model has been developed linked with an environment of E.Coli in which TrpRS II is
induced after radiation damage. It is interesting to note that Electrostrictive energy is
the input to the model the output of which is the oxidant/antioxidant ratio. This ratio
is related to the status of Electrostrictive energy derived from capacitance relaxation
phenomenon (US patent No.TK Basak 5691178, 1997) which happens to be
decaying or dissipative nature in cancer cell. The oxidant/antioxidant ratio is linked
to Electrostrictive energy with increasing pH. In this chapter it is discussed about the
status of phosphorylation and dephosphorylation after radiation therapy linked to
E.Coli environment against the pH gradient is indicative for the treatment of cancer.
Oxidant/antioxidant balance is an important factor related to initiation and
progression of cancer. Clinical research shows that more the oxidant/antioxidant ratio
more is the metastasis [Himmetoglu Solen-2009]. High urate at baseline may
compensate against the oxidative stress caused by Ling Cancer. Radiation Therapy
shifts the oxidant/ antioxidant balance towards lipid peroxidation (which may lead to
more metastasis). However, the antioxidant defense mechanism of the body appears
to counteract the increased oxidative stress rather effectively [Marika Crohns-2009].
The antioxidant activity of the polysaccharide from Ramulus mori and its
derivatives (sulfated, phosphorylated, acetylated and benzoylated polysaccharides)
were determined, including scavenging activity against superoxide, hydroxyl and
1,1-diphenyl-2-picrylhydrazyl radicals. Although oxygen ion is a relatively weak
65
oxidant, it decomposes to form stronger reactive oxidative species, such as singlet
oxygen and HO-, which initiates Lipid peroxidation. Oxygen ion indirectly initiates
lipid peroxidation as a result of hydrogen peroxide formation. Thus, polysaccharides
initiate which, in turn, increases oxidant/antioxidant ratio [Zuofa Z.-2008].
It has been investigated that antioxidant activity occurs at higher values of pH
[Jing Zhou-2008]. Referring to Fig. 5.1, we can say that as pH becomes more basic
(increasing pH), less will be the oxidant/antioxidant ratio. It can be noted that the
environment of survival of E. Coli over a wide pH range has been analyzed in
relation to proliferation and inhibition of metastasis with cyclic genetic reform.
Fig.5.1 Status of Oxidant/antioxidant of E. Coli Trps II with Respect to the pH of its
Environment.
During radiation therapy for suppression of metastasis, the antioxidant
oscillation will grow up. But for a certain period of time after that, antioxidant
oscillation will decay or dissipate and this process continues with the application of
successive phases of radiotherapy[Marika Crohns-2009].
It is observed very clearly in fig5.1, showing the status of oxidant/antioxidant
of E.coli Trps II with respect to the pH of its environment, which happens to be of
dissipative(inhibition) behavior with the increase in pH almost around 6.4 of value.
66
E. Coli related archaeabacteria in lipid peroxidation is influenced by the asymmetry
of the lipid and the more the lipid peroxidation less is the relaxation time [Gliozzi,
A.-1986].
Asymmetry is linked with pH gradient mediated by lipid peroxidation.
Relaxation of polar head is related archaeabacteria. Na+/H+ antiporters cause
enhancement of lipid peroxidation [S.Tajdoost-2007, Priya C. Kadam-1996].
Antiporters maintain alkaline environment where as lipid peroxidation initiated by
antiporters maintains acidic pH homeostasis of the fluid of the E. Coli
archaeabacteria. Antiporters initiate lipid peroxidation which sustains pH gradient in
the environment of archaeabacteria [G. Dennis SprottS-1985]. Thus the asymmetry
of polar head of archeobacteria (E.Coli) is sustained in its pH environment mediated
by the antiporters linked with the electrochemical gradient across its membrane [S.
Tajdoost-2007, Priya C. Kadam-1996, G. Dennis SprottS-1985].
DNA-binding
proteins
from
starved
cells
(Dps
proteins)
protect
archaeabacteria primarily from oxidative damage. They are composed of 12 identical
subunits assembled with 23-symmetry to form a compact cage-like structure known
to be stable at temperatures greater than 700•C and over a wide pH range.
Thermosynechococcus elongatus Dps thermostability is increased dramatically
relative to mesophilic Dps proteins. Hydrophobic interactions at the dimeric and
trimeric interfaces called Dps-like are replaced by salt bridges and hydrogen bonds, a
common strategy in thermophiles [Stefano Franceschini-2006].
During the reduction of CO2 to CH4, methanogenic bacteria appear to
maintain their cytoplasmic pH slightly acidic (pH 6.6-6.8) and to generate an
electrical potential (inside negative) of -120 to -200 mV. Growth is usually
conducted in media buffered near pH value of 6.8 where the membrane potential is
the predominant, or sole, component of the pmf explained.’ An ATP pool is
maintained in the range of 1 to 23 nmol/mg, protein being especially large in Msp.
hungatei. These factors may be relevant to the bioenergetics of K+ movement, since
67
activity of the low affinity, constitutive systems of Escherichia coli and
Streptococcus faecalis requires both ATP and the pmf[Priya C. Kadam-1996, G.
Dennis SprottS-1985, Stefano Franceschini-2006 ].
The b-glucoside utilization (bgl) genes of Escherichia coli are positively
regulated by the product of the bglG, which functions as an anti terminator by
binding to specific sequences present within the bgl mRNA. BglG is inactivated by
phosphorylation in the absence of b-glucosides by BglF. Here, we present evidence
for an additional function for BglG, namely the stabilization of the 5¢ end of the bgl
mRNA.
The bgl operon in Escherichia coli, induced by an environmental signal (bglucoside), is regulated by a sensory system which consists of a membrane-bound
sensor, BglF, and a cytoplasmic response regulator, BglG which is a transcriptional
antiterminator binds to the bgl RNA transcript to prevent the formation of
transcriptional terminators.
The sensor BglF controls the activity of the response regulator BglG by
phosphorylation and dephosphorylation, depending on b-glucoside availability.
Reversible phosphorylation of BglG by BglF was shown to regulate its activity by
controlling its dimeric state. Thus, BglG exists in the cell in two forms: an inactive,
monomeric phosphorylated form and an active, dimeric nonphosphorylated form.
Different organisms were reported to resemble the bgl system. Based on this
similarity, the mechanism by which these systems induce the expression of particular
carbohydrate-catabolic
operons
was
suggested
to
involve
PTS-mediated
phosphorylation of transcriptional antiterminator proteins. To understand the rules of
recognition and interaction between sensors and regulators of this new family, it is
important to define the functional domains involved in transduction of a signal by the
components which constitute systems of this family. It is to be noted that BglG, is the
response regulator of the two-component systems which are phosphorylated on an
aspartate [Orna Amster-Choder-1997].
68
Oxidative stress causes damage of DNA up to pH from 3 to 4.5 and the effect of
antioxidant reduces the oxidative stress beyond pH 4.5. This particular phenomenon
has been correlated with the pH for the status of oxidant to antioxidant ratio in cancer
cell. Oxidant/antioxidant ratio in E.Coli with lower pH is linked to metastasis
[Himmetoglu Solen-2009, Marika Crohns-2009, Marialuisa Sensi-2005, T. K. Basak2009].
Identification of a number of unique tumor antigens in long-term survivors,
has suggested that this class of antigens is immunogenic and relevant to tumor
rejection. On the other hand, some of the mutant proteins that generate unique tumor
antigens can be critical for tumor cell survival, due to their involvement in basic
metabolic pathways, or in regulation of cell cycle and apoptosis. In these instances,
immunogenicity and function of the mutant protein will likely act as opposing
selective forces for the maintenance of expression of the antigen along tumor
progression. In fact, immunogenicity may promote the T cell–mediated response
leading to emergence of antigen loss variants, where the mutant protein is no longer
expressed. However, neoplastic cells that will survive in the host during tumor
progression will attempt to maintain expression of the mutant protein due to its
relevant cellular function [Marialuisa Sensi-2005]. Associated oxidative stress causes
DNA damage with increase oxidant/antioxidant ratio in E.Coli for pH range 3-4.5.
As DNA damage is correlated with increased oxidant/antioxidant ratio in E. Coli for
tumor
progression.
Similarly
DNA
recovery
is
correlated
to
decreased
oxidant/antioxidant ratio in E. Coli when the effect of antioxidant reduces the
oxidative stress beyond pH 4.5. [Himmetoglu Solen-2009, Marika Crohns-2009,
Marialuisa Sensi-2005, T. K. Basak-2009].
It is to be noted that unusual tryptophanyl tRNA synthasase (TrpRS)interacts
with nitric oxide in D. radioduranspresent in the genes for TrpRS II identified as the
NOS- interfacting protein in alkaline environment. TrpRS II is induced after
radiation damage and contains an N-terminal extension similar to those of proteins
involved in stress responses. Recombinantly expressed TrpRS II binds tryptophan
(Trp), ATP, and D. radiodurans tRNATrp and catalyzes the formation of 5′ adenyl-
69
Trp and tRNATrp.Upon coexpression in Escherichia coli, TrpRS II dramatically
enhances the solubility of deiNOS in alkaline environment. It is interesting to note
that the ability of TrpRS II and ATP to enhance the deiNOS nitration reaction
underscores the functional coupling of these two enzymes in conformity with Lipid
peroxidation in E.Coli [Madhavan R.Budha-2004, Madhavan R. Buddha-2004].
5.2
MODELLING AND SIMULATION
Fig.5.2 shows a generalized model of metastasis and Apoptosis in cancer. As
we are interested in the status of oxidant/antioxidant ratio with respect
electrostrictive energy, the input to the model is electrostrictive energy [T. K. Basak2009, 2008, 2007, Shivamurthy B- 2008] and output of the model is
oxidant/antioxidant ratio. The homeostat and transduction phase [T.K. Basak-2005]
are linked with lipid peroxidation mediated by antiporters in E. Coli archaeabacteria.
The incremental input electrostrictive energy is applied to the model on the basis of
pH homeostasis linked with capacitance relaxation phenomenon.
Fig.5.2 Generalized model of metastasis at apostosis.
Fig. 5.3 shows the 0.15 p.u. change in electrostrictive energy with the
oxidant/antioxidant status. The Yaxis peak represents the ratio and we have
considered only the first two peaks for calculation related to damping. For 0.15 PU
change in Electrostrictive energy the oxidant/antioxidant ratio is (0.1331/0.114)
1.1675 corresponding to first two peaks and settling/relaxation time is 70ms at
pH=7.5.
70
For 0.12 p.u. change in electrostrictive energy the oxidant/antioxidant
response is plotted in Fig. 4.
Fig. 5.4 shows that for 0.12 p.u. change in electrostrictive energy, the
oxidant/antioxidant ratio is (0.1065/0.0911) 1.169 and settling/relaxation time is 71
ms at pH =6.
For 0.06 p.u. change in electrostrictive energy the oxidant/antioxidant
response is plotted in Fig.5. 5.
Fig.5.3 Oxidant/antioxidant response for .15PU changes in Electrostrictive energy
(ratio peak above 0.12).
Fig.5.4 Oxidant/antioxidant response for .15PU changes in Electrostrictive energy
(ratio peak below 0.12).
71
For 0.06 p.u. change in electrostrictive energy, the oxidant/antioxidant ratio is
(0.0532/0.0455) 1.16932 and settling/relaxation time is 72 ms at pH=4.5.
For 0.04 p.u. change in electrostrictive energy the oxidant/antioxidant
response is plotted in Fig.5. 6.
For 0.04 p.u. change in electrostrictive energy the oxidant/antioxidant ratio is
(0.0355/0.0303) 1.1716 and settling/relaxation time is 73 ms at pH=3.
Fig.5.5 Oxidant/antioxidant response for .15PU changes in Electrostrictive energy.
(ratio peak above 0.05)
Fig.5.6 Oxidant/antioxidant response for .15PU changes in Electrostrictive energy
(ratio peak around 0.035).
72
From the figures Fig. 5.3 to Fig.5.6, we observe that as electrostrictive energy
increases, the ratio oxidant/antioxidant decreases and lipid peroxidation decreases.
As oxidant/antioxidant ratio decreases metastasis decreases. It can also be seen that
with increasing pH metastasis decays.
When electrostrictive energy decreases, ratio of oxidant/antioxidant increases,
lipid peroxidation increases, relaxatation time of lipid of E Coli increases and pH
decreases leading to metastasis.
Fig.5.7 shows graph of Oxidant/antioxidant ratio Vs pH from information obtained
from Fig.5.3 to Fig.5.6.
Fig 5.7 Oxidant/Antioxidant ratio Vs pH
From Fig. 5.7, it is clear that ratio of oxidant/antioxidant decreases with
increment of pH. Thus we can conclude that oxidant/antioxidant ratio increases with
decrease in pH value. With low acidic pH (pH=3.5), the relaxation time increases
which initiates metastasis. Again metastasis can be retrarted with the increase of pH
(pH=7.5) which causes decrement of oxidant/antioxidant ratio associated with
decreased relaxation time. With more metastasis oxidant/antioxidant ratio will
increase. With lower metastastasis oxidant/antioxidant ratio will decrease.
73
Fig.5.8 represents mean value of oxidant/antioxidant response corresponding
to Fig.5.3-5.6 Vs pH representing dephosphorylation with almost a constant slope.
Fig.5.8 Mean Value of Oxidant/Antioxidant response Vs pH
5.3
CONCLUSIONS
This chapter monitors the dissipative status of Oxidant/antioxidant ratio for
incremental change in Electrostrictive energy linked to lipid peroxidation of cancer
cell in the environment of E.Coli. From the oxidant/antioxidant response it is
possible to focus on how TrpRS II is induced after radiation damage since it contains
an N-terminal extension similar to those of protein involved in stress response. The
Matlab Simulation shows the damped response after radiation therapy in cancer cell
with the relevant relaxation time for asymmetrical bipolar lipid present in E.Coli . It
is interesting to note that with increasing pH linked to electrostrictive energy
oxidant/antioxidant ratio decreases. The result about the status of phosphorylation
and dephosphorylation with and after radiation therapy linked to E.Coli environment
against the pH gradient can be an indicative for the treatment of cancer.
74
CHAPTER 6
ANALYSIS OF E-COLI ENVIRONMENT RELATED TO
CANCER WITH ANN
6.1
INTRODUCTION
6.1.1
Artificial Neural Network
An artificial neural network (ANN), usually called "neural network" (NN), is
a mathematical model or computational model that tries to simulate the structure
and/or functional aspects of biological neural networks. It consists of an
interconnected group of artificial neurons and processes information using a
connectionist approach to computation. In most cases an ANN is an adaptive system
that changes its structure based on external or internal information that flows through
the network during the learning phase. They are usually used to model complex
relationships between inputs and outputs or to find patterns in data.
In a neural network model simple nodes, which can be called variously
"neurons", "neurodes", "Processing Elements" (PE) or "units", are connected
together to form a network of nodes — hence the term "neural network". Practical
use of neural network comes with algorithms designed to alter the strength (weights)
of the connections in the network to produce a desired signal flow.
6.1.2 Network function
The word network in the term 'artificial neural network' refers to the inter–
connections between the neurons in the different layers of each system. The most
basic system has three layers. The first layer has input neurons which send data via
synapses to the second layer of neurons and then via more synapses to the third layer
of output neurons. More complex systems will have more layers of neurons with
some having increased layers of input neurons and output neurons. The synapses
75
store parameters called "weights" which are used to manipulate the data in the
calculations.
The layers network through the mathematics of the system algorithms. The
network function f(x) is defined as a composition of other functions gi(x), which can
further be defined as a composition of other functions. This can be conveniently
represented as a network structure, with arrows depicting the dependencies between
variables. A widely used type of composition is the nonlinear weighted sum, where
f(x)=K(Σ wi gi (x)) , where K (commonly referred to as the activation function) is
some predefined function. It will be convenient for the following to refer to a
collection of functions gi as simply a vector .
6.1.3 ANN dependency graph
The figure 6.1 below depicts a decomposition of f, with dependencies
between variables indicated by arrows.
Fig 6.1 ANN Dependency Graph
These can be interpreted in two ways. The first view is the functional view:
the input x is transformed into a 3-dimensional vector h, which is then transformed
into a 2-dimensional vector g, which is finally transformed into f. This view is most
commonly encountered in the context of optimization.
The second view is the probabilistic view: the random variable F = f(G)
depends upon the random variable G = g(H), which depends upon H = h(X), which
76
depends upon the random variable X. This view is most commonly encountered in
the context of graphical models. The two views are largely equivalent. In either case,
for this particular network architecture, the components of individual layers are
independent of each other (e.g., the components of g are independent of each other
given their input h). This naturally enables a degree of parallelism in the
implementation. Such networks are commonly called feedforward, because their
graph is a directed acyclic graph.
6.1.4 Multilayered Network Architecture
Layering drastically increases the computational power of the system. This is
because a multilayered linear neuron network is equivalent to a single layer linear
neuron network. Computational capability of the neural network can be increased by
introducing a smooth non-linearity in the form of the sigmoidal signal function and
through appropriate layering of fields of such neurons. A multilayered neural
network architecture is potrayed in figure 6.2
Fig 6.2 Generic architectre of a feedforward neural network
77
The input layer has n linear neurons that receive real valued external inputs in
the form of an n-dimensional vector in Rn .This layer also includes an additional bias
neuron that receives no external input but generates a signal +1 that feeds all bias
connections of the neurons of the hidden layer. Similarly, the hidden layer has q
sigmoidal neurons that receives signals from the input layer. A bias neuron has been
additionally included in the hidden layer to generate a +1 signal for bias connections
of the output layer neurons. The output layer comprises of p sigmoidal neurons.
Neuron layers compute in a strictly feedforward fashion-signals from one layer of
neurons act as inputs to the next layer, and so on. Finally, the network signals that
emanate from the last layer of neurons comprise a p-dimensional vector of real
numbers. The neural network thus maps a point in Rn (the input space) to a point in
Rp (the output space).
To learn such a mapping, the network is provided with a training set of
discrete data samples that comprise input-output vector pairs that describe an
unknown function.
6.1.5 Back Propagation Learning Algorithm
Back propagation, or propagation of error, is a common method of teaching
artificial neural networks how to perform a given task. Back propagation learning
algorithm can be divided into two phases:
1. Propagation
2. Weight update.
Each propagation has two steps. First step involves forward propagation of a
training pattern's input through the neural network in order to generate the
propagation's output activations. And second step involves Back propagation of the
propagation's output activations through the neural network using the training
pattern's target in order to generate the deltas of all output and hidden neurons. For
each weight-synapse it is required to multiply its output delta and input activation to
get the gradient of the weight. And then bring the weight in the opposite direction of
the gradient by subtracting a ratio of it from the weight. This ratio influences the
78
speed and quality of learning; it is called the learning rate. The sign of the gradient of
a weight indicates where the error is increasing; this is why the weight must be
updated in the opposite direction. The two phases are repeated until the performance
of the network is good enough.
As the algorithm's name implies, the errors (and therefore the learning)
propagate backwards from the output nodes to the inner nodes. So technically
speaking, back propagation is used to calculate the gradient of the error of the
network with respect to the network's modifiable weights. Back propagation is the
most popular neural network algorithm. So much so that in many cases it is almost
blindly applied to a problem without much regards (or respect) for its limitations.
The back propagation algorithm is popular for its simplicity of implementation and
its ability to quickly generate networks that have the capability to generalize.
In this thesis we have attempted to correlate the oxidant/antioxidant ratio with
the pH of the blood through control system models which depict the dynamic
processes. The control system model is better than technology like PNN and Fuzzy
Logic. The output of the control system model is a simulated curve that relates the
cancer status to the pH of the blood. From this simulated output we will be able to
classify the stages of cancer using ANN. This helps the physicians to diagnose the
stage of the cancer patient and in the further treatment of the disease.
6.2
ANN AS A CLASSIFIER
Artificial Neural Network is an adaptive system that has been used for
training and classification. The network changes its structure based on external or
internal information that flows through it during the learning phase. It processes
information using a connectionistic approach. There are three layers namely input,
hidden and output layer. The weights of neurons of input layer are forwarded to the
hidden layer and from hidden layer weights are propagated to to output layer.
Initially random weight values are assigned. The weight values are updated using
Back propagation algorithm. The algorithm works until the error comes out to be less
than the given tolerance. Since the weights are propagated backward from output
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layer to input layer for update procedure, hence the name backpropagation is given to
algorithm. This algorithm is used for training and classification.
Artificial neural networks have been used earlier for the diagnosis of the
prostate cancer. For such diagnosis, various types of data are used, such as prostate
specific antigen levels [B. Djavan-2002], clinical and biochemical criteria [R.N.G.
Naguib-1997] ultrasonic echo signals with prostate specific antigen levels [W.
Gnadt-1999]. Neural networks have been utilized in this kind of biomedical
applications because of their ability to perform more accurately than other
classification techniques. Basic advantages of the neural network method over
traditional classifiers are; easy adaptation to different types of data and use of its
complex configuration to find the best nonlinear function between the input and the
output data.
A novel fast fuzzy back propagation algorithm has also been proposed earlier
for classification of colon cell images. The experimental results of the algorithm
proved that the accuracy of the method was very high. The algorithm was evaluated
using 116 cancer suspects and 88 normal colon cells images and results in a
classification rate was quite high.[Ephram Nwoye-2006]
Artificial neural networks have also been used for classification of H 1
Nuclear Magnetic Resonance spectroscopic data, recorded on whole-cell culture
samples of four different lung carcinoma cell lines, which display different drug
resistance patterns. The robustness of the approach was demonstrated by its ability to
classify the cell line correctly in 100% of cases, despite the demonstrated presence of
operator-induced sources of variation, and irrespective of which spectra were used
for training and for validation. ANN has proved to be quite potential for lung
carcinoma classification in realistic situations [D.F. Brougham-2010].
6.2.1 ANN Based Classifier
In this chapter existing system and proposed model have been discussed.
Since existing system has various drawbacks, new automated system has been
designed. Back propagation algorithm in Artificial Neural Network has been used for
training and classification.
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6.2.2 Existing System for Diagnosis of Cancer Stages
The Existing system is a manual process which consumes weeks or months to
test the cancer patient from the clinical test data results that is being performed on
them. The block diagram of the existing system is given in the figure 6.3
Fig 6.3 Block Diagram of Existing System
6.2.3 Drawbacks of the Existing System:
1. Time consuming
2. Uncertainty in Decision Making
3. Possibility of human errors
4. Generates Bewildering amount of data
6.3
PROPOSED ANN MODEL
For the diagnosis of different diseases like cancer and also to identify that the
patient belongs to which stage it is required to perform many clinical tests. The
existing method of identifying the stage of the cancer patient is time consuming, and
it involves possibility of human error. Thus a new modern method has been
introduced to identify the stage of the cancer patient using artificial neural network.
ANN training has been done with Back propagation algorithm (also called conjugate
gradient algorithm) in our proposed model. The data is obtained from the MATLAB
simulation by correlating the capacitance relaxation phenomena, Vascular
Endothelial Growth Factor and Estrogen Receptors ER- alpha, ER- beta against pH.
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This data set is being subjected to training and validation in ANN analysis.
Classification of Cancer cells into different stages has been done by the proposed
ANN model which provides more accurate results as compared to the classification
done using DTREG software.
6.3.1 System Architecture
The proposed system is an automated system which computes and takes
decision automatically. The main objectives of the proposed system are:
Produce reliable performance estimates.
Allow to apply models to unseen patients.
The block diagram of the proposed system is shown in the fig6.4
Data Set Preparation
Diagnosis
(Training, validation and
classification using ANN)
Fig 6.4 Block diagram of proposed system
6.3.2 Data set Preparation
Data set has been generated from the output of Matlab simulation correlated
with published journals. After that, software DAGRA has been used to generate huge
data set and this data set was selectively processed in training and classification of
cancer stages. After the data set has been prepared, it is being subjected to the
process of diagnosis which includes training, validation and classification using
artificial neural network.
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6.3.3 Diagnosis
Diagnosis of Cancer Stages has been done using ANN architecture as
provided by DTREG software. Since, the classifier didn’t give accurate results, ANN
architecture that uses back propagation algorithm has been implemented in
MATLAB environment. It removes various constraints of DTREG software. DTREG
allows us to have only 1 or two hidden layers whereas proposed architecture can
have any no. of hidden layers. The no. of neurons in hidden layer in DTREG is also
limited but ANN architecture in MATLAB gives us the flexibility to give any no. of
neurons in hidden layer. The set of in-built functions are fixed in DTREG as a result
of which misclassification is more. Proposed architecture can use any function and so
it gives more accurate results.
6.4
IMPLEMENTATION OF ANN BASED CLASSIFIER
The objective of this thesis is to find an efficient algorithm that would assist
in classifying the cancer into different stages with relatively good accuracy.
We have implemented the model with 2 approaches namely:
1. ANN implementation in DTREG
2. ANN implementation in MATLAB
Both the approaches give different misclassification. Backpropagation
algorithm has been used for training and classification of the data. It has been found
that MATLAB gines much better result than the ready-made tool of DTREG
software. Both the approaches have been compared and results hence results have
been interpreted.
Simulation is performed using the MATLAB 7.0 simulation. Various factors
like oxidant/antioxidant ratio, metastasis, capacitance relaxation phenomena, ERalpha, ER- beta against pH are simulated using differential equation models. The
graphical outputs obtained from the simulink model are considered as the data set.
This data set was combined with the data obtained from experimental findings to
generate a larger data set.
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6.4.1 Implementation in DTREG
The fig 6.5 illustrates a perceptron network with three layers:
Fig 6.5 Multilayer perceptron network
This network has an input layer (on the left) with three neurons, one hidden
layer (in the middle) with three neurons and an output layer (on the right) with three
neurons.
There is one neuron in the input layer for each predictor variable (x1…xp). In
the case of categorical variables, N-1 neurons are used to represent the N categories
of the variable.
6.4.1.1 Input Layer
A vector of predictor variable values (x1…xp) is presented to the input layer.
The input layer distributes the values to each of the neurons in the hidden layer. In
addition to the predictor variables, there is a constant input of 1.0, called the bias that
is fed to each of the hidden layers; the bias is multiplied by a weight and added to the
sum going into the neuron.
6.4.1.2 Hidden Layer
Arriving at a neuron in the hidden layer, the value from each input neuron is
multiplied by a weight (wji), and the resulting weighted values are added together
producing a combined value uj. The weighted sum (uj) is fed into a transfer function,
σ, which outputs a value hj. The outputs from the hidden layer are distributed to the
output layer.
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6.4.1.3 Output Layer
Arriving at a neuron in the output layer, the value from each hidden layer
neuron is multiplied by a weight (wkj), and the resulting weighted values are added
together producing a combined value vj. The weighted sum (vj) is fed into a transfer
function, σ, which outputs a value yk. The y values are the outputs of the network.
If a regression analysis is being performed with a continuous target variable,
then there is a single neuron in the output layer, and it generates a single y value. For
classification problems with categorical target variables, there are N neurons in the
output layer producing N values, one for each of the N categories of the target
variable.
6.4.1.4 Multilayer Perceptron Architecture
The network diagram shown above is a full-connected, three layer, feed
forward, perceptron neural network. Fully connected means that the output from
each input and hidden neuron is distributed to all of the neurons in the following
layer. Feed forward means that the values only move from input to hidden to output
layers, no values are fed back to earlier layers. All neural networks have an input
layer and an output layer, but the number of hidden layers may vary.
When there is more than one hidden layer, the output from one hidden layer
is fed into the next hidden layer and separate weights are applied to the sum going
into each layer.
6.4.1.5 Training Multilayer Perceptron Networks
The goal of the training process is to find the set of weight values that will
cause the output from the neural network to match the actual target values as closely
as possible. There are several issues involved in designing and training a multilayer
perceptron network:
Selecting how many hidden layers to use in the network.
Deciding how many neurons to use in each hidden layer.
85
Finding a globally optimal solution that avoids local minima.
Converging to an optimal solution in a reasonable period of time.
Validating the neural network to test for over fitting.
6.5
SELECTING THE NUMBER OF HIDDEN LAYERS
DTREG can build models with one or two hidden layers.
6.5.1 Deciding how many neurons to use in the hidden layers
One of the most important characteristics of a multilayer perceptron network
is the number of neurons in the hidden layer(s). If an inadequate number of neurons
are used, the network will be unable to model complex data, and the resulting fit will
be poor. If too many neurons are used, the training time may become excessively
long and worse, the network may over fit the data. When over fitting occurs, the
model fits the training data extremely well, but it generalizes poorly to new, unseen
data.
DTREG includes an automated feature to find the optimal number of neurons
in the hidden layer. We need to specify the minimum and maximum number of
neurons we want it to test, and it will build models using varying numbers of
neurons. This is a highly effective method for finding the optimal number of neurons,
but it is computationally expensive, because many models are built, and each model
has to be validated. If we select a model with two hidden layers, we must manually
specify the number of neurons in the second hidden layer.
6.5.2 Finding a globally optimal solution
DTREG uses an algorithm to select the initial range of starting weight values.
It then uses the conjugate gradient algorithm to optimize the weights. Conjugate
gradient usually finds the optimum weights quickly, but there is no guarantee that the
weight values it finds are globally optimal. So it is useful to allow DTREG to try the
optimization multiple times with different sets of initial random weight values. The
number of tries allowed can be specified here.
86
6.5.3 Converging to the Optimal Solution – Conjugate Gradient
Given a set of randomly-selected starting weight values, DTREG uses the
conjugate gradient algorithm to optimize the weight values.
Most training algorithms follow this cycle to refine the weight values:
1. Run the predictor values for a case through the network using a tentative set
of weights.
2. Compute the difference between the predicted target value and the actual
target value for the case. This is the error of the prediction.
3. Average the error information over the entire set of training cases.
4. Propagate the error backward through the network and compute the gradient
(vector of derivatives) of the change in error with respect to changes in
weight values.
5. Make adjustments to the weights to reduce the error.
Each cycle is called an epoch.
Because the error information is propagated backward through the network,
this type of training method is called backward propagation.
DTREG uses the conjugate gradient algorithm to adjust weight values using
the gradient during the backward propagation of errors through the network..
Conjugate gradient also does not allow the user to specify learning rate and
momentum parameters.
6.5.4 Avoiding over fitting
Over fitting occurs when the parameters of a model are tuned so tightly that
the model fits the training data well but has poor accuracy on separate data not used
for training. DTREG has two methods for dealing with over fitting: (1) by selecting
the optimal number of neurons and (2) by evaluating the model as the parameters are
being tuned and stopping the tuning when over fitting is detected. This is known as
early stopping.
87
If we enable the early-stopping option, DTREG holds out a specified
percentage of the training rows and uses them to check for over fitting as model
tuning is performed. The tuning process uses the training data to search for optimal
parameter values. But as this process is running, the model is evaluated on the holdout test rows, and the error from that test is compared with the error computed using
previous parameter values. If the error on the test rows does not decrease after a
specified number of iterations then DTREG stops the training and uses the
parameters which produced the lowest error on the test data.
Thus, DTREG allows programmers to specify no. of network layers i.e. how
many inputs, hidden and output layers to be taken in order to get the best results.
There can be any no. of neurons in the three layers as per our requirements. No. of
neurons to be taken in hidden layer is the most challenging task. If too few neurons
are selected, the model may not be adequate to model complex data, and if too many
neurons are selected it may over-fit the data. Automatic neuron optimization
automatically selects the no. of neurons in hidden layer which give us the minimum
misclassification and thus the optimal results.
6.6
ANN IMPLEMENTATION WITH MATLAB
6.6.1 Back propagation learning algorithm
Back propagation, or propagation of error, is a common method of teaching
artificial neural networks how to perform a given task. Back propagation learning
algorithm can be divided into two phases:
1. Propagation
2. Weight update.
Each propagation has two steps. First step involves forward propagation of a
training pattern's input through the neural network in order to generate the
propagation's output activations. And second step involves Back propagation of the
propagation's output activations through the neural network using the training
pattern's target in order to generate the deltas of all output and hidden neurons.
88
For each weight-synapse it is required to multiply its output delta and input
activation to get the gradient of the weight. And then bring the weight in the opposite
direction of the gradient by substracting a ratio of it from the weight. This ratio
influences the speed and quality of learning; it is called the learning rate. The sign of
the gradient of a weight indicates where the error is increasing; this is why the weight
must be updated in the opposite direction.
The two phases are repeated until the performance of the network is good
enough. As the algorithm's name implies, the errors (and therefore the learning)
propagate backwards from the output nodes to the inner nodes. So technically
speaking, back propagation is used to calculate the gradient of the error of the
network with respect to the network's modifiable weights.
Back propagation is the most popular neural network algorithm. So much so
that in many cases it is almost blindly applied to a problem without much regards (or
respect) for its limitations. The back propagation algorithm is popular for its
simplicity of implementation and its ability to quickly generate networks that have
the capability to generalize.
Notation
Input
Hidden
output
Number of neurons
n+1
q+1
p
Signal function
linear
sigmoidal
sigmoidal
Neuron index range
i=0,….n
h=0,….q
j=1,…p
Activation
xi
zh
yj
Signal
∆(xi)
∆(zh )
∆(yj)
Weights
wih
whj
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6.7
ALGORITHM
The basic procedure of gradient descent based learning is outlined as follows in steps
1-6.
1. Select a pattern Xk from the training set T, and present it to the network.
2. Compute activations and signals of input, hidden and output neurons in that
sequence.
3. Compute the error over the output neurons by comparing the generated
outputs with the desired outputs.
4. Use the error calculated to compute the change in the hidden to output layer
weights and the change in input to hidden layer weights such that global error
measure gets reduced.
5. Update all weights of the network in accordance with the changes computed
in Step 4.
Hidden to output layer weights are calculated as:
wk+1 hj =wk hj +∆wk hj
(6.1)
Input to hidden layer weights is calculated as:
wk+1 ih =wk ih +∆wk ih
where ∆w
k
hj and
∆w
k
ih are
(6.2)
weight changes computed in Step 4.
6. Repeat steps until global error falls below a predefined threshold.
Each pattern presentation represents an iteration; and a presentation of the
entire training set is called an epoch.
6.7.1 Square Error Performance Function
The vector pairs in training set are assumed to be sample representatives of
some unknown function which neural network is supposed to approximate. The
general idea is to employ the gradient of the pattern error in order to reduce the
global error over the entire training set i.e. error gradient for a pattern is computed
and used to change weights in the network. Such weight changes are effected for a
sequence of training pairs picked from the training set. Each weight change perturbs
the existing neural network slightly, in order to reduce the error on the pattern.
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If we denote the neural network as N then this update procedure generates a
sequence of neural networks N1, N2, N3,……Nk… ,where , for a given pattern X,
the neural network Nk generates an output signal vector S(Yk), Yk, being the vector of
activations of output layer neurons. The kth training pair (Xk,Dk) then defines the
instantaneous error:
Ek=Dk-S(Yk)
(6.3)
Where Dk is a vector response desired when input Xk is presented as input to the
network.
After the error has been computed, it is compared with the tolerance rate. If
the error is tolerable i.e. value of error is less then the tolerance rate then it means
current weight values are the values with which neural network can be trained. If the
error is not tolerable i.e. value of error is greater then tolerance rate, weights need to
be updated further in order to get the optimized results.
6.7.2 Computation of neuronal signals
1. For input layer:
S(xi)=xi
(6.4)
where xi is the ith component of the input vector presented to the network
2. For hidden layer:
Zh=S(xi)* wih and
(6.5)
S(zh)=1/(1+e^ (-zh))
( 6.6)
3. For output layer:
yj=S(zh)* whj and
(6.7)
S(yj)= 1/(1+e ^ (-yj)
(6.8)
6.7.3
Computation of error gradients
1. Hidden to output layer weight gradients:
Signals S(zh) generate output layer activations yj through weights whj. Signals
S(yj) come from yj and are compared with desired outputs dj to generate an error
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estimate ej from which Ek is derived. The reverse arrows show the route to whj from
Ek in order to calculate the gradient ∆E/∆whj using the chain rule of calculus:
Signals S(zh) generate output layer activations yj through weights whj. Signals
S(yj) come from yj and are compared with desired outputs dj to generate an error
estimate ej from which Ek is derived. The reverse arrows show the route to whj from
Ek in order to calculate the gradient ∆E/∆whj using the chain rule of calculus:
∆S (y j ) ∆y j
∆E
∆E
=
*
*
∆w hj ∆S(y j )
∆y j
∆w hj
(6.9)
The intermediate partial derivatives in eq 3.9 are derived as:
∆E
= − (d j − S(y j )) = −ej
∆S(y j )
∆S(y j )
∆y j
∆y j
∆w hj
= S' (y j )= S(y j )(1 − S(y j ))
= S(z h )
(6.10)
(6.11)
(6.12)
The above three equations are substituted in eq 3.9 which yields the result as:
∆E
= −e j S' (y j )S(z h )
∆w hj
= -δj S(zh)
(6.13)
(6.14)
Here δj= ej S’(yj) represents an error and signal slope product. ∆j is the error
scaled by the slope. For very large or very small activations the slope S’(.) is very
small, and correspondingly δj is relatively small. For zero activations the slope is the
largest, and δj is relatively large.
2. Input to hidden layer weight gradients:
Target error derivative is calculated as:
∆E
∆E
∆S(z h ) ∆z h
=
×
×
∆w ih
∆w ih ∆S(z h ) ∆z h
(6.15)
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Since E is a function of the errors at each of the output neurons, the route to S(zh)
will be through all the output neurons:
 ∆E
∆y j 
∆E
= ∑ j=1 p 
×

∆S(z h )
 ∆y j ∆S(z h )
(6.16)
The eq. 3.16 when substituted in eq. 3.15 yields:
k
P 
∆E k
 ∆E k ∆y j
= ∑
∆Wihk j=1  ∆y kj ∆s z kh


S'(Zk S (X k ))
h
i
(6.17)
( )

( )
( ) ( ) ( )
 ∆E k ∆S y kj
Sy kj

∑
k
k
k
j=1 
 ∆S y j ∆S Z h ∆S Z h
P
=
∑{
}(
P
=
( )
(6.18)
S'
(
Z kh
S
( ))
X ik
− e kj S ' y kj Whjk S ' Z k X k
h
i
j=1
P
(
)
)( )
= − ∑ s kj w khj s' z kh x ik
(6.19)
(6.20)
j=1
error back propagation
In order to calculate the extent of contribution of the hth hidden neuron to the
output error scaled errors δj are back propagated through weights.
Thus, the error contribution of the hth hidden node is
P
e kh = ∑ s kj w khj
(6.21)
j=1
and
( )
e kh = e kh s' z kh
(6.22)
We, therefore have
sE k
= −s kh x ik
k
sw ih
(6.23)
In this way the errors may be propagated backwards through as many layers
as desired
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6.7.4 Weight Updates
Now, finally new weights will be computed by formulas given below. These
are the weight changes between hidden and output layer & between input and hidden
layer. After the weights have been calculated, error will be calculated which is again
compared with tolerance. If the error is less than the tolerance then these are the final
weights which will be used for training. If the error is more than the tolerance, the
whole process is repeated again until global error is reduced as desired.
1.
For hidden to output layer weights:
w khj−1 = w khj + ∆w khj
2.
(6.24)
=
 ∆E k 

w khj + n  −
 ∆w k 
hj


=
w khj + ns kj s z kh
(6.25)
( )
(6.26)
For input to hidden layer weights:
w khj−1
=
w khj + ∆w khj
=
 ∆E k
w ihk + n  −
k
 ∆w ih
(6.27)



w ihk + ns kh x ik
(6.28)
(6.29)
This is the procedure which is used for training and classification. Activations
and signals of input, hidden and output neurons are found in each iteration which are
then used to find the error. The error calculated is then used to compute the change in
the hidden to output layer weights and the change in input to hidden layer weights
such that global error measure gets reduced. Finally, all weights of the network are
updated in accordance with the weight changes computed.
94
6.8
EXPERIMENTAL RESULTS
In this research work, analysis of E.Coli environment has been done.
Simulation has been done in DTREG and MATLAB. DTREG software gives the
misclassification around 11% whereas MATLAB provides much better results and
reduces misclassification to 6.9%.
Protein kinease C plays an important role in angiogenesis and apoptosis in
cancer. During the phase of angiogenesis the growth factor is up regulated where as
during apoptosis the growth factor is down regulated. For down regulation of growth
factor the pH environment of intra-cellular fluid has a specific range in the alkaline
medium showing dissipative phenomenon. Protein kinease C along with E-coli through
interaction of Selenometabolite is able to maintain that alkaline environment for the
apoptosis of the cancer cell with inhibition of the growth factor related to
antioxidant/oxidant ratio. The present work through implementation of Artificial Neural
Network, also DTREG, has focused on metastasis linked with Capacitance Relaxation
phenomena and down regulation of growth factor (VGEF). In this work a distributed
neural network has been applied to a data mining problem for classification of cancer
stages in order to have proper diagnosis of patient with PKC sensor simulated in E.coli
environment. The Network was trained off line using 270 patterns each of 6 inputs.
Using the weight obtained during training, fresh patterns were tested for accuracy in
diagnosis linked with the stages of cancer.
It is to be noted that the environment of survival of E. coli over a wide range has
been analyed in relation to proliferation and inhibition of metastasis with cyclic genetic
reform mediated through oxidant/antioxident.The Oxidant/Antioxident can be simulated
in the E-coli environment through the Electrostrictive energy derived from capacitance
relaxation shown in figure 6.6.
95
Fig.6.6 Oxidant/antioxidant ratio of E. Coli w.r.t. pH of its Environment
Fig.6.7 Status of Electrostrictive energy in cancer cells.
Figure 6.6 and 6.7 shows the status of electrostive energy and capacitance
relaxation in cancer cells respectively. It is to be noted that with higher value of
electrostictive energy apolosis in cancer cell is initiated.
It is interesting to note that the matastasis of the cancer cell can be corelated
to capacitance relaxation phenomenon against pH which is nothing but the E.coli
environment and this phenomenon is repesented in fig 6.9.
96
Fig.6.8 Capacitance relaxation in cancer cels
Fig. 6.9 Capacitance relaxation- metastasis curve related to pH
E. coli related archaeabacteria in lipid peroxidation is influenced by the
asymmetry of the lipid and the more the lipid peroxidation less is the relaxation time
Asymmetry is linked with pH gradient mediated by lipid peroxidation.
Relaxation of polar head is related archaeabacteria. Na+/H+ antiporters cause
enhancement of lipid peroxidation Antiporters maintain alkaline environment where
as lipid peroxidation initiated by antiporters maintains acidic pH homeostasis of the
fluid of the E. coli archaeabacteria. Antiporters initiate lipid peroxidation which
sustains pH gradient in the environment of archaeabacteria. Thus the asymmetry of
polar head of archeobacteria (E. coli) is sustained in its pH environment mediated by
97
the antiporters linked with the electrochemical gradient across its membrane. DNAbinding proteins from starved cells (Dps proteins) protect archaeabacteria primarily
from oxidative damage. The Oxidative stress infuenced by catecolamine [Tapas. K.
Basak-2005] causes damage of DNA up to pH from 3 to 4.5 and the effect of
antioxidant reduces the oxidative stress beyond pH 4.5. This particular phenomenon
has been correlated with the pH for the status of oxidant to antioxidant ratio in cancer
cell. Oxidant/antioxidant ratio in E.Coli with lower pH is linked to metastasis and
capacitance relaxation phenomenon [Basak TK-2009, .Guangyue, Shi-2010]. Debye
dispers Tumors induce blood vessel growth (angiogenesis) through Vascular
Endothelial Growth Factor (VEGF). Over-expression of VEGF causes increased
permeability in blood vessels in simulating angiogenesis. Malignant cells exhibit
capacitance Relaxation phenomena and it has been correlated with VEGF. In the
process of pH homeostasis influenced by Ca and NO the cell signaling pathway is
modulated by NRF2 that tends to reduce the oxidative stress due to VEGF [Dinel j2005 Dinel j]. This is achieved through VEGF mRNA levels mediated through the
increase in expression of intracellular GSH.
Associated
oxidative
stress
causes
DNA
damage
with
increase
oxidant/antioxidant ratio in E.Coli for pH range 3-4.5. As DNA damage is correlated
with increased oxidant/antioxidant ratio in E. coli for tumor progression. Similarly
DNA recovery is correlated to decreased oxidant/antioxidant ratio in E. coli when the
effect of antioxidant reduces the oxidative stress beyond pH 4.5.
Oxidant/antioxidant balance is an important factor related to initiation and
progression of cancer. Clinical research shows that more the oxidant/antioxidant ratio
more is the metastasis.
It has been investigated that antioxidant activity occurs at higher values of pH.
Referring to Fig. below, we can say that as pH becomes more basic (increasing pH),
less will be the oxidant/antioxidant ratio. It can be noted that the environment of
survival of E. coli over a wide pH range has been analyzed in relation to proliferation
and inhibition of metastasis with cyclic genetic reform.
98
Oxidative stress causes damage of DNA up to pH from 3 to 4.5 and the effect of
antioxidant reduces the oxidative stress beyond pH 4.5. This particular phenomenon has
been correlated with the pH for the status of oxidant to antioxidant ratio in cancer cell.
Oxidant/antioxidant ratio in E.Coli with lower pH is linked to metastasis [Himmetoglu
Solen,-2009, Marika Crohns -2009, T. K. Basak -2008, 2009].
Fig.6.10 Oxidant/ antioxidant ratio Vs Ph
When electrostrictive energy decreases, ratio of oxidant/antioxidant increases
[Basak T. K-2008].Lipid peroxidation relaxatation time of lipid of E Coli also
increases and pH decreases leading to metastasis. From Figure 6.9, it is clear that
ratio of oxidant/antioxidant decreases with increment of pH. Thus we can conclude
that oxidant/antioxidant ratio increases with decrease in pH value. With low acidic
pH (pH=3.5), the relaxation time increases which initiates metastasis. Again
metastasis can be retrarted with the increase of pH (pH=7.5) with higher value of
electrostictive energy and it causes decrement of oxidant/antioxidant ratio associated
with decreased relaxation time. With more metastasis oxidant/antioxidant ratio will
increase. With lower metastastasis oxidant/antioxidant ratio will decrease, showing
dissipative phenomenon.
99
6.8.1
Results of ANN as a Classifier in Dtreg
Table 6.1 Result of ANN implementation in DTREG
Number
Variable
Class
Type
1.
pH
Predictor
Continuous
Missing
rows
0
2.
ER
Predictor
Continuous
0
153
3.
Cr
Predictor
Continuous
0
177
4.
Meta
Predictor
Continuous
0
166
5.
Oxi ratio
Predictor
Continuous
0
69
6.
Stage
Target
Categorical
0
4
Categories
142
The table above shows that stage has been taken as the target variable
whereas other five attributes i.e. pH, oxidant/antioxidant ratio, metastasis,
capacitance relaxation value and estrogen receptors ratio are the predictor variables.
Predictor variables is helping in the classification process. Project parameters display
information about target variable, no. of predictor variables, type of model being
used, no. of neurons in hidden layer and activation functions being used.
Model size summary report
Network size evaluation was performed using a 20% hold-back sample.
100
Table 6.2: Display of Model size
Hidden layer 1 neurons
% Misclassifications
2
15.38462
3
15.38462
4
15.38462
5
15.38462
6
11.53846
7
11.53846
8
15.38462
9
15.38462
10
15.38462
11
15.38462
12
15.38462
13
7.69231  optimal size
14
15.38462
15
15.38462
16
23.07692
17
15.38462
18
30.76923
19
15.38462
20
19.23077
The network will be built using 13 neurons for hidden layer 1.
In table 6.2 we can see how the misclassification percentage varies on
varying no. of neurons in hidden layer. The optimum design shows there should be
13 neurons in hidden layer for minimum classification and accurate results.
101
Table 6.3: Miscalassification of Training Data
Category
1
Actual
Count
Weight
8
8
Count
8
Misclassified
Weight
Percent
8
100.000
Cost
1.000
2
52
52
2
2
3.846
0.038
3
43
43
0
0
0.000
0.000
4
27
27
6
6
22.222
0.222
Total
130
130
16
16
12.308
0.123
Table 6.4: Miscalassification of Validation Data
Category
1
Actual
Count
Weight
8
8
Count
8
Misclassified
Weight
Percent
8
100.000
Cost
1.000
2
52
52
4
4
7.692
0.077
3
43
43
0
0
0.000
0.000
4
27
27
3
3
11.111
0.111
Total
130
130
15
15
11.538
0.115
Table 6.3 & 6.4 shows misclassification tables for training and validation. The
average misclassification of validation data comes out to be around 11.5%
Fig. 6.11 Graph of Model Size Vs Error Rate
102
The graph above is a plot showing number of neurons in hidden layer Vs
percent misclassification. For 13 neurons in hidden layer, error comes out to be
minimum.
6.8.2
ANN as a classifier in matlab
No. of samples used for training
=
260
Same samples when used for validation that gives error
Therefore, misclassification
=
=
18
18
x 100 = 6.9%
260
No. of iterations taken is upto 2000
No. of neurons in hidden layeris taken=
5
Tolerance
=
0.01
Momentum is taken
=
1.2 and Learning Rate is taken=0.8
Sumerror initially is taken
=
0
No. of iterations starting from 0 will go upto 2000 till sumerror is greater than
tolerance
Fig. 6.12 Graph of sumerror Vs iterations
103
The above graph (Fig. 6.12) is a plot how sumerror varies with increasing no.
of iterations. The no. of iterations in our proposed model is around 2000 for desired
sumerror and tolerance.
Below given are the snapshots of how the sumerror decreases with the
increase in no. of iterations. Iterations upto which process will continue is 2000, so it
is shown in snapshot that on completion of 2000 iterations, we get the final weight
matrices.
These weight matrices are then used for classification of the data.
Fig. 6.13 Snapshot showing sumerror with increase in iterations
104
Fig. 6.14: Snapshot displaying weight matrices at 2000th iteration
The above snapshot(Fig.6.14) shows weight matrices at 2000th iteration.
Now, the training part has been completed. After this we need to check
whether the designed neural network classifies the data correctly or not. The below
given snapshots shows that how the neural network classifies stage of cancer on
giving 5 inputs of pH, oxidant/antioxidant ratio, estrogen receptor ratios, metastasis
value and capacitance relaxation value.
Inputted data has been given column wise. 260 samples have been used for
training as well as classification.
105
Fig. 6.15: Snapshot displaying classification
6.8.3 Weight matrices:
Wih is the weight matrix between input and hidden layer. And Whj is the
weight matrix between hidden and output layer.
Here, Matlab simulation is linked in such a way that the optimum value of the
learning rate and momentum coincides
Table 6.5 [6*5]weight matrix displaying weights between input and hidden layer
Wih
-1.8247
1.085
-6.6023
-0.42835
-2.4974
-4.3012
-3.9735
7.6455
-2.8382
0.52438
-0.74473
0.26892
-1.7553
-0.15328
1.5206
-0.41868
-0.70051
-1.4084
-3.5615
-4.2045
-3.2854
-1.0747
-0.36513
0.74208
-3.8915
1.3873
0.38019
-1.5126
2.3367
0.12508
106
Whj
-0.34443
0.47733
1.4617
-5.1274
1.9881
0.070157
Figure 6.16: [6*1]weight matrix displaying weights between hidden and output layer
6.9
Results of Classification in MATLAB
The input and output given below are the simulated data obtained as a result of
implementation in MATLAB
Table 6.6: Input and Output Simulations in MATLAB
pH
ER ratio
CR value
Meta
value
Oxi/anti
ratio
3.986508
0.888058
1.17371
1.16946
1.176
Actual
stage of
of cancer
4
5.109075
0.788784
1.17627
1.17174
1.16949
3
3
4.952163
0.825593
1.17597
1.1714
1.169877
4
3
6.013101
0.355356
1.17816
1.1736
1.16851
2
2
6.581641
0.266838
1.17932
1.1748
1.168363
1
1
6.730979
0.260783
1.17951
1.17504
1.1683341
1
1
5.227494
0.746477
1.17645
1.17201
1.169288
3
3
5.651322
0.519325
1.17739
1.17288
1.168657
2
1
4.28771
0.880807
1.1743
1.16968
1.171216
4
4
Recalled
stage
4
The above chart shows input and output simulations of the ANN implementation in
MATLAB. It has been found in most of the cases recalled stage is same as the actual
stage.
107
6.9.1
Performance Analysis
Table 6.7: Performance Analysis
Stage of
Cancer
Stage 4
53
Accurate
classification
84.91%
Misclassification
15.09%
Accuracy
%
69.81%
Stage 3
86
100%
0%
100%
Stage 2
104
84.62%
15.38%
69.23%
Stage 1
17
94.12%
5.88%
88.23%
Total data size
The above table 6.7 shows performance analysis. It shows total no. of
samples that has been taken for each stage. It also shows accurate classification and
misclassification in each stage. Misclassification means data that is not being
classified accurately. Finally, table also displays % accuracy of each stage.
6.10 CONCLUSION
With the help of ANN and data mining technique majority of the cancer cases
are correctly classified into different stages for subsequent treatment in the E.coli
domain.
This classification helps the physicians to correctly identify the stage of the
cancer patient and also helps in proper decision making for the type of treatment to
be provided.
The work done has designed and developed a comprehensive model for the
analysis of stages of cancer. Analysis has been in DTREG software and MATLAB.
DTREG software employs Black-Box approach which means that end user is
not able to see intermediate results. It uses a fixed set of in-built functions for
classification. The database has been divided into two data sets with the records in
108
each of them randomly selected. One of the data set was used for training and other
for testing the data. Also there are various other constraints as a result of which
DTREG gives misclassification around 11.5%.
Proposed approach is flexible and gives more accurate results. Parameters
such as momentum, learning rate can be made to set to give accurate results. Unlike
DTREG, MATLAB approach can use any function. Many functions have been tried
but it has been found that sigmoidal function gives the best results. Misclassification
using MATLAB has been reduced to 6.9%. Thus, in the proposed model, an efficient
means has been proposed for the classification of cancer into different stages under
the E.coli environment.
In the present work partial dataset are prepared with the relevance angiogenesis
in cancer linked with capacitance relaxation phenomena and metastasis in the pH
environment of E-coli with Protein Kinease C. This data is mind separately using
decision based data mining algorithm. The same data set are used for classification in
ANN with Back propagation algorithm. The classifier in used for prediction of the
status of the subject with cancer in different stages of metastasis corosponding to
respective pH range of the intra-cellular fluid stimulated in E-coli environment with
Protein Kinease C. This prediction in respect of the subject related to the stages of
cancer may be useful for the Healthcare Management and treatment of cancer patient.
In ANN implementation learning rate with a variable function also the error
involving the stages in metastasis is 6% to 8% linked with the stimulated environment
of E-coli corresponding to the pH of intra-cellular fluid.
109
CHAPTER 7
A DISSIPATIVE MODEL OF ANTIPORTER LINKED
TO HYDROPHOBICITY IN pH MEDIUM
7.1
INTRODUCTION
E.Coli related bacteria have the property of lipid peroxidation and
hydrobhobicity with microbial adhesion to solvent (M.AT.S.). In this respect the
lipid of E.Coli undergoes polar head orientation against the pH gradient in the
environment of E.Coli. This phenomenon has been projected in this chapter in a
comprehensive way in higher acidic medium, which happens to be dissipative in
nature for Na+/H+ in acidic medium gradually going towards alkaline medium.
E.Coli related archaeobacteria possesses the property of lipid peroxdation
which arises from asymmetry of Lipid. The lipid per oxidation is related to relaxation
time with change of hydrophobicity of E.Coli in the pH gradient [J. Li-1999,
Guzman-2003, Priya C. Kadam-1996]. So more the asymmetry of Lipid more will be
the relaxation time linked with the orientation of polar head [Gliozzi, A.-1986].
Antiporters initiates lipid per oxidation which sustain pH gradient in the environment
of archeobacteria [Orna Amster-Choder and Andrew Wright-1997].
M.A.T.S (Microbial adhesion to solvents) is used in order to examine the
influence of pH and ionic strength on electron donor/electron acceptor properties
with antiporterand surface hydrophobicity of Staphylococcus aureus ATCC 25923
and Escherichia coli AL52. In this technique it was found that the electron donor
character for both strains was influenced by pH and ionic strength. This effect was
more important for S. aureus. The electron acceptor was expressed for acidic pH.
Regardless of pH, the electron donor character of S. aureus changed when the ionic
strength increased. For E. coli, the electron donor character varied with ionic strength
at pH 2, nevertheless, for other pH, this character was not much influenced by ionic
110
strength. Moreover, surface hydrophobicity of two microbial cells surface was
affected by pH and ionic strength. It was maximal at acidic pH and lower at basic pH
for S. aureus. Regardless of pH and ionic strength, E. coli was hydrophilic in UTI
and GID. [F. Hamadi1-2004, T. K. Basak-2009]
The transport and deposition behavior of Escherichia coli O157: H7 was
investigated in saturated packed-bed columns and micro model systems over a range
of ionic strength (1, 10, and 100 mM) and pH (5.8, 8.4, and 9.2) conditions. At a
given Ionic Strength, elevated solution pH resulted in decreased deposition as a result
of the increase in the measured zeta potential. This deposition trend was consistent
with predictions from classic Derjaguin-Landau-Verwey-Overbeek (DLVO) theory.
Conversely, the E. coli O157:H7 deposition is inversely proportional to Ionic strenth
(1-100mM) at high pH conditions (8.4 and 9.2). This deposition trend was not
consistent with DLVO theory, but could be explained by pH-associated electrosteric
stabilization. This phenomenon is driven by the pH-dependent protonated state of
functional groups on E. coli O157:H7 surface macromolecules and the corresponding
conformational state of the bacterial polymers. Results from this study demonstrate
that retention of E. coli O157:H7 cells in porous media are a complex process that
depends on the solution chemistry, cell-cell interactions, and pore structure. The
findings in this study also imply that previous work conducted at lower pH and IS
conditions may underestimate E. coli O157:H7 travel distance in higher salt and pH
groundwater environments [Hyunjung N.Kim-2009].
Exposure to low pH triggers an increase in the hy-drophobicity of the colicin
E3 molecule. Using a [3H] Triton X-100 binding assay we have shown that the
amount of detergent (at supramicellar concentrations) associated with colicin E3
increased dramatically at pH 3.8 and below. Interaction of colicin E3 with aso- lectin
vesicles was monitored by following its cross- linking with two different
photoactivatable radioac- tive phospholipid analogues. At neutral pH colicin E3 was
cross-linked with the phospholipid probing the membrane surface whereas at pH 4.5
and below, the bacteriocin reacted with the phospholipid probing the hydrophobic
core of the bilayer [Vincent Escuyer-1986].
111
NMR studies on the denatured states of proteins indicate that residual
structure often resides predominantly in hydrophobic clusters. Such hydrophobic
cluster formation implies burial of apolar surface and, consequently, is expected to
cause a decrease in heat capacity. We report here that, in the case of ribonuclease H
from the thermophile Thermus thermophilus, a sharp decrease in denatured-state heat
capacity occurs at about pH 3.8; this result points to the formation of hydrophobic
clusters triggered by the protonation of several (about four) carboxylic acid groups,
and indicates that the burial of apolar surface is favored by the less hydrophilic
character of the uncharged forms of Asp and Glu side-chains. The process is not
accompanied by large changes in optically active structure, but appears to be highly
cooperative, as indicated by the sharpness of the pH-induced transition in the heat
capacity. This acid-induced hydrophobic burial in denatured T. thermophilus
ribonuclease H is clearly reflected in the pH dependence of the denaturation
temperature (i.e. an abrupt change of slope at about pH 3.8 is seen in the plot of
denaturation temperature versus pH), supporting a role for such denatured-state
hydrophobic clusters in protein stability. The finding of cooperative protonation of
several groups coupled to surface burial in denatured T. thermophilus ribonuclease H
emphasizes the potential complexity of denatured-state electrostatics and advises
caution when attempting to predict denaturedstate properties on the basis of simple
electrostatic models. Finally, our results suggest a higher propensity for hydrophobic
cluster formation in the denatured state of T. thermophilus ribonuclease H as
compared with that of its mesophilic counterpart from Escherichia coli [H Mercedes
Guzman-Casado-2003].
The above mentioned feature of hydrophobicity is very is very important in
respect of coupling of antagonist with protein Guanylate cyclase in E.coli which
may facilitates phosphorylation to counter the UTI (Urinary Tract Infection) and
GID(Gastro Intestinal Disease) caused by E.Coli [ J K crane, MS wehner-1992].
112
7.2
A MAT LAB MODEL & SIMULATION
Fig.7.1 Model of Lipid Peroxidation due to Na+/H+ antiporter
The fig 7.1 shows the Model of Lipid Peroxidation due to Na+/H+ antiporter
in which two biofeedback models present in cascade of which the first one is
antioxidant negative feedback system incorporating transduction phase one, whose
output is input to the second negative feedback system of lipid peroxidation
homeostat incorporating transduction phase2.
Due Na+/H+ antiporter the lipid peroxidation Vs pH are given as follows.
Fig.7.2 Lipid Peroxidation Vs pH due to 0.2 p.u change in Na+/H+ Antiporter
113
Due to 0.2 p.u change in Na+/H+ antiporter the lipid asymmetry is shifted
towards right of pH=5 with response peak at pH=5.1
Fig.7.3 Lipid Peroxidation Vs pH due to 0.4 p.u change in Na+/H+ Antiporter
Due to 0.4 p.u change in Na+/H+ antiporter the lipid asymmetry is shifted
towards right of pH=5 with response peak at pH=5.2
Fig.7.4 Lipid Peroxidation Vs pH due to 0.6 p.u change in Na+/H+ Antiporter
Due to 0.6 p.u change in Na+/H+ antiporter the lipid asymmetry is shifted
towards right of pH=5 with response peak at pH=5.35
114
Due to K+/H+ antiporter, lipid peroxidation axis asymmetry shifted towards
left side. This phenomenon occurs in acidic environment. Conversely due to Na+/H+
antiporter, lipid peroxidation axis asymmetry shifted towards right side. This
dissipative phenomenon occurs in alkaline environment.
The shift of axis of symmetry shifted towards the right side will promote
inhibition of the growth factor at the appropriate pH range. Whereas shift of axis
symmetry towards the left side will produce oxidative stress in the low acidic
medium linked with the proliferation of the growth factor for sustaining metastasis.
The asymmetry of polar head.of archeobacteria (E. Coli) is sustained in its pH
environment mediated by the antiporters linked with the electrochemical gradient
across its membrane.
7.3
CONCLUSION AND DISCUSSION
From the result it is evident that for Na+/H+ the antiporter the lipid
asymmetry is shifted towards the right and the shift is maximum related to the peak
response at pH 5.35 & decaying/dissipating with increasing pH level.
The shift of axis symmetry towards left side will promote antioxidant release
at the appropriate pH level. Whereas the shift towards left side in the subject with
hypoxia.
Thus, the result derived from Matlab Simulation linked with hydrophobicity
of E. Coli in relation to the axis asymmetry of polarhead of E.Coli is very interesting
& the above mentioned feature of hydrobhobicity is very important in respect of
coupling in protein of beef muscle which may facilitate phosphorylation linked to
mRNA transcriuptions. This phenomenon derived from Mat Lab simulation is related
to the dissipative phase of antiporters linked to E-coli.
However at neutral pH 7.0 orientation of E.coli polarheads may be inhibitive
and thus hydrophobicity of E.coli with antiporters has a distinct dissipative phase
linked to the pH gradient. This particular phenomena of hydrophobicity is initiated
by the laws of surface charge.
115
CHAPTER 8
ANALYSIS OF THERMAL ENERGY DISSIPATION OF
BRAIN WITH REGARDS TO HUMAN BODY-A STUDY
8.1
INTRODUCTION AND DEFINITIONS
Mind means mental activity. Mind is the product of brain activity, which
depends on brain’s self-organization. Brain and mental activities occur concurrently
like two parallel streams. Mental activity includes three classical domains of
conation, cognition, and affect.
Conation means intention or volition with self-activation toward a goal.
Volition is the mental faculty by which an individual decides upon and commits to a
particular course of action. Volition involves a sense of self and an active control
over the decision and behavior.
Cognition means information processing.
Affect is any experience of feeling or emotion, ranging from suffering to
elation. Affect can be irreflexive meaning directly experienced or reflexive when the
person makes the emotion an object of his or her conscious perception.
Feeling is a self-contained phenomenal experience. Feelings can vary along
three dimensions: pleasant-unpleasant, exciting-calm, and arousal-relaxation.
8.2
THE MIND IN NEUROSCIENCE AS DISSIPATIVE
HOMEOSTAT
Neuroscience is a scientific study of the brain and mind. A lot of progress has
been made in our understanding of the brain that mediates the mental processes. The
brain is an interface between the internal and external environments of a living
organism. In general, the internal environment is monitored and managed by
the brainstem-limbic system, whereas the external environment and situations are
116
handled by the thalamo-cortico-striate system. The major conscious mental functions
including conation, cognition and emotion are experienced and responded to by
the multimodal associative cortex. The back part of the associative cortex, occipitoparieto-temporal
cortex mediates
perception,
cognition,
attention,
memory,
comprehension, self-situational monitoring and awareness. It has been called
the semantic-conceptual field. The front part of the multimodal associative
cortex frontal-premotor-cingulate-prefrontal cortex is involved in sensory-motorcognitive and emotional integration, decision making, working memory, intention,
voluntary action, and the executive control of behavior and mentation.
Most complex mental functions are mediated by cortical neural networks
with multiple, reciprocal connections between different cortical areas and both
cerebral
hemispheres.
Some
areas
of
the
brain
are
specialized.
For
instance, Wernicke’s area specializes in processing language comprehension and
semantic integration andBroca’s area specializes in language expression including
semantic, phonological, and grammatical processing. Hippocampus and the medial
temporal
lobespecialize
in
episodic
memories
including
autobiographical
memories. Right hemispheric parietal association areas specialize in attention to both
right and left halves of environment and the body. Fusiform gyrus specializes in face
recognition. Prefrontal cortex with its “frontal intelligence” is crucial for working
memory, decision-making, and executive functioning, which includes initiation,
continuation, pausing, and stopping of cognition and voluntary action.
The attended information in the working memory is held in active awareness
by replaying it intentionally by the internal self-talk.If the information is not
rehearsed, it quickly dissipates out of awareness like the cliché, “out of sight, out of
mind.” This is also true of all emotions, thoughts and intentions, which dissipate if
not voluntarily or habitually maintained in awareness. Like many functions of the
body, the mind also follows the general principle of “dissipative homeostasis.” So, it
may be called the “homeostatic mind.” Homeostasis is defined as a state of
equilibrium between different but interdependent elements of an organism.
117
In this work a comparison and predictions of the dissipative quantum
phase/model of the Human body with neuropsychological data collected from
electroencephalograms resulting from high-density arrays fixed on the surfaces of
primary sensory and limbic areas of living being is been studied. Functional Human
brain imaging in relation to human body behavior reveals the formation of coherent
domains of synchronized neuronal oscillatory activity and phase transitions predicted
by the dissipative phase/model. The dissipative quantum model, presented in this
paper, extends the original quantum model of the Human brain to the dissipative
dynamics intrinsic to the Human body functional activity.
The Human brain is the central part of the nervous system. Of all the Human
body systems, the nervous system is the most complicated system in the Human
body. The Human brain is an intriguing organ, that has been studied right from the
time of Human body development in foetus. The Human brain weighs about 1.5 kg
in adults. The cerebrum, which forms the bulk of the Human brain, is divided into
two hemispheres, the right hemisphere and the left hemisphere.
Lot much effort and research has gone into mapping Human brain, its
patterns, and understanding its true nature of working. Many instruments have been
developed
as
Mass
action
which
has
been
confirmed
by
EEG,
by
magnetoencephalogram (MEG), functional magnetic resonance imaging (fMRI),
positron electron tomography (PET) and single photon emission computed
tomography (SPECT). These techniques gave observational access to real-time
imaging of ‘patterns of excitation’ and dynamical formation of spatially extended
domains of neuronal fields of activity. The neocortex is observed to be characterized
by the exchangeability of its ports of sensory input; its ability to adapt rapidly and
flexibly to short and long-term changes; its reliance on large-scale organization of
patterns of neural activity that mediate its perceptual functions; the incredibly small
amounts of information entering each port in brief behavioral time frames that
support effective and efficient intentional action and perception [Bach-y-Rita P1995, 2004].
118
None of the following four material agencies which have been proposed to
account for the processes involving large populations of neurons, appear to be able to
explain the observed cortical activity [Freeman W J 2005].
(1) Nonsynaptic transmission is essential for neuromodulation and diffusion
of chemical fields of metabolites providing manifestations of widespread coordinated
firing. It has been proposed [Bach-y-Rita P-1995] as the mechanism for
implementation of volume transmission to answer the question of how broad and
diffuse chemical gradients might induce phase locking of neural pulse trains at ms
intervals. However, it is too slow to explain the highly textured patterns and their
rapid changes [Freeman W J 2005]. Observations [Freeman W J 2006] show that
cortex indeed jumps abruptly from a receiving state to an active transmitting state.
Spatial amplitude modulated (AM) patterns with carrier frequencies in the beta and
gamma ranges (12–80 Hz) form during the active state and dissolve as the cortex
returns to its receiving state after transmission. These state transitions in cortex form
frames of AM patterns in few ms, hold them for 80–120 ms, and repeat them at rates
in alpha and theta ranges (3–12 Hz) of EEG [Vitiello G 2006 , Freeman W J 2006,
2003].
(2) Electric fields are revealed by the extracellular flow of dendritic current
across the resistance of Human body tissue [Freeman W J 1975]. Weak extracellular
electric currents have been shown to modulate the firing of neurons in vitro and have
been postulated as the agency by which neurons are linked together [Terzuolo C A1961].
(3) Magnetic fields of such intensity which can be measured 4–5 cm above
the scalp with MEG are generated by the intracellular current in palisades of
dendritic shafts in cortical columns.
(4) The combined agency of electric and magnetic fields propagating as radio
waves has also been postulated [Adey W R 1981]. However, neuronal radio
communication is unlikely, owing to the 80:1 disparity between electric permittivity
119
and magnetic permeability of the Human body tissue and to the low frequency (<100
Hz) and kilometer wavelengths of electromagnetic radiation at EEG frequencies.
Observations in this paper, has been proposed [Vitiello G 1995] as an
alternative approach to account for the observed dynamical formation of spatially
extended domains of neuronal synchronized oscillations and of their rapid
sequencing. The dissipative model explains indeed two main features of the EEG
data [Vitiello G 2006 ]: the textured patterns of AM in distinct frequency bands
correlated with categories of conditioned stimuli, i.e. coexistence of physically
distinct AM patterns and the remarkably rapid onset of AM patterns into (irreversible
sequences that resemble cinematographic frames. Each spatial AM pattern is
described to be consequent to spontaneous breakdown of symmetry (SBS) triggered
by external stimulus and is associated with one of the emerging unitarily in
equivalent ground states. Their sequencing is associated with the non-unitary time
evolution implied by dissipation, as discussed below. It has to be remarked that the
neuron and the glia cells and other physiological units are not quantum objects in the
many-Human body model of the Human body. This distinguishes the dissipative
quantum model from all other quantum approaches to Human body, mind and
behavior. Moreover, the dissipative model describes the Human body, not mental
states. Also in this respect this model differs from those approaches where Human
body and mind are treated as if they were a priori identical.
8.3
THE CONVENTIONAL MODEL
The dissipative quantum model of the Human brain, which we compare with
laboratory. The dissipative quantum model of body [Vitiello G 1995], on which we
focus our attention in this paper, extends the original quantum model of the Human
brain to the dissipative dynamics intrinsic to the Human body functional activity. The
main ingredient of the model is thus the mechanism of SBS by which long range
correlations are dynamically generated Water constitutes more than 80% to Human
body mass, and in the many-Human body model it is, therefore, expected to be a
major facilitator or constraint on Human body dynamics.
120
The symmetry which gets broken is the rotational symmetry of the electric
dipole vibrational field of the water molecules and of other biomolecules present in
the Human body structures [Vitiello G 1995]. The quantum variables are identified
with those of the electric dipole vibrational field and with the associated NG modes,
named the dipole wave quanta (DWQ). These are dynamically created and are not
derived from Coulomb interaction. If the cortex is at or near a singularity, the
external input or stimulus acts on the Human brain as a trigger for the breakdown of
the dipole rotational symmetry. As a consequence long range correlation is
established by the coherent condensation of DWQ bosons. SBS guarantees the
change of scale, from the microscopic dynamics to the macroscopic order parameter
field. The density value of the condensation of DWQ in the ground state (also called
vacuum state) acts as a label classifying the state and thus the memory thereby
created. The stored memory is not a representation of the stimulus, nor is it a
collection of stimulus features. Indeed, a specific feature of the SBS mechanism in
QFT is that the ordered pattern generated is controlled by the inner dynamics of the
system, not by the external field (stimulus) whose only effect is the breakdown of the
symmetry.
The recall of the recorded information occurs under the input of a
stimulus capable of exciting DWQ out of the corresponding ground state. In the
model, such a stimulus is called ‘similar’ to the one responsible for the memory
recording. Similarity is not an intrinsic property of the stimuli. Rather, it refers to
their effects on the Human body, namely inducing the formation or excitation of
‘similar’ ordered pattern(s). One shortcoming of the many-Human brain model in its
original form is that any subsequent stimulus would cancel the previously recorded
memory by renewing the SBS process and the consequent DWQ condensation, thus
printing the new memory over the previous one (‘memory capacity problem’).
Moreover, the model fails in explaining the observed coexistence of AM patterns and
their irreversible time evolution. These problems are solved by endorsing the original
many-Human brain model with dissipative dynamics [Vitiello G 1995, 2001],
accounting for the fact that the Human body is an open system in permanent
interaction with its environment.
121
8.4
THE DISSIPATIVE PHASE/MODEL
8.4.1 Coherent States
The details of the coupling of the Human body with the environment are very
intricate and variable, and thus they are difficult to be characterized and measured. In
QFT the canonical quantization of a dissipative system requires that the environment
in which the system is embedded must also be included in the formalism. This is
achieved by describing the environment as the time-reversed image of the system,
and this is realized by doubling the system’s degrees of freedom. In the dissipative
quantum model, the Human body dynamics is indeed described in terms of an
infinite collection of damped harmonic oscillators aκ (a simple prototype of a
dissipative system) representing the boson DWQ modes and by the ˜aκ modes which
are the time-reversed mirror images of the aκ modes. The doubled modes ˜aκ
represent the environment. The role of the ˜aκ system is to restore energy
conservation by balancing the (in-/out-) energy fluxes. The label κ generically
denotes degrees of freedom such as, e.g., spatial momentum, etc [Vitiello G 1995].
Although the living Human body operates far from equilibrium (Thermal
homeostasis), it evolves in time through a sequence of states where the energy fluxes
and heat exchanges at the system–environment interface are balanced: Esyst − Eenv ≡
E0 = 0. This energy balance is manifested in the regulation of mammalian Human
body temperature i.e., Thermal bio-homeostasis. The balanced non-equilibrium
system state, denoted by |0)N , is thus the system vacuum or ground state.
These patterns are represented by order parameters that are stable against
quantum fluctuations. This is a manifestation of the coherence of the DWQ boson
condensation. In this sense, the order parameter is a macroscopic observable and the
state |0)N provides an example of the macroscopic quantum state. The change of
scale (from microscopic to macroscopic) is dynamically achieved through the SBS
leading to boson condensation.
122
8.4.2 Phase transitions
The Human body (ground) state may be represented as the collection (or the
superposition) of the full set of states, |0)N for all N. In the memory space or the
Human body state space, each representation {|0)N } denotes a physical phase of the
system and may be conceived as a ‘point’ identified by a specific N-set (or θ-set). In
the infinite volume limit, points corresponding to different N (or θ) sets are distinct
points. The Human body in relation to the environment may occupy any of the
ground states, depending on how the E0 = 0 balance is approached. Or, it may be in
any state that is a collection or superposition of these Human body environment
equilibrium ground states. Under the influence of one or more stimuli (acting as
control parameters), the system may shift from ground state to ground state in this
collection (from phase to phase), namely it may undergo an extremely rich sequence
of phase transitions, leading to the actualization of a sequence of dissipative
structures formed by AM patterns.
Time-dependence of the DWQ frequency implies that higher momentum κcomponents of the N-set possess longer life-times. Momentum is proportional to the
inverse distance over which the mode propagates; thus modes with a shorter range of
propagation (more ‘localized’ modes) survive longer. In contrast, modes with a
longer range of propagation decay sooner. As a result, condensation domains of
different finite sizes with different degrees of stability are predicted by the model.
They are described by the condensation function f (x) which acts as a ‘form factor’
specific for the considered domain.
8.5
RESULT AND OBSERVATIONS
8.5.1 Observation in dynamic environment
The high spatial resolution required to measure AM pattern textures in
Human brain activity is achieved by using high-density electrode arrays, fixed on the
scalp or the epidural surface of cortical areas and fast Fourier transform (FFT)
[Burke B C-2003]. The set of n amplitudes squared from an array of n electrodes
123
(typically 64) defines a vector, A2(t), of the spatial pattern of power at time t. The
vector specifies a point on a dynamic trajectory in Human brain state space,
conceived as the collection of all possible (essentially infinitely many) Human body
states. The measurement of n EEG signals defines a finite n-dimensional subspace,
so the point specified by A2(t) is unique for a spatial AM pattern of an a periodic
carrier wave. Similar AM patterns form a cluster in n-space, and multiple patterns
form either multiple clusters or trajectories with large Euclidean distances between
the digitizing steps in n-space.
The inverse of the absolute value of the step size between successive values
of De(t) = |A2(t)−A2(t−1)| provides a scalar index of the order parameter. Indeed,
small steps in Euclidean distances, De(t) (higher spikes in figure 1) indicate pattern
amplitude stability.
0.85
Stepsize, Eculidian distance
0.8
0.75
0.7
0.65
0.6
0.55
0.5
0.45
0.4
0.35
0
10
20
30
40
50
60
70
80
Entropy Variation w.r.t. time
90
100
Fig 8.1 The sharp spikes [gray, De(t)] show the rate of change in spatial AM pattern.
8.5.2 The thermal connection
In the dissipative model, the free energy functional for the aκ modes is given by



1 
Fa ≡ N 0(t ) |  H a − Sa  | 0(t ) N,
β 



…8.1
with time-dependent inverse temperature β(t) = 1/(kβT(t)). Sa is the entropy operator
given by
124
{
Sa ≡ −∑ a 'k a k in sinh 2 Θ k − a k a 'k in cosh 2 Θk
}
…8.2
The time evolution of the state |0(t))N at finite volume V can be shown to be
controlled by the entropy variations, which reflects the irreversibility of timeevolution (breakdown of time-reversal symmetry) characteristic of dissipative
systems.
The dissipation thermal free energy enhances because of dissipative phase
homeostasis which is seen both with human body and human brain till the normal
equilibrium is reached (Fig.8. 2).
Fig 8.2 Thermal energy dissipation performance characteristic of entropy variation
with time. This corresponds to the choice of a privileged direction in time-evolution
called arrow of time.
8.5.3 Classicality and attractor landscapes
One of the merits of the dissipative many-Human body model is the
possibility of deriving from the microscopic dynamics the classicality of the
trajectories representing the time evolution of the state |0(t)_N in the Human body
state space. These trajectories are found to be deterministic chaotic trajectories. This
is a particularly welcome feature of the model since observed changes in the order
parameter become susceptible to be described in terms of trajectories on attractor
landscapes. One can show these trajectories are classical and that:
(i) They are bounded and do not intersect themselves (trajectories are not periodic);
125
(ii) There are no intersections between trajectories specified by different initial
conditions;
(iii) Trajectories of different initial conditions diverge.
Although property (ii) implies that no confusion or interference arises among
different memories, even as time evolves, states with different N labels may have
non-zero overlap (non-vanishing inner products) in realistic situations of finite
volume. This means that some association of memories becomes possible: at a
‘crossing’ point between two, or more than two, trajectories, one can ‘switch’ from
one of them to another one. This reminds us of the ‘mental switch’ occurring during
particular perceptual and motor tasks as well as during free associations in memory
tasks.
The deterministic chaotic motion described by (i)–(iii) takes place in the
space of the parameters labeling the system ground state. It is low dimensional and
noise-free. In a more realistic framework, the motion must be conceived as highdimensional, noisy, engaged and time varying. Nevertheless, it is remarkable that, at
the present stage of the work, the dissipative model predicts that the system
trajectories through its physical phases may be chaotic and itinerant through a chain
of ‘attractor ruins’ embedded in a set of attractor landscapes accessed serially or
merely approached in the coordinated dynamics of a meta stable state.
8.6
CONCLUSIONS
Our work in this paper leads us to conclude that the dissipative quantum
model of Human body predicts two main features observed in neuropsychological
data i.e. human brain model: the coexistence of physically distinct AM patterns
correlated with categories of conditioned stimuli and the remarkably rapid onset of
AM patterns into irreversible sequences that resemble cinematographic frames. Each
spatial AM pattern is described to be consequent to the spontaneous breakdown of
symmetry triggered by an external stimulus and is associated with one of the
unitarily in equivalent ground states of QFT. Their sequencing is associated with the
non-unitary time evolution implied by dissipation, to attain thermal homeostasis in
human brain with regards to human body.
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CHAPTER 9
AUTOMATED BONE MARROW DIAGNOSIS USING
ADVANCED IMAGE PROCESSING APPLICATIONS-A
REVIEW
9.1
INTRODUCTION
9.1.1 Digital image processing fundamentals
Image processing is a rapidly growing area of computer science. Its growth
has been fueled by technological advances in digital imaging, computer processors
and mass storage devices. Fields which traditionally used analog imaging are now
switching o digital systems, for their exibility and affordability. Important examples
are medicine, image and video production, document storage and archival,
photography, remote sensing, and security monitoring. These and other sources
produce huge volumes of digital image data every day, more than could ever be
examined manually.
What is digital image processing?
An image may be defined as a 2-D function f(x, y), where x and y are spatial
coordinates, and the amplitude of f at any coordinate (x, y) is called the intensity or
grey level of the image at that point. When x, y and the amplitude values of f are all
finite, discrete quantities (e.g. 0 or 1 for a black and white image), we call the image
a digital image.
The field of digital image processing refers to processing digital images by
means of digital computer. A digital image is composed of a finite numbers of
elements in an array, each of which has a particular location and value within that
array. These elements are called pixels.
There are no clear cut boundaries in the continuum from image processing at
one end to computer vision at the other. However, on useful paradigm is to consider
127
three types of computerized processes in this continuum; low-, mid-, and high-level
processes.
•
Low-level processes involve simple operations such as image processing to
reduce noise, contrast enhancement, and image sharpening. A low-level process
is characterized by the fact that both its inputs and outputs are images.
•
Mid-level processing on images involves tasks such as segmentation, description
of those objects to reduce them to a form suitable for computer processing, and
classification of individual objects. A mid-level process is characterized by the
fact that its inputs are generally images, but its outputs are attributes extracted
from those images.
•
Finally, higher-level processing involves “making sense’ of a collection of
recognized objects, as in image analysis, and at the far end of the continuum,
performing the cognitive functions normally associated with vision.
Image processing applications
As an illustration of image processing in the visual spectrum, consider a
thumb print. Images of fingerprints are routinely processed by computer, either to
enhance them or to find features that aid in the automated search of a database for
potential matches. Another example is the imaging of paper currency; applications of
digital image processing in this area include automated counting and, in law
enforcement, the reading of serial number for the purpose of tracking and identifying
bills. It is also possible to use digital image processing for automated licence plate
reading. The light rectangles in the picture represent the area in which the imaging
system detected the plate. The black rectangles show the results of automated reading
of the plate content by the system. Licence plate and other applications of character
recognition are used extensively for traffic monitoring and surveillance.
Other examples include following:
Image processing
─ Pattern recognition
─ Robotic vision
─ Image enhancement
128
─ Facsimile
─ Satellite weather map
─ Animation
Instrumentation/Control
─ Spectrum analysis
─ Position and rate control
─ Noise reduction
─ Data compression
Speech/Audio
─ Speech recognition
─ Speech synthesis
─ Text to speech
─ Digital audio
─ Equalization
Military
─ Secure communication
─ Radar processing
─ Sonar processing
─ Missile guidance
Telecommunication
─ Echo cancellation
─ Adaptive equalization
─ ADPCM transcoders
─ Spread spectrum
─ Video conferencing
─ Data communication
Biomedical
─ Patient monitoring
─ Scanners
129
─ EEG brain mappers
─ ECG analysis
─ X-ray storage/enhancement
─ MRI, fMRI
─ Ultrasound imaging
Although extensive, this list is far from complete in the uses of digital signal
processing and just serves to show the importance of DSP in everyday life.
Fundamentals steps in digital image processing
The fundamental steps in digital image processing involve many stages:
image acquisition, enhancement, restoration, colour image processing, wavelets,
compression,
morphological
processing,
segmentation,
representation
description as well as recognition.
Wavelet and
Multiresolution
processing
Compression
Morphological
Processing
Colour Image
Processing
Segmentation
Image
Restoration
KnowledgeBase
Problem
Domain
Image
Enhancement
Representation
And
Description
Image
Acquisition
Object
Recognition
Fig. 9.1 Fundamental steps in digital image processing
and
130
Image acquisition
This is the first process in digital signal processing and acquisition could be
as simple as being given an image that is already in digital form. Generally, the
image acquisition stage involves pre-processing, such as scaling.
Image enhancement
This is among the simplest and most appealing areas of digital image
processing. Basically, the idea behind enhancement techniques is to bring out detail
that is obscured, or simply to highlight certain features of interest in an image.
Image restoration
Image restoration is an area that also deals with improving the appearance of
an image. However, unlike enhancement, which is subjective, image restoration is
objective, in the sense that restoration techniques tend to be based on mathematical
or probabilistic models of image degradation. Enhancement on the other hand, is
based on human subjective preferences regarding what constitutes a “good”
enhancement result.
Wavelelets
These are the foundation for representing images in various degrees of
resolution. In particular, this can be used for image data compression and for
pyramidal representation, in which images are subdivided successively into smaller
regions.
Compression
This step deals with techniques for reducing the storage required to save an
image, or the bandwidth required to transmit it. Although storage technology has
improved significantly over the past decade, the same cannot be said for transmission
capability. This is true particularly in uses of the internet, which is characterized by
significant pictorial content.
131
Morphological processing
Morphological processing deals with tools for extracting image components
that are useful in representation and description of shape.
Segmentation
Segmentation procedures partition an image into its constituent parts or
objects. In general, autonomous segmentation is one of the most difficult task in
digital image processing. A rugged segmentation procedure brings the process a long
way towards successful solution of imaging problems that require objects to be
identified individually. On the other hand, weak or erratic segmentation algorithm,
almost always guarantee eventual failure. In general, the more accurate the
segmentation, the more likely recognition is succeed.
Representation and description
Representation and description almost always follows the output of a
segmentation stage, which usually is raw pixel data, constituting either a boundary of
a region or all the pints in the region itself. In either case, converting the data to form
suitable for computer processing is necessary. The first decision that must be made is
whether the data should be represented as a boundary or as a complete region.
Boundary representation is appropriate when the focus region is on external shape
characteristics, such as corners and inflections. Regional representation is appropriate
when the focus region is on internal properties such as texture or skeletal shape. In
some applications, these representations complement each other. Choosing a
representation is only part of the solution for transforming raw data into a form
suitable for subsequent computer processing. A method must also be specified for
describing the data so that features of interest are highlighted. Description, also
called feature selection, deals with extracting attributes that result in some
quantitative information of interest or are basic for differentiating one class of
objects from another.
132
9.1.2 Basics of Bone marrow cancer
Bone marrow is the spongy, soft tissue contained inside most bones. This
tissue is made up of immature cells, which are known as stem cells, which develop
into various types of blood cells such as: red blood cells, which help in carrying
oxygen throughout the body; white blood cells, which are part of the immune system
and therefore help in fighting infection; and platelets, which help in clotting blood.
As red blood cell production requires iron, the body usually stores a large portion of
its supply of iron in the bone marrow. Bone marrow cancer occurs when the cells
that form blood become cancerous.
Cancer that begins in the bone is called primary bone cancer. Primary bone
cancer is relatively uncommon in comparison with secondary or metastatic cancer
(cancer that occurs initially in another organ and then spreads to bone tissue). The
bones in the body serve several purposes. They support and protect internal organs
(for example, the skull protects the brain and the ribs protect the lungs). Muscles pull
against the bones to make the body move. Bone marrow (the soft, spongy tissue in
the center of many bones) makes and stores blood cells.
Primary bone cancer is found most often in the arms and legs, but it can occur
in any bone in the body. Children and young people are more likely than adults to
have bone cancer. Primary bone cancers are called often called sarcomas. There are
several types of sarcoma. Each type begins in a different kind of bone tissue. The
most
common
are
osteosarcoma,
Ewing's
sarcoma
and
chondrosarcoma.
Osteosarcoma is the most common type of bone cancer in young people. It usually
occurs between the ages of 10 and 25. Males are affected more often than females.
Osteosarcoma often starts in the ends of bones, where new bone tissue forms as a
young person grows. It usually affects the long bones of the arms or legs.
Acute leukemia is a disease of the leukocytes and their precursors. It is
characterized by the appearance of immature, abnormal cells in the bone marrow and
peripheral blood and frequently in the liver, spleen, lymph nodes, and other
parenchymatous organs. The paper presents the preprocessing methods of the
133
leukemic blast cells image in order to generate the features well characterizing
different types of cells. The solved problems include: the segmentation of the bone
marrow aspirate by applying the watershed transformation, selection of individual
cells, feature generation on the basis of texture, statistical and geometrical analysis of
the cells.
Leukemia is a cancer that begins in the bone marrow. It is caused by an
excessive production of immature leucocytes that replace normal blood cells
(leukocytes, red blood cells, and platelets). It causes the body to be exposed to many
diseases with no possibility to fight them for lack of defense.
Without proper treatment, cancer is the cause of many deaths. Based on the
statistics it is been realized that it is the fifth and sixth cause of death among men and
women with this cancer.
Leukemia is curable if it is detected and treated at early stage. Its detection
starts with a complete blood count. If there are abnormalities in this count, a study of
morphological bone marrow smear analysis is done to confirm the presence of
leukemic cells. In this study, a pathologist observes some cells under a light
microscope looking for abnormalities present in the nucleus or cytoplasm of the cells
in order to classify the abnormal cells in their particular types and subtypes of
leukemia.
This classification is very important as it determines which treatment is
given. This study has an error rate between 30% and 40% depending on the
pathologist experience and the difficulty to distinguish leukemia types and subtypes.
A flow cytometry test is highly accurate to classify leukemia’s but it is very
expensive and not all the hospitals have the equipment to perform it. The
classification of leukemia types and subtypes facilitate the physicians’ work in
deciding what treatment is the best or a given cell type (lymphocytic or
myelogenous) and disease progress (acute or chronic). This paper presents a
preprocessing method of the leukemic blast cells image in order to generate the
134
features well characterizing different types of cells. The recognition of the blast cells
in the bone marrow of the patients suffering from myelogenous leukemia is a very
important step in the recognition of the development stage of the illness and proper
treatment of the patients [H. Hengen-2002, K. Lewandowski-2001, W. Wolberg1994]. The percentage of blasts is a major factor at defining various subtypes of
acute myeloid leukemia. According to French-American-British (FAB) standard, 8
acute leukemia types are classified on the basis of the ratio of myelo/monoblasts, the
number of erythroid precursors or non-erythroid cells as well as megacarioblasts
cells. It is known that proper treatment of leukemia requires not only recognition of
different stages of the development of the blasts but also calculation of their quantity
in the aspirated bone marrow.
9.2
TYPES OF CELLS
There exist many different cell types in the bone marrow. The most known
and recognized abnormal cells include monoblasts, promonocytes, monocytes,
myeloblasts,
promyelocytes,
myelocytes,
metamyelocytes,
proerythroblasts,
basophilic erythroblasts, polychromatic erythroblasts, orthochromatic erythroblasts,
lymphocytes, plasmocytes, megacaryoblasts, megakaryocytic, etc [H. Hengen2002,K. Lewandowski-2001]. The variety of cells occurring in the bone marrow
demands a high expertise of the analyst, which is usually verbal one. For improving
the reliability of the analysis and diagnosis, computer based digital image processing
offers a useful tool. This paper is dedicated to the task of feature generation for the
automatic blast cell recognition. The well-defined features should suppress the
differences among the cells belonging to the same class and amplify them for cells
belonging to different classes. The presented solution may be treated as the first step
in building up an automatic system able to recognize different blood cells.
9.3
MEDICAL IMAGE PROCESSING
9.3.1 Medical Imaging
In the clinical context, medical imaging is generally equated to radiology or
"clinical imaging" and the medical practitioner responsible for interpreting (and
sometimes acquiring) the images are a radiologist. Diagnostic radiography designates
135
the technical aspects of medical imaging and in particular the acquisition of medical
images. The radiographer or radiologic technologist is usually responsible for
acquiring medical images of diagnostic quality, although some radiological
interventions are performed by radiologists. While radiology is an evaluation of
anatomy, nuclear medicine provides functional assessment.
As a field of scientific investigation, medical imaging constitutes a subdiscipline of biomedical engineering, medical physics or medicine depending on the
context: Research and development in the area of instrumentation, image acquisition
(e.g. radiography), modeling and quantification are usually the preserve of
biomedical engineering, medical physics and computer science; Research into the
application and interpretation of medical images is usually the preserve of radiology
and the medical sub-discipline relevant to medical condition or area of medical
science (neuroscience, cardiology, psychiatry, psychology, etc.) under investigation.
Many of the techniques developed for medical imaging also have scientific and
industrial applications.
9.3.2 Image Segmentation
Segmenting the nucleus and cytoplasm of leukocytes from bone marrow
images is a very difficult task, as the images show heterogeneous staining and highcell population as shown in figure9.2
Fig 9.2 The exemplary image of the bone marrow smear of the acute leukemia
patient containing different blast cells.
Some segmentation techniques such as thresholding, edge detection, pixel
clustering, and growing regions have been combined to extract the nucleus and
cytoplasm of leukocytes. These techniques could be applied as the images show
136
uniform backgrounds and high contrast that appropriately defines the objects of
interest. In this paper an approach is been proposed in which a segmentation
algorithm based on color and texture of pixels features is performed that can work in
bone marrow images showing heterogeneous staining.
9.4
PROPOSED METHODS
The task of segmentation of the image is focused on the automatic
recognition and separation of each cell for further processing, in order to obtain
stable features, useful in recognition of the cell.
9.4.1 Cell Segmentation
Three important steps is involved in segmenting the image given as:
1. Segmentation of cellular elements,
2. Identification of nucleus and cytoplasm, and
3. Separation of overlapped blood cells.
Morphological operations are used in solving the segmentation. The
morphological operations aim at extracting relevant structures of the image by
probing the image with another set of a known shape called structuring element,
chosen as the result of prior knowledge concerning the geometry of the relevant and
irrelevant image structures. The most known morphological operations include
erosion, dilation, opening and closing [P. Soile-2003]. The morphological approach
to image segmentation combines regions growing and edge detection techniques. It
groups the pixels around the regional minima of the image. The boundaries of
adjacent grouping are precisely located along the crest lines of the gradient image. In
our experiments, watershed transformation operation is performed to obtain the
result. The watershed transformation [O. Lezoray-2002,P. Soile-2003] takes its
origin from the topographic interpretation of the gray scale image. According to the
law of gravitation, the water dropped on such surface will flow down until reaches a
minimum. The whole set of points of the surface, whose steepest slope paths reach a
given minimum, constitutes the catchment’s basis associated with this minimum. The
137
watersheds are the zones dividing adjacent catchment’s basins. In numerical
implementation of the watershed algorithm the original image is transformed so, as
to output an image whose minima mark relevant image objects and whose crest lines
correspond to image object boundaries. In this way the image is partitioned into
meaningful regions that may correspond for example to the individual blast cells. In
our experiments, Watershed algorithm is used for implementation using Mat lab
platform [Image processing toolbox, Math Works, Natick, 1999]. The applied
procedure of the image segmentation and cell separation consists of the following
stages:
• Transformation of the original image into gray scale.
• Transformation of the gray image to binary one by applying the biased
segmentation.
• Application of closing and erosion operations to smooth the contours and to
eliminate the distortions.
• Generation of the map of distances from the black pixel to the nearest white pixels.
• Application of the watershed algorithm for the image segmentation.
Fig 9.3 The segmented image of the bone marrow smear.
9.4.2 Futures Extraction
Applying decomposition model on image, as a texture analysis is used, to
separate texture into its structural and stochastic components. The blood cells images
present heterogeneous textures, hence both periodical and random textures can be
found in such images. Additional motivations for choosing this model were its
similarity relation with the human visual perception system, and its invariant
properties to translation, rotation, and scale.
138
The word decomposition model interprets the image texture by means of the
sum of three mutually orthogonal components, a harmonic field, a generalized
evanescent field, and a stochastic field. The perceptual characteristics of these fields
can as periodicity, directionality, and randomness, respectively, according to the
three most important human perception dimensions.
9.4.3 The geometrical features
The important information is contained in the geometrical shapes and
parameters [9] associated with them. Various cells differ greatly with the size. For
example the orthochromatic erythroblasts have the size of 8-10 micrometer, while
megakcariocyte may be up to 100 micrometer. The shapes of different blasts are
either round, oval or kidney-shaped. Following geometrical features of the cells is
been considered.
•
Radius –measured by averaging the length of the radial line segments defined
by the centroid and border points
•
Perimeter - the total distance between consecutive points of the border
•
The ratio of the perimeter and radius
•
Area – the number of pixels on the interior of the cell, defined separately for
the nuclei and for the whole cell; as the features we assume the area of the
nucleus and the ratio of the areas of the nucleus and the whole cell
•
The area of convex part of the nucleus
•
Compactness – given by the formula: perimeter2/area
•
Concavity – the severity of concavities in a cell
•
Concavity points – the number of concavities, irrespective of their amplitudes
•
Symmetry – the difference between lines perpendicular to the major axis to
the cell boundary in both directions
•
Major and minor axis lengths.
9.4.4 Statistical Features Process
The next set of features has been generated on the basis of the intensity
distribution of the image. The histograms and gradient matrices of such intensity
have been determined for three color components R, G and B. On the basis of this
139
the following features have been generated: the mean value and variance of the
histogram and the gradient matrix of the image of the nucleus and the whole cell (24
features), skew ness and kurtosis of the histogram and gradient matrix of the whole
cell (12 features). All these features have been calculated for three colors. 36 features
have been generated in this way. All numerical experiments of feature generation
have been implemented on the platform of Matlab [Image processing toolbox, Math
Works, Natick, 1999].
9.4.5 Identification Process
From the regions obtained in the segmentation process, by analyzing their
shape, color, and special relation with respect to other regions to determine whether
and analyzed region is a nucleus or a leukocyte.
The features that were used to recognize cellular elements are: circularity to
measure the perimeter complexity of a circular object (circularity= perimeter2/
(4πarea)), eccentricity to find out how much the object deviates from being circular
(eccentricity = dist (center, focus)), color to determine if a region is darker than
other, and containment proportion to establish whether a region contains or is
contained by another region.
9.4.6 Classification Process
The suitable recognition of leukemia cells requires the definition of good
descriptive features that facilitate their classification. In this phase geometric feature
is been extracted, and statistical, texture, and size ratio features from regions
obtained in the segmentation process (nucleus, cytoplasm, and whole cell) and
analyzed these features to identify types and subtypes of acute leukemia.
140
9.5
RESULTS AND OBSERVATIONS
Fig 9.4 The distribution of cell locations in plane formed by 11th Markov feature
corresponding to cytoplasm and nucleus.
Fig 9.5 The distribution of cell locations in the plane formed by 2 geometrical
features.
9.6
CONCLUSIONS
This chapter has presented the image processing approach to the recognition
and classification of the leukemia cells. The most important points of this approach
are: segmentation of the image of bone marrow aspirate using watershed algorithm,
the extraction of individual cells from the image, automatic generation of different
features of the cell, assessment of the feature quality of the cells using analysis of
distribution, correlation and principal component analysis, application of support
vector machine for final recognition and classification of cells.
141
CHAPTER 10
CONCLUSION AND SCOPE FOR FUTURE WORK
10.1 CONCLUSION
The physiological dissipative homeostat linked with a control system
incorporates negative feedback as it requires proper coordination with the
transduction phase for initiation of entropy regulation in every living subject
irrespective of its origin. In this respect the iron homeostasis of E.coli linked with the
dissipative phase of iron present in the environment is discussed with the mentioning
of appropriate references. It is to be understood that the model of the iron
homeostasis is very important to prevent cellular damage because of excess iron
present in the extra cellular fluid. And in this respect it is interesting to note the role
played by E.coli in inhibiting excess iron storage in the cellular environment.
In the present research work a novel concept of electrostrictive energy linked
with capacitance relaxation phenomenon has been incorporated during the stages of
the metastasis. The electrostrictive energy with the population of metastasis declines
in steps and this phenomenon can be explained by with the help of quantum
mechanical approaches. The metastasis once initiated it becomes linked with the
Capacitance Relaxation Phenomena and it can be coordinated with the simulated
E.coli environment. With the application of drug the metastasis can temporarily be
arrested depending on the antioxidant/oxidant ratio during the process of
phosphoralization which is found to be dissipative in nature. This phenomenon has
been analyzed with Artificial Neural Network (ANN) in an elaborative way so as to
assess the impact of the drug. And in this respect the application of cancer diseases in
the E.coli environment is to be encouraged for apoptosis of cancer cell.
142
It is to be noted that the dissipative model of E.coli is also linked with the
hydrophobicity in respect of coupling of protein with polar head during the
transcription phase of mRNA nucleotide sequence.
10.2 SCOPE OF FUTURE WORK
The research carried out in E.coli environment is a very upcoming and
challenging to the scientist for exploring new drug with the simulated E.coli
environment for initiation of apoptosis in the crucial stages of cancer and in this
respect the present work is very significant to explore the possibility of a novel drug
design.
143
REFERENCES
1.
A Whybro, H Jagger, M Barker and R Eastell; Phosphate supplementation in
young men: lack of effect on calcium homeostasis and bone turnover, Nature,
Volume 52, Number 1, Pages 29-33, January 1998
2.
Abdul-Tehrani,H., Hudson,A.J., Chang,Y.S., Timms,A.R., Hawkins,C.,
Williams,J.M., Harrison,P.M., Guest,J.R. and Andrews,S.C. (1999) Ferritin
mutants of Escherichia coli are iron deficient and growth impaired, and fur
mutants are iron deficient. J. Bacteriol., 181, 1415–1428.
3.
Adey W R 1981 Physiol. Rev. 61 435
4.
Adhya,S. (2003) Suboperonic regulatory signals. Sci. STKE, 2003, pe22.
5.
Alex J. Brown, Adriana Dusso, and Eduardo Slatopolsky; Vitamin D, Renal
Physiology, Vol.277, F157-F175,1999
6.
Amit D J 1989 Modeling Human body Function: The World of Attractor
Neural Networks (Cambridge: Cambridge University Press)
7.
Andrews,S.C., Robinson,A.K. and Rodriguez-Quinones,F. (2003) Bacterial
iron homeostasis. FEMS Microbiol. Rev., 27, 215–237.
8.
Ann M O'Hara, Fergus Shanahan The gut flora as a forgotten organ. EMBO
reports 7, 688 - 693 (1 July 2006)
9.
Ashby, W. R. Vvedenie v kibernetiku. Moscow, 1959. In Problemy
inzhenernoi psikhologii, vol. 3, part 2. Moscow, 1968.
10.
Awayda
M.S., Van Driessche W., Helman S.I. Frequency – Dependent
capacitance of the apical membrane of frog skin: Dielectric Relaxation
processes. Biophysics J, January 1999, P 219-232, Vol. 76, No. 1
11.
Azanza M J and del Moral A 1994 Prog. Neurobiol. 44 517
12.
B. Djavan, M. Remzi, A. Zlotta, C. Seitz, P. Snow, and M. Marberger, (Feb
2002), “Novel Artificial Neural Network for Early Detection of Prostate
Cancer”
144
13.
Bach-y-Rita P 1995 Nonsynaptic Diffusion Neurotransmission and Late
Human body Reorganization (New York: Demos-Vermande)
14.
Bach-y-Rita P 2004 Ann. N Y Acad. Sci. 1013 83 Bach-y-Rita P 2005 J.
Integr. Neurosci.
15.
Bagg,A. and Neilands,J.B. (1987) Ferric uptake regulation protein acts as a
repressor, employing iron (II) as a cofactor to bind the operator of an iron
transport operon in Escherichia coli. Biochemistry, 26, 5471–5477.
16.
Basak T. K., (2007)Electrical Engineering Materials, New Age International
(India),.
17.
Basak T. K., Electrical Engineering Materials, New Age International (India),
2007.
18.
Basak T. K., Ramanujam T., Halder S., Cyrilraj V., Ravi T., Kulshreshtha
Prachi Mohan. pH homeostasis and cell signaling pathway reflected in
capacitance relaxation phenomena, Int. J. Medical Engineering and
Informatics, Vol. 1, No. 1, 2008, pp. 85-90.
19.
Basak T. K., Ramanujam T., Halder S., Cyrilraj V., Ravi T., Kulshreshtha
Prachi Mohan.(2008) pH homeostasis and cell signaling pathway reflected in
capacitance relaxation phenomena, Int. J. Medical Engineering and
Informatics, Vol. 1, No. 1, pp. 85-90.
20.
Basak Tapas K, Halder Suman, Kumar Madona, Sharma Renu and Midya
Bijoylaxmi. A topological model of biofeedback based on catecholamine
interactions. Theor Biol Med Model., March 21,2005. doi: 10.1186/1742-4682-2-11
21.
Basak TK, Aich NS: Photo induction on fish-anabas testudineus, Proceedings
of IEEE EMBS International Conference, vol-XII, no.4, pg-1635-1636,
March 1990.
22.
Basak Tk, Bhattacharya K, Halder S, Murugappan S, Raj Vc, Ravi T,
Gunasekaran G, Shaw P (2009). Modeling of capacitance relaxation
phenomena in a malignant membrane ; Modelling in Medicine and Biology
VIII, Edited by CA Brebbia WIT Transactions on Biomedicine and Health
Vol 13 WIT. Pp. 247-256
145
23.
Basak TK, Dutta J: pH dependence of the interactions in blood pressure
transduction, proceeding of IEEE EMBS International conference, 0-78032050, pg.852-853, June 1994
24.
Basak TK, Dutta JC, Ghosh SK: Some optical characteristics of
biomembrane in the development of biosensors, International society for
optical engineering SPIE, USA, vol.1407, 2001
25.
Basak TK, Halder S, Kumar M, Sharma R, Bijoylaxmi M. (2005). A
topological model of biofeedback based on catechalamine interaction, Theor.
Biol. Med. Biol. PubMed PMCID:PMC1079950.
26.
Basak TK, Islam R: Role of sensory hormones in estimation of blood
pressure transduction, Proceedings of IEEE EMBS International Conference,
0-7803-1377, pg. 1590-1591, January 1993
27.
Basak TK, Ramanujam T, Halder S, Cyrilraj V, Ravi T, Kulshrestha PM
(2008). pH homeostasis and cell signalling pathway reflected in Capacitance
Relaxation phenomena. Int. J. Med. Engg. Inform.1
28.
Basak TK,Ghosh Nc (2006). Capacitance Relaxation phenomenon in
cartilaginous membrane ; Every Man’s Science. Vol. XL No 6.
29.
Basak TK: Catecholamine interaction in blood pressure transduction,
conference on Medimechatronics, Malaga, Spain organized by Bristol
University UK, 1992
30.
Basak TK: Entropy transduction on photoinduction, Proceedings of IEEE
EMBS International Conference, vol.13, no.4, pg. 1534-1535, April 1991.
31.
Basak TK: pH dependent transduction in renal function regulation,
proceeding of 18th Annual International conference of the IEEE EMBS, no.
0-7803-3811, pg. 2227-2228 Amsterdam 1996.
32.
Bassett D S, Meyer-Lindenberg A, Achard S, Duke T and Bullmore E 2006
PNAS 103 19518
33.
Bauminger,E.R., Cohen,S.G., Dickson,D.P., Levy,A., Ofer,S. and Yariv,J.
(1980) Mossbauer spectroscopy of Escherichia coli and its iron-storage
protein. Biochim. Biophys. Acta, 623, 237–242.
34.
Bhagavan, N. V. (2002). Medical biochemistry (4th ed.). Academic Press.
pp. 499.ISBN 9780120954407.
146
35.
Bishop,R. (1997) Iron metabolism in Escherichia coli. M.Phil. Thesis,
University of Sheffield, Sheffield, UK.
36.
Bojana B. Belesslin- Cokic, Xiaobing Yu, Babette B.
Weksler, Alan
N.Schechter, and Constance Tom Noguchi. Erythropoietin and hypoxia
stimulated erythropoietin receptor and nitric oxide production by endothelial
cells: DOI 10.1182/blood-2004-02-0744.
37.
C.C.Capen; Calcium – Regulating Hormones and Metabolic Bone Disease;
International Veterinary Information Service, 1985
38.
Canadian erythropoietin study group: . Association between recombinant
human erythropoietin and quality of life and exercise capacity of
patients receiving hemodialysis . Br Med J 1990 ; 300 : 573 – 578.
39.
Cannon WB. Organization For Physiological Homeostasis. Physiol Rev.
1929; 9: 399-431.
40.
Cannon WB. The Wisdom of the Body. 1932. W.W. Norton & Company,
Inc., New York.
41.
Coster, H.G.L., and Smith J.R., The molecular organization of bimolecular
lipid membranes. A study of the low frequency Maxwell-Wagner impedance
dispersion. Biochim. Biophys. Acta. 1974, 373: 151-154, [Medline]
42.
Covert,M.W. and Palsson,B.O. (2002) Transcriptional regulation in
constraints-based metabolic models of Escherichia coli. J. Biol. Chem., 277,
28058–28064.
43.
D. F. Brougham, G. Ivanova, M. Gottschalk, D. M. Collins, A. J. Eustace, R.
O'Connor, and J. Havel (July 2010) Artificial Neural Networks for
Classification in Metabolomic Studies of Whole Cells Using 1H Nuclear
Magnetic Resonance
44.
Daniel V.V., Dielectric Relaxation, 1967, Academic Press, London,.
45.
David Goltzman; Approach to hypercalcemia: Endotext.com, April 2002
46.
Debanjan
C,Chandrani
S,Biswarup
B,Partha
SD,Sujit
B
(2009).
Catecholamines Regulate Tumor Angiogenesis. Cancer Res
47.
Delany,I., Rappuoli,R. and Scarlato,V. (2004) Fur functions as an activator
and as a repressor of putative virulence genes in Neisseria meningitidis. Mol.
Microbiol., 52, 1081–1090.
147
48.
Delany,I., Spohn,G., Rappuoli,R. and Scarlato,V. (2001) The Furrepressor
controls transcription of iron-activated and -repressed genes in Helicobacter
pylori. Mol. Microbiol., 42, 1297–1309.
49.
Dinel J., Hicklin Lee ,M. Ellis Roll of the Vascular Endothelial Growth
Factor Pathway in Tumar Growth and Angiogenesis, Journal of Clinical
Oncology, vol 23, No 5 (February 10),2005: pp. 1011-1027.
50.
Djaman,O., Outten,F.W. and Imlay,J.A. (2004) Repair of oxidized iron-sulfur
clusters in Escherichia coli. J. Biol. Chem., 279, 44590–44599.
51.
Dunn J R, Fuller M, Zoeger J, Dobson J, Heller F, Hammann J, Caine E and
Moskowitz B M 1995 Human body Res. Bull. 36 149
52.
Eckardt K-U . Erythropoietin : oxygen dependent control of erythropoiesis
and its failure in renal diseases . Nephron 1994 ; 67: 7- 23.
53.
Ephram Nwoye, Li C. Khor, Satnam S. Dlay and Wai L. Woo (2006) A
Novel Fast Fuzzy Neural Network Backpropagation Algorithm for Colon
Cancer Cell Image Discrimination
54.
Ernst,J.F., Bennett,R.L. and Rothfield,L.I. (1978) Constitutive expression of
the iron-enterochelin and ferrichrome uptake systems in a mutant strain of
Salmonella typhimurium. J. Bacteriol., 135, 928–934.
55.
Eschbach JW . Egrie JC , Dowing MR et al . Correction of the anemia of
end stage renal disease with recombinant human erythropoietin . N Engl J
Med 1987 ; 316 : 73 – 78.
56.
F. Hamadi1, H. Latrache1*, A. El Ghmari, M. El Louali, M. Mabrrouki, N.
Kouider ,Effect of pH and ionic strength on hydrophobicity and electron
donor and acceptor characteristics of Escherichia coli and Staphylococcus
aureus, Annals of Microbiology, 54 (2), 213-225 (2004).
57.
Fasciotto Bh, Trauss Ca, Greeley Gh, Cohn DV (1993). Parastatin (porcine
chromogranin A347-419), a novel chromogranin A-derived peptide, inhibits
parathyroid cell secretion. Endocrinol. 133: 461-466
58.
Feng C, Li Hz, Yan Wg, Luo Yf, Cao Jl (2005). The expression and
significance of chromogranin A and synaptophysin in adrenal gland tumors.
Zhonghua Zhong Liu Za Zhi 27:486-488
148
59.
Fidler IJ (2003) The pathogenesis of cancer metastasis: the ‘seed and soil’
hypothesis revisited (Timeline). Nat Rev Cancer 3: 453-458.
60.
Langley RR, Ramirez KM, Tsan RZ, Van Arsdall M, Nilsson MB, Fidler IJ
(2003) Tissue-specific microvascular endothelial cell lines from H-2k b-
tsA58 mice for studies of angiogenesis and metastasis. Cancer Res 63: 29712976.
61.
Kim SJ, Uehara H, Yazici S, He J, Langley RR, Mathew P, Fan D, Fidler IJ
(2005) Modulation of bone microenvironment with Zoledronate enhances the
therapeutic effects of STI571 and Paclitaxel against experimental bone
metastasis of human prostate cancer. Cancer Res 65: 3707-3715.
62.
Frazzon,J. and Dean,D.R. (2003) Formation of iron–sulfur clusters in
bacteria: an emerging field in bioinorganic chemistry. Curr. Opin.Chem.
Biol., 7, 166–173.
63.
Freeman W J 1975 Mass Action in the Nervous System (New York:
Academic) (Reprinted 2004)
64.
Freeman W J 2004 Clin. Neurophysiol. 115 2077
65.
Freeman W J 2004 Clin. Neurophysiol. 115 2089
66.
Freeman W J 2005 Clin. Neurophysiol. 116 1118
67.
Freeman W J 2005 J. Integr. Neurosci
68.
Freeman W J 2006 Clin. Neurophysiol. 117 572
69.
Freeman W J and Rogers L J 2003 Intern. J. Bifurcation Chaos 13 2867
70.
Freeman W J and Vitiello G 2006 Phys. Life Rev. 3 93 (Preprint qbio.OT/0511037)
71.
Freeman W J, Burke B C and Holmes M D 2003 Human Human body Mapp.
19 248
72.
Freeman W J, Burke B C, Holmes M D and Vanhatalo S 2003 Clin.
Neurophysiol. 114 1055
73.
Freeman W J, Ga´al G and Jornten R 2003 Intern. J. Bifurcation Chaos 13
2845
74.
Frey N , Mckinsey , T. A , and Olson , E.N. 2000a. Decoding calcium
signals involved In cardiac growth and function . Nat . Med .6: 12211227.
149
75.
G. Dennis SprottS, Kathleen M. Shaw, and Ken F. Jarrellj, (1985)
Methanogenesis and the K+ Transport System Are Activated by Divalent
Cations in Ammonia-treated Cells of Methanospirillurn Hungatei, The
Journal of Biological Chemistry, Vol. 260, No. 16, Issue 5, pp. 9244-9250.
76.
G. Dennis SprottS, Kathleen M. Shaw, and Ken F. Jarrellj, Methanogenesis
and the K+ Transport System Are Activated by Divalent Cations in
Ammonia-treated Cells of Methanospirillurn Hungatei, The Journal of
Biological Chemistry, Vol. 260, No. 16, Issue 5, 1985, pp. 9244-9250.
77.
Gallo Mp, Levi R, Ramella R, Brero A, Boero O, Tota B, Alloatti G
(2007).Endothelium-derived nitric oxide mediates the antiadrenergic effect of
humanvasostatin-1 in rat ventricular myocardium. Am. J. Physiol. Heart Circ.
Physiol. 292:H2906-H2912
78.
Glenn M. Chertow, Steven K. Burke, Maureen A. Dillon, Eduardo
Slatopolsky: Long-term effects of sevelamer hydrochloride on the calcium x
phosphate product and lipid profile of haemodialysis patients, Nephrol Dial
Transplant (1999) 14: 2907-2914
79.
Glilozzi A, Basak Tk (1986). Organization and dynamics of bipolar lipids
from sufobus solfatricus in bulk phases and, omolayer membranes,Syst. Appl.
Microbiol. 7:266-27
80.
Gliozzi A, Bruno S, Basak TK, Rosa MD, Gambacorta A: Organization and
Dynamics of Bipolar Lipids from Sulfobus Solfataricus in Bulk Phases and in
Monolayer Membranes. System Appl Microbiol 1986, 7:266-27
81.
Gliozzi, A., Bruno, S., Basak, T. K., Rosa, M. D. and Gambacorta,
Organization and dynamics of bipolar lipids from sulfobus solfataricus in
bulk phases and in monolayer membranes, System Appl. Microbiol., Vol. 7,
1986, pp. 266–27.
82.
Gliozzi, A., Bruno, S., Basak, T. K., Rosa, M. D. and Gambacorta, (1986)
Organization and dynamics of bipolar lipids from sulfobus solfataricus in
bulk phases and in monolayer membranes, System Appl. Microbiol., Vol. 7,
pp. 266–27.
83.
Gliozzi, A., Bruno, S., Basak, T. K., Rosa, M. D. and Gambacorta,
Organization and dynamics of bipolar lipids from sulfobus solfataricus in
150
bulk phases and in monolayer membranes, System Appl. Microbiol.,Vol. 7,
1986, pp. 266–27.
84.
Gorr. Thomas A., Cahn Joshua D., Yamagata Hideo and Bunn H. Franklin:
Hypoxia-induced synthesis of Hemoglobin in the Crustacean Daphnia magna
Is Hypoxia-inducible Factor-dependent, J. Biol. Chem., Vol.279, Issue 34,
36038-36047, August 20, 2004
85.
Guillemin Karen and Krasnow Mark A.: The Hypoxic Response: Huffing and
HIFing, Cell, Vol. 89, 9-12, April 4, 1997
86.
Guzman-Casado M, Parody-Morreale A, Robic S, Marqusee S, SanchezRuiz JM,Energetic evidence for formation of a pH-dependent hydrophobic
cluster in the denatured state of Thermus thermophilus ribonuclease H., , J.
Mol Biol. 2003 Jun 13;329(4):731-43.
87.
H Mercedes Guzman-Casado, Antonio Parody-Morreale1Srebrenka Robic,
Susan Marqusee and Jose M. Sanchez-Ruiz, Energetic Evidence for
Formation of a pH-dependent Hydrophobic Cluster in the Denatured State of
Thermus thermophilus Ribonuclease, doi:10.1016/S0022-2836(03)00513-8
J. Mol. Biol. (2003) 329, 731–743.
88.
H. Hengen, S. Spoor, M. Pandit, “Analysis of blood & bone marrow smears,
SPIE Med. Imag., San Diego, 2002
89.
Hamed,M.Y. (1993) Binding of the ferric uptake regulation repressor protein
(Fur) to Mn(II), Fe(II), Co(II), and Cu(II) ions as co-repressors: electronic
absorption, equilibrium, and 57Fe Mossbauer studies. J. Inorg. Biochem., 50,
193–210.
90.
Hantke,K. (1981) Regulation of ferric iron transport in Escherichia coli K12:
isolation of a constitutive mutant. Mol. Gen. Genet., 182, 288–292.
91.
Hantke,K. (2001) Iron and metal regulation in bacteria. Curr. Opin.
Microbiol., 4, 172–177.
92.
Hartwell,L.H., Hopfield,J.J., Leibler,S. and Murray,A.W. (1999) From
molecular to modular cell biology. Nature, 402, C47–C52.
93.
Helman S.I., Thompson S.M, Interpretation and use of electrical equivalent
circuits in studies of epithelial tissues, Am. J. Physiol. 1982, 243:F519-F531,
[Medline]
151
94.
Herrgard,M.J., Covert,M.W. and Palsson,B.O. (2004) Reconstruction of
microbial transcriptional regulatory networks. Curr. Opin. Biotechnol., 15,
70–77.
95.
Himmetoglu Solen, Dincer Yildiz, Ersoy Yeliz E, Bayraktar Baris, Celik
Varol, Akcay Tulay, DNA Oxidation and Antioxidant Status in Breast
Cancer, Journal of Investigative Medicine, August 2009, Volume 57, Issue 6,
pp. 720-723.
96.
Himmetoglu Solen, Dincer Yildiz, Ersoy Yeliz E, Bayraktar Baris, Celik
Varol, Akcay Tulay, (August 2009) DNA Oxidation and Antioxidant Status
in Breast Cancer, Journal of Investigative Medicine,Volume 57, Issue 6, pp.
720-723.
97.
Holloway,A.J., van Laar,R.K., Tothill,R.W. and Bowtell,D.D. (2002) Options
available—from start to finish—for obtaining data from DNA microarrays II.
Nature Genet., 32 (Suppl), 481–489.
98.
Hye-Youn Cho, Sekhar P. Reddy Andrea DeBiase, Masayuki Yamamoto and
Steven R. Kleeberger Gene expression profiling of NRF2-mediated
protection against oxidative injury: Free Radical Biology and Medicine:
Volume 8, issue 3, 1 February 2005, Pages 325-343
99.
Hyunjung N Kim , Scotta .Bradford , and Sharonl. Walker, Escherichia coli
O157:H7 Transport in Saturated Porous Media: Role of Solution Chemistry
and Surface Macromolecules, , Environ. Sci. Technol. 2009, 43, 4340–4347
100.
J K crane, MS wehner, EJ Bolen, JJ Sando, J Linden, R L Guerrant and CL
Sears, regulation of intestinal guanylate Cyclase by heat –stable enterotoxin
of Escherichia Coli(STA) and protein Kinase C, journal of Infection and
Immunity, dec1992,60(12):5004-5012.
101.
J. Li and L. A. McLandsborough ,The effects of the surface charge and
hydrophobicity of Escherichia coli on its adhesion to beef muscle ,
International Journal of Food Microbiology, Volume 53, Issues 2-3, 15
December 1999, Pages 185-193 .
102.
Jing Zhou, Nan Hu, Ya-Lin Wu, Yuan-jiang Pan, Cuirong Sun. (2008)
Preliminary studies on the chemical characterization and antioxidant
152
properties of acidic polysaccharides from Sargassum fusiforme, Journal of
Zhejiang University SCIENCE B, 9, 9, pp. 721-727.
103.
Jing Zhou, Nan Hu, Ya-Lin Wu, Yuan-jiang Pan, Cuirong Sun. Preliminary
studies on the chemical characterization and antioxidant properties of acidic
polysaccharides from Sargassum fusiforme, Journal of Zhejiang University
SCIENCE B, 9, 9, 2008, pp. 721-727.
104.
K. Lewandowski, A. Hellmann, “Hematology atlas”, Multimedia Medical
Publisher, Gdansk, 2001
105.
Karl Ludwig von Bertalanffy: ... aber vom Menschen wissen wir nichts,
(English title: Robots, Men and Minds), translated by Dr. Hans-Joachim
Flechtner. page 115. Econ Verlag GmbH (1970), Düsseldorf, Wien. 1st
edition.
106.
Kell D.B., and Harris C.M., On the dielectrically observable consequences of
the diffusional motions of lipids and membranes. I. Theory and overview.
Eur. Biophysics J. 1985, 12: 181-197 (Medline)
107.
Keyer,K. and Imlay,J.A. (1996) Superoxide accelerates DNA damage by
elevating free-iron levels. Proc. Natl Acad. Sci. USA, 93, 13635–13640.
108.
Kim SJ, Uehara H, Yazici S, Busby JE, He J, Maya M, Logothetis CJ,
Mathew P, Wang X, Do KA, Fan D, Fidler IJ (2006) Targeting plateletderived growth factor receptor on endothelial cells of multidrug resistant
prostate cancer. J Natl Cancer Inst 98: 783-793.
109.
Kim T, Tao-Cheng Jh, Eiden Le, Loh Yp (2001). Chromogranin A, an
“on/off” switch controlling dense-core secretory granule biogenesis. Cell 106:
499-509.
110.
Krishna,S., Andersson,A.M., Semsey,S. and Sneppen,K. (2006) Structure and
function of negative feedback loops at the interface of genetic and metabolic
networks. Nucleic Acids Res., 34, 2455–2462.
111.
Krizsan-Agbas D, Zhang R, Marzban F, Smith PG: Presynaptic adrenergic
facilitation of parasympathetic neurotransmission in sympathectomized rat
smooth muscle. J Physiol 1998, 512:841-849.
153
112.
L. Seedlings. S. Tajdoost, T. Farboodnia and R. Heidary Amiloride
inhibition of Vacuolar Na+/H+ enhance salt stress in Zea mays, Pakistan
Journal of Biological Sciences 10(12):2020-2024, 2007 ISSN 1028-8880 .
113.
Lando David, Gorman Jeffrey J., Whitelaw Murray L. and Peet Daniel J.:
Oxygen dependent regulation of hypoxia-inducible factors by prolyl and
asparaginyl hydroxylation, Eur. J. Biochem. 270, 781-790 (2003)
114.
Lashley K 1948 The Mechanism of Vision, XVIII, Effects of Destroying the
Visual ‘Associative Areas’ of the Monkey (Provincetown, MA: Journal
Press)
115.
Leslie Cromwell, Fred J. Weibell, Erich A. Pfeiffer, “Biomedical
Instrumentation and Measurements” published by Prentice Hall of India in
2002.
116.
Liew SP: Monitoring galvanic skin responses in functional magnetic
resonance imaging. Ph.D. thesis. Queensland University, Brisbane; 2001.
117.
Macdougall
IC , Lewis
NP , Saunders
MJ
et
al . Long - term
cardiorespiratory effects of Amelioration of renal anemia by erythropoietin
. Lancet 1990 ;335 : 489- 493 .
118.
Madhavan R. Buddha, Tao Tao, Ronald J. Parry, and Brian R. Crane.
Regioselective Nitration of Tryptophan by a Complex between Bacterial
Nitric-oxide Synthase and Tryptophanyl tRNA Synthetase, The Journal of
Biological Chemistry, Vol. 279, No. 48, 2004, pp. 49567–49570.
119.
Madhavan R. Budha, Kim M. Keery and Brian R. Crane, An unusual
tryptophanyl tRNA synthetase interacts with nitric oxide synthase in
Deinoccocus radiodurans, PNAS, Vol. 101, No. 45, 2004, pp. 15881-86.
120.
Mahapatra NR, O'connor DT, Vaingankar SM, Sinha Hikim, Mahatam AP,
Ray S, Staite E, Wu H, Gu Y, Dalton N, Kennedy BP, Zeigler MG, Ross J Jr,
Mahata SK (2005). Hypertension from targeted ablation of chromogranin A
can be rescued by the human ortholog. J. Clin. Invest. 115: 1942-1952, 2005.
121.
Marialuisa Sensi, Gabriella Nicolini, Marina Zanon, Chiara Colombo,
Alessandra Molla, Ilaria Bersani, Raffaella Lupetti, Giorgio Parmiani, and
Andrea Anichini1, Immunogenicity without Immunoselection: A Mutant but
Functional Antioxidant Enzyme Retained in a Human Metastatic Melanoma
154
and Targeted by CD8+ T Cells with a Memory Phenotype, Cancer Res, 65, 2,
2005.
122.
Marialuisa Sensi, Gabriella Nicolini, Marina Zanon, Chiara Colombo,
Alessandra Molla, Ilaria Bersani, Raffaella Lupetti, Giorgio Parmiani, and
Andrea Anichini1, (2005).Immunogenicity without Immunoselection: A
Mutant but Functional Antioxidant Enzyme Retained in a Human Metastatic
Melanoma and Targeted by CD8+ T Cells with a Memory Phenotype, Cancer
Res, 65, 2,
123.
Marika Crohns, Seppo Saarelainen, Hannu Kankaanranta, Eeva Moilanen,
Hannu Alho, and Pirkko Kellokumpu-Lehtinen, Local and systemic
oxidant/antioxidant status before and during lung cancer radiotherapy, Free
Radical Research, 43, 7, 2009, pp. 646-57.
124.
Marika Crohns, Seppo Saarelainen, Hannu Kankaanranta, Eeva Moilanen,
Hannu Alho, and Pirkko Kellokumpu-Lehtinen, (2009) Local and systemic
oxidant/antioxidant status before and during lung cancer radiotherapy, Free
Radical Research, 43, 7, pp. 646-57.
125.
Masse´,E. and Arguin,M. (2005) Ironing out the problem: new mechanisms
of iron homeostasis. Trends Biochem. Sci., 30, 462–468.
126.
Masse´,E. and Gottesman,S. (2002) A small RNA regulates the expression of
genes involved in iron metabolism in Escherichia coli. Proc. Natl Acad. Sci.
USA., 99, 4620–4625.
127.
Masse´,E., Escorcia,F.E. and Gottesman,S. (2003) Coupled degradation of a
small regulatory RNA and its mRNA targets in Escherichia coli. Genes. Dev.,
17, 2374–2383.
128.
Masse´,E., Vanderpool,C.K. and Gottesman,S. (2005) Effect of RyhB small
RNA on global iron use in Escherichia coli. J. Bacteriol., 187, 6962–6971.
129.
Matlab user manual – Image processing toolbox, Math Works, Natick, 1999
130.
McHugh,J.P., Rodriguez-Quinones,F., Abdul-Tehrani,H.,Svistunenko,D.A.,
Poole,R.K., Cooper,C.E. and Andrews,S.C. (2003) Global iron-dependent
gene regulation in Escherichia coli. A new mechanism for iron homeostasis.
J. Biol. Chem., 278, 29478–29486.
155
131.
Mckinsey , T. A , Zhang , C. L and Olson , E. N 2002 : MEF 2: A calcium –
dependent regulator of cell division , differentiation and death . Trends
Biochem . Sci. 27: 40 – 47.
132.
Mills,S.A. and Marletta,M.A. (2005) Metal binding characteristics and role of
iron oxidation in the ferric uptake regulator from Escherichia coli.
Biochemistry, 44, 13553–13559.
133.
Moftaquir-Handaj A, Barbé F, Barbarino-Monnier P, Aunis D, Boutroy MJ.
(1995). Circulating ChromograninA and Cathacolamines in Human Fetuses at
Uneventful Birth. Pediatr. Res. 37(1)
134.
Morrow A. L, Suzdak P D and Paul S M 1988 Adv. Biochem.
Psychopharmacol. 45 247
135.
Mosley Ca, Taupenot L, Biswas N, Taulane JP, Olson NH, Vaingankar Sm,
Wen G, Schork NJ, Ziegler MG, Mahata SK, O'connor DT (2007).Biogenesis
of the secretory granule: chromogranin a coiledcoil structure results in
unusual physical properties and suggests a mechanism for granule
corecondensation. Biochem. 46:10999-11012
136.
Neil J Schroeder and John Cunninghum: What’s new in vitamin D for the
nephrologist?; Nephrol Dial Transplant (2000) 15: 460-466
137.
Ni Z, Wang XQ, Vaziri ND: Nitric oxide metabolism in erythropoietininduced
hypertension: Effect of calcium channel blockade. Hypertension
32: 724-729, 1998
138.
Nissenson AR , Besarab A , Bolton WK et al . Target haematocrit during
erythropoietin therapy . Nephrol Dial Transplant 1997 ; 12 : 1813 -1816 .
139.
Nunoshiba,T., Obata,F., Boss,A.C., Oikawa,S., Mori,T., Kawanishi,S. and
Yamamoto,K. (1999) Role of iron and superoxide for generation of hydroxyl
radical, oxidative DNA lesions, and mutagenesis in Escherichia coli. J. Biol.
Chem., 274, 34832–34837.
140.
O. L. Mangasarian, P. Lagrangian, “Support Vector Machines”, Journal of
Machine Learning, 161-177, 2001
141.
O. Lezoray, H. Cardot, “Cooperation of color pixel classification schemes
and color watershed”, IEEE Trans. Image Processing, vol. 11, pp. 783-789,
2002
156
142.
Operon, Is Phosphorylated on a Histidine Residue, Journal of Bacteriology,
Vol. 179, No. 17, Sept. 1997,
143.
Orna Amster-Choder and Andrew Wright, BglG, the Response Regulator of
the Escherichia coli bgl Operon, Is Phosphorylated on a Histidine Residue,
Journal of Bacteriology, Vol. 179, No. 17, Sept. 1997, pp. 5621–5624.
144.
Orna Amster-Choder and Andrew Wright, BglG, the Response Regulator of
the Escherichia coli bgl
145.
Outten,F.W., Djaman,O. and Storz,G. (2004) A suf operon requirement for
Fe–S cluster assembly during iron starvation in Escherichia coli. Mol.
Microbiol., 52, 861–872.
146.
P. Soile, “Morphological image analysis, principles and applications”,
Springer, Berlin, 2003
147.
Park,S. and Imlay,J.A. (2003) High levels of intracellular cysteine promote
oxidative DNA damage by driving the fenton reaction. J. Bacteriol., 185,
1942–1950.
148.
Patzer,S.I. and Hantke,K. (1999) SufS is a NifS-like protein, and SufD is
necessary for stability of the [2Fe-2S] FhuF protein in Escherichia coli. J.
Bacteriol., 181, 3307–3309.
149.
Pething R., Dielectric and electronic properties of biological materials. 1979,
John Willey & Sons, New York,. pp. 5621–5624.
150.
Pribram K H 1971 Languages of the Human body (Engelwood Cliffs, NJ:
Prentice-Hall) Pribram K H 1991 Human body and Perception (Hillsdale, NJ:
Lawrence Erlbaum Associates Publ.)
151.
Priya C. Kadam and David R. Boone. (February 2010) Influence of pH on
Ammonia Accumulation and Toxicity in Halophilic, Methylotrophic
Methanogens, Applied and Environmental Microbiology, Vol. 62, No. 12,
1996, p. 4486–4492. Sensors & Transducers Journal, Vol. 113, Issue 2, pp.
158-166 166
152.
Puig,S., Askeland,E. and Thiele,D.J. (2005) Coordinated remodeling of
cellular metabolism during iron deficiency through targeted mRNA
degradation. Cell, 120, 99–110. 4966 Nucleic Acids Research, 2006, Vol. 34,
157
No. 17 Downloaded from nar.oxfordjournals.org by guest on December 12,
2010
153.
Quackenbush,J. (2002) Microarray data normalization and transformation.
Nature Genet., 32 (Suppl), 496–501.
154.
R.N.G. Naguib, and F.C. Hamdy, (1997), “Prognostic neuroclassification of
prostate cancer patients”
155.
Raine AEG , Roger SD , Effect of erythropoietin on Blood pressure . Am
J Kid Dis 1991; 18 : 76-83.
156.
Rao F, Wen G, Gayen Jr, Das M, Vaingankar Sm, Rana Bk, Mahata M,
Kennedy Bp, Salem Rm, Stridsberg M, Abel K, Smith Dw, Eskin E, Schork
Nj,
Hamilton
Ba,
Ziegler
Mg,
Mahata
Sk,
O'connor
DT
(2007).Catecholamine release-inhibitory peptide catestatin (chromogranin
A(352-372)):naturally occurring amino acid variant Gly364Ser causes
profound changes in human autonomic activity and alters risk for
hypertension. Circulation 115: 2271-2281.
157.
Resnick LM: Cellular calcium and magnesium metabolism in the
pathophysiology and treatment of hypertension and related metabolic
disorders. Am J Med 93:S11-S20 (suppl 2A), 1992
158.
Ricciardi L M and Umezawa H 1967 Kibernetik 4 44
159.
Roland P E 1993 Human body Activation (New York: Wiley-Liss)
160.
S I Girgis CPD: Extra – cellular calcium homeostasis ; Role of the
calcium sensing – receptor in health and disease ,.Clinical Biochemistry
2004 ; 6(1) : 21- 23.
161.
S. Haykin, “Neural networks, comprehensive foundation”, Prentice Hall,
New Jersey, 1999
162.
S. Tajdoost, T. Farboodnia and R. Heidary (2007). Amiloride inhibition of
Vacuolar Na+/H+ enhance salt stress in Zea mays L. Seedlings., Pakistan
Journal of Biological Sciences, 10, 12, pp. 2020-2024.
163.
S. Tajdoost, T. Farboodnia and R. Heidary. Amiloride inhibition of Vacuolar
Na+/H+ enhance salt stress in Zea mays L. Seedlings., Pakistan Journal of
Biological Sciences, 10, 12, 2007, pp. 2020-2024.
158
164.
Sara B. Cullinan and J. Alan Diehl PERK-dependent Activation of Nrf2
Contributes to Redox Homeostasis and Cell Survival following Endoplasmic
Reticulum Stress J. Biol. Chem., Vol. 279, Issue 19, 20108-20117, May 7,
2004
165.
Schillen T B and K¨onig P 1994 Biol. Cybern. 70 397
166.
Schwan H.P.
Electrical properties of tissues and cell suspensions. In
Advances in Biological and Medical Physics, 1957, p.147-209 Academic
Press, New York,
167.
Schwartz,C.J., Giel,J.L., Patschkowski,T., Luther,C., Ruzicka,F.J., Beinert,H.
and Kiley,P.J. (2001) IscR, an Fe–S cluster-containing transcription factor,
represses expression of Escherichia coli genes encoding Fe–S cluster
assembly proteins. Proc. Natl. Acad. Sci. USA., 98, 14895–14900.
168.
Semsey,S., Virnik,K. and Adhya,S. (2006) Three-stage regulation of the
amphibolic gal operon: from repressosome to GalR-free DNA. J Mol Biol.,
358, 355–363.
169.
Shaw P., Basak T.K. & Ghosh N.C., Novel Method for Detecting
Malignancies in Membrane, Science & Culture, No.5-6, May – June 2006
170.
Shaw P., Basak T.K., Ghosh N.C.. Capacitance Relaxation Phenomena in
Cartilaginous Membrane Everyman’s Science, Vol.XL No. 6,February’06March’06
171.
Shen-Orr,S.S., Milo,R., Mangan,S. and Alon,U. (2002) Network motifs in the
transcriptional regulation network of Escherichia coli. Nature Genet., 31, 64–
68.
172.
Shivamurthy B., Basak T. K., Prabhuswamy M. S., Siddaramaiah, Tripathi
Himangshu, Deopa S. S. Influence of Quartz Fillers in Dielectric Composites
on Electrostrictive, Sensors & Transducer, Vol. 92, Issue 5, May 2008, pp.
32-42.
173.
Shivamurthy B., Basak T. K., Prabhuswamy M. S., Siddaramaiah, Tripathi
Himangshu, Deopa (May 2008) S. S. Influence of Quartz Fillers in Dielectric
Composites on Electrostrictive, Sensors & Transducer, Vol. 92, Issue 5, pp.
32-42.
159
174.
Silberberg J z, Racine M , Barre P et al . Regression of Left Ventricular
Hypertrophy
in dialysis patients
following
correction of anemia with
recombinant human erythropoietin. Can J Cardiol 1990 ; 6 : 1-4.
175.
Soitamo Arto J., Rabergh Christina M.I., Gassmann Max, Sistonen Lea, and
Nikinmaa Mikko: Characterization of a Hypoxia-inducible Factor (HIF-1α)
from Rainbow Trout, The journal of Biological Chemistry, Vol. 276, No. 23,
Issue of June 8, pp. 19699-19705, 2001
176.
Stefano Franceschini, Pierpaolo Ceci, Flaminia Alaleona, Emilia Chiancone
and Andrea Ilari. Antioxidant Dps protein from the thermophilic
cyanobacterium Thermosynechococcus elongates An intrinsically stable
cage-like structure endowed with enhanced stability, FEBS Journal, 273,
2006, pp. 4913–4928.
177.
Stefano Franceschini, Pierpaolo Ceci, Flaminia Alaleona, Emilia Chiancone
and Andrea Ilari (2006). Antioxidant Dps protein from the thermophilic
cyanobacterium Thermosynechococcus elongates An intrinsically stable
cage-like structure endowed with enhanced stability, FEBS Journal, 273, pp.
4913–4928.
178.
Tapas K. Basak, Suman Halder, Madona Kumar, Renu Sharma and
Bijoylaxmi Midya, A topological model of biofeedback based on
catecholamine interactions, Theoretical Biology and Medical Modelling, 2,
2005, p. 11.
179.
T. K. Basak, T. Ramanujam, J. C. Kavitha, Poonam Goyal, Deepali Garg,
Arpita Gupta, Suman Halder. pH Homeostasis Linked with Capacitance
Relaxation Phenomena and Electrostrictive Energy in Cancer Cells, Sensors
& Transducers Journal, Vol. 109, Issue 10, October 2009, pp. 147-153.
180.
T. K. Basak, T. Ramanujam, V. Cyrilraj, G. Gunshekharan, Asha Khanna,
Deepali Garg, Poonam Goyal, Arpita Gupta, pH Homeostasis of a Biosensor
in Renal Function Regulation Linked with UTI, Sensors & Transducers, Vol.
105, Issue 6, June 2009, pp. 127-134.
181.
T. Wagner, “Texture analysis” (in Jahne, B., Haussecker, H., and Geisser P.,
(Eds.), Handbook of Computer Vision and Application), Academic Press, pp.
275-309, 1999
160
182.
T.K. Basak , Guangyue, Shi, Guanjie Sui, Jianxia Jiang (2010) Role of
catecholamine in tumor angiogenesis linked to capacitance relaxation
phenomenon Journal of Medicine and Medical Science Vol. 1(8) pp. 336-340
183.
T.K.Basak, “Electrical Engg Materials” published by New Age publishers,
India in 2007.
184.
T.K.Basak, K.Bhattacharya, S. Halder, S.Murugappan, V.Cyril Raj, T. Ravi,
G. Gunasekaran and P. Shaw: Modelling of capacitance relaxation
Phenomena in malignant membrane: W.I.T International conference, New
Forest, U.K.
185.
Tapas Kumar Basak, T. Ramanujam, Suman Halder, Poonam Goyal, Prachi,
Mohan Kulshreshtha, Shweta Pandey, Himanshu Tripathi. Electrostrictive
Effect in Cancer Cell Reflected in Capacitance Relaxation Phenomena,
Sensors & Transducers, Vol. 99, Issue 12, December 2008, pp. 90-101.
186.
Tarvainen M, Koistinen A, Valkonen-Korhonen M, Partanen J, Karjalainen
P: Principal component analysis of galvanic skin responses. IEEE Trans
Biomed Eng, in press, 2001. Guyton & Hall: Textbook of Medical Physiology:
Elsevier; 2003.
187.
Taylor Cv, Taupenot L, Mahata Sk, Mahata M, Wu H, Yasothornsrikul S,
Toneff T, Caporale C, Jiang Q, Parmer Rj, Hook Vy, O'connor Dt (2000).
Formation of the catecholamine release-inhibitory peptide catestatin from
chromogranin A. Determination of proteolytic cleavage sites in hormone
storage granules. J. Biol. Chem. 275:22905-22915
188.
Terzuolo C A and Bullock T H 1961 Proc. Natl Acad. Sci. USA 42 687
189.
Thulasiraman,P., Newton,S.M., Xu,J., Raymond,K.N., Mai,C., Hall,A.,
Montague,M.A. and Klebba,P.E. (1998) Selectivity of ferric enterobactin
binding and cooperativity of transport in gram-negative bacteria. J. Bacteriol.,
180, 6689–6696.
190.
Tian H, Habecker B, Guidry G, Gurtan A, Rios M, Roffler-Tarlov S, Landis
SC: Catecholamines Are Required for the Acquisition of Secretory
Responsiveness by Sweat Glands, J Neurosci, 2000, 20:7362-7369.
191.
Truyen Nguyen, Philip J. Sherratt, H.-C. Huang, Chung S. Yang, and Cecil B.
Pickett :Increased Protein Stability as a Mechanism That Enhances Nrf2-
161
mediated Transcriptional Activation of the Antioxidant Response Element
Degradation Of Nrf2 By The 26 S Proteasome. J. Biol. Chem., Vol. 278,
Issue 7, 4536-4541, February 14, 2003
192.
V. Vapnik, “Statistical Learning Theory”, Wiley, N.Y., 1998
193.
vakhnenko, A. G. Tekhnicheskaia kibernetika: Sistemy avto-maticheskogo
upravleniia s prisposobleniem kharakteristik, 2nd ed. Kiev, 1962.
194.
van Harreveld A and Khattab F I 1968 Anat. Rec. 162 467
195.
Varela F, Lachaux J-P, Rodriguez E and Marinerie J 2002 Nat. Rev.
Neurosci. 2 229
196.
Vassinova,N. and Kozyrev,D. (2000) A method for direct cloning of furregulated genes: identification of seven new fur-regulated loci in Escherichia
coli. Microbiology, 146, 3171–3182.
197.
Vincent Escuyer, Patrice Boquet, David Perrin, Cesare Montecucco , and
Michele Mock, A pH-induced Increase in Hydrophobicity as a Possible Step
in the Penetration of Colicin E3 through Bacterial Membranes, , THE
JOURNAL OF BIOLOGICAL CHEMISTRY, 0 1986 by The American Society
of Biological Chemists, Inc. Vol. 261, No, , 23, Issue of August 15, pp. 1089110898,1986
198.
Vitiello G 1995 Int. J. Mod. Phys. B 9 973
199.
Vitiello G 2001 My Double Unveiled (Amsterdam: John Benjamins)
200.
W. B. Cannon. ‘‘Physiological regulation of normal states: some tentative
postulates concerning biological homeostatics.’’ IN: A. Pettit (ed.). A Charles
Richet: ses anims, ses collegues, ses elves, p. 91. Paris: editions Medicales,
1926.
201.
W. Gnadt, D. Manolakis, E. Feleppa, F. Lizzi, T. Liu, P. Lee,( 5, July 1999)
“Classification of prostate tissue using neural networks”
202.
W. Wolberg, W. N. Street, O. L. Mangasarian, “Machine learning to diagnose
breast cancer from image-processed features”, Rep. of Uni. Wisconsin, 1994
203.
Walker M M and Bitterman M E 1989 J. Exp. Biol. 145 489
204.
Weihua Z, Tsan R, Schroit AJ, Fidler IJ (2005) Apoptotic cells initiate
endothelial cell sprouting via electrostatic signaling. Cancer Res 65: 1152911535
162
205.
Wenger Ronald H.: Cellular adaptation to hypoxia: O2-sensing protein
hydroxylases, hypoxia-inducible transcription factors, and O2-regulated gene
expression, The FASEB Journal. 2002; 16, 1151-1162
206.
Wilderman,P.J., Sowa,N.A., FitzGerald,D.J., FitzGerald,P.C., Gottesman,S.,
Ochsner,U.A. and Vasil,M.L. (2004) Identification of tandem duplicate
regulatory small RNAs in Pseudomonas aeruginosa involved in iron
homeostasis. Proc. Natl Acad. Sci. USA, 101, 9792–9797.
207.
Wintrobe, Thorn, Adams, Braunwald, Isselbacher and Petersdorf: Principles
of Internal Medicine: McGraw-Hill Kogakusha Ltd.; 1972.
208.
Wizemann V , Kanfmann J , Kramer W . Effect of erythropoietin on
ischemia tolerance in anemic hemodialysis patients with confirmed coronary
artery disease . Nephron 1992 ;62 : 161- 165 .
209.
Wu Hj, Rozansky DJ, Parmer RJ, Gill BM, O'connor DT (1991).Structure
and function of the chromogranin A gene: clues to evolution and tissuespecific expression. J. Biol. Chem. 266: 13130- 13134, 1991.
210.
yatt, James K.; Ritz-De Cecco, Angela; Czeisler, Charles A.; Dijk, Derk-Jan
(1 October 1999)."Circadian temperature and melatonin rhythms, sleep, and
neurobehavioral function in humans living on a 20-h day". Am J
Physiol 277 (4): R1152–R1163. Fulltext. PMID 10516257. Retrieved 200711-25. "... significant homeostatic and circadian modulation of sleep
structure, with the highest sleep efficiency occurring in sleep episodes
bracketing the melatonin maximum and core body temperature minimum".
211.
Yokoi K, Sasaki T, Bucana CD, Fan D, Baker CH, Kitadai Y, Kuwai T,
Abbruzzese JL, Fidler IJ (2005) Simultaneous inhibition of EGFR, VEGFR,
and PDGFR signaling combined with Gemcitabine produces therapy of
human pancreatic carcinoma and prolongs survival in an orthotopic nude
mouse model. Cancer Res 65: 10371-10380
212.
Zheng,M., Doan,B., Schneider,T.D. and Storz,G. (1999) OxyR and SoxRS
regulation of fur. J. Bacteriol., 181, 4639–4643.
213.
Zuofa Z., Hang, Jie Jin and Liangen Shi, Antioxidant Activity of the
Derivatives of Polysaccharide Extracted from a Chinese Medical Herb.
Ramulus mori, Food Sci. Technol. Res., 14, 2, 2008, pp. 160-168.
163
CURRICULUM VITAE
NAME
:
SUDHEER PATIL
DESIGNATION
:
SENIOR GRADE LECTURER
ADDRESS
:
RESIDENCE
C/o Annaraogowda Patil,
H.No. 11-1478, 49 “KASTURI”,
Beside mini theater, Near central bus stand,
Vidya nagar, Gulbarga – 585103.
Telephone:
Mobile +919241602091
+919845570075
Home 08472253096,
+919060901011
DATE OF BIRTH
:
01.06.1970
EDUCATIONAL QUALIFICAITONS:
Qualification
Institution
University
M.Tech
(Information
Tech.)
AAAIU,
Allahabad
B.E
(Instrumentation
Tech.)
PDA
Collage of
Eng.,
Gulbarga.
Work Experience
:
Year
Class
Allahabad
University
2006
First Class
with
Distinction
Gulbarga
University,
Gulbaraga.
1992
First Class
with
Distinction
18 years of Teaching
164
AUTHOR’S PUBLICATIONS
Journal Publications
1. “A Model Linked to E.coli Related to Electrostrictive Energy in Cancer
Cells”, International Journal of Sensors and Transducers, vol.113, issue 2,
February-2010, pp.158-166.
2. “A Model of Antiporter Linked to Hydrophobicity of E.coli”, Reviewed
and Selected for Publication in international journal “African Journal of
Microbiology”.
3. “ANN analysis of E.coli Environment Related to the Stages of Cancer”,
Reviewed and Selected for Publication in international journal “Journal of
Medicine and Medical-Sciences (JMMS)”.
Conference Publications
1. “Automated bone marrow diagnosis using advanced image processing
applications”, selected and published in proceedings of International
Conference at REC, Bhalki, Karnataka, 29-30 october-2010, pp.346-349.
2. “Research Design” a paper related to academic research presented and
published in proceedings of “Issues of Excellence in Academi Research
in the National conference at Dr. M.G.R. University, Chennai, August 1314, 2010, pp. 327-330.
3. “Analysis of human brain dynamics in coherence with human body with
regards to dissipative phase/model of thermal energy”, selected and
published in proceedings of International Conference on Systemics,
Cybernetics and Informatics, ICSCI-2011, January 05-08, 2011, pp 97100.
4. “A topological control system model of dissipative phenomena w.r.t. iron
homeostasis linked to E.coli environment”, communicated to international
conference on “Biomedical and Pharmaceutical Engineering”, ICBPE2011 at Singapore.