CHAPTER-5
Building an Algorithm-Hybrid Approach and Validation
CHAPTER 5
Building an Algorithm-Hybrid
Approach and Validation
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CHAPTER-5
Building an Algorithm-Hybrid Approach and Validation
5.1 Introduction
A cross breed calculation was intended to overcome any and all hardships between the two
methodologies. A repetitive arrangement of model building and speculation driven reproduction
procedures were employed. Each fixation has been recovered from distributed literature.
Information is mapped for each biochemical response and its parameters from its source into Cell
Designer. To align parameters by fitting them to an arrangement of test perceptions COPASI was
utilized for running simulations. Metabolic requirements influencing different conditions were
mulled over. A broad investigation was performed for the accessible in vivo convergences of
metabolites in people through which seven distinct conditions as shown in table 5.1 chose were
tried for assessment of the model. The primary parameter that was tried was the glycaemic
condition where fixation qualities were utilized for hypoglycaemic and hyperglycaemic
conditions and varieties were watched. The parameter to be tried was the starvation/fasting
conditions and the variety in the vitality bends were seen. The regulation of vitality by the
creatine phosphate stands very much illustrated. Creatine phosphate is known for its vitality
support capacity. In the muscle and the mind, creatine phosphate goes about as a fast supply for
ATPs and along these lines a reversible response catalysed by phosphocreatine kinase even
restores the energized state to a store of ADPs. In addition, the impacts on the metabolic
regulation amid the time of activity were investigated. Also to check the workability of HEPNet,
we examined two ailment conditions, which are because of metabolic annoyances in particular
uremia and dihydrolipoamide dehydrogenase insufficiency (DLDD). The premise of uptake of
diverse parameters was to mull over the impact on the focal vitality pool under different
physiological and sickness conditions. Likewise the physiological conditions of heftiness,
starvation and fasting have been actualized.
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Sl.
Condition
Building an Algorithm-Hybrid Approach and Validation
Reaction ID
Reaction Details
No.
1
Mitochondrial β
Re 110-112
oxidation
Re110: C22Acyl-CoA + FAD -> "C22 2-trans-enoyl-CoA" + FADH2;
"Acyl-CoA
dehydrogenase"
Re111: "C22 2-trans-enoyl-CoA" + H2O{mitochondria} -> "C22 L-3-hydroxyacyl-CoA";
"Enoyl-CoA hydratase"
Re112: "C22 L-3-hydroxyacyl-CoA" + NAD+{mitochondria} -> "C22 Ketoacyl-CoA" +
NADH{mitochondria} + H+{mitochondria}; "Beta-hydroxyacyl-CoA dehydrogenase"
2
DLDD
Re 18, 19, 23, 98
Re 18: Alpha-KG + CoA-SH{mitochondria} + NAD+{mitochondria} -> "S CoA" +
CO2{mitochondria}
+
NADH{mitochondria}
+
H+{mitochondria};
"Alpha-KG
dehydrgenase complex"
Re 19: "S CoA" + GDP{mitochondria} = Succinate + ATP{mitochondria} + CoASH{mitochondria}; "S CoA synthase"
Re 23: Isocitrate + NAD+{mitochondria} -> Alpha-KG + CO2{mitochondria} +
NADH{mitochondria} + H+{mitochondria}; "Isocitrate dehydrogenase"
Re 98: Acetoacetate + "S CoA" ->AcetoacetylCoA + Succinate;
"Beta-KetoacylCoA
dehydrogenase"
3
Glycemia
Re 25, 32, 56, 80,
85, 87
Re 25: Glucose + ATP{default} -> G6P + ADP{default}; Hexokinase
Re 32: G6P + H20 -> Glucose + Pi{default}; "G-6-P Phosphatase"
Re 56: "Limit Dextrin" -> "Unbranched alpha(1,4)polymer" + Glucose;
Glycosidase
Re 80: Sucrose + H2O{default} -> Glucose + Fructose; Sucrase
Re 85: Lactate + NADH{default} -> Glucose + NAD+{default}
Re 87: Trehalose + H2O{default} -> Glucose; Trehalase
4
Starvation
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Re 25, 32, 56, 80,
Re 25: Glucose + ATP{default} -> G6P + ADP{default}; Hexokinase
Alpha1,6-
CHAPTER-5
Building an Algorithm-Hybrid Approach and Validation
85, 87
Re 32: G6P + H20 -> Glucose + Pi{default}; "G-6-P Phosphatase"
Re 56: "Limit Dextrin" -> "Unbranched alpha(1,4)polymer" + Glucose;
Alpha1,6-
Glycosidase
Re 80: Sucrose + H2O{default} -> Glucose + Fructose; Sucrase
Re 85: Lactate + NADH{default} -> Glucose + NAD+{default}
Re 87: Trehalose + H2O{default} -> Glucose; Trehalase
5
Fasting
Re 50, 55
Re 50: "Glycogen Primer" -> Glycogen; "Glycogen Synthase" "Glycosyl transferase"
Glycosyl-4,6-Transferase
Re 55: Glycogen -> G1P + "Limit Dextrin"; "Glycogen phosphorylase"
6
Exercise
Re 15, 42, 92, 93,
Re 15: "A CoA" + CO2{mitochondria} + OAA + H2O{mitochondria} -> Citrate + CoA-
96, 97, 147
SH{mitochondria}; "Citrate Synthase"
Re 42: Pyruvate + TPP + Co-Ash + NAD+{mitochondria} + FAD + LIPOATE -> "A CoA" +
NADH{mitochondria}
+
H+{mitochondria};
"PYRUVATE
DEHYDROGENASE"
Mg2+{mitochondria}
Re 92:AcetoacetylCoA + AcetylCoA + H2O{mitochondria} ->HMGCoA + CoASH{mitochondria}; "HMG-CoA Synthase"
Re 93:HMGCoA -> Acetoacetate + "A CoA"
Re 96:AcetoacetylCoA + CoA-SH{mitochondria} -> "A CoA"; Thiolase
Re 97: "A CoA" ->AcetoacetylCoA + CoA-SH{mitochondria}; Thiolase
Re 147: "C4 Ketoacyl-CoA" + CoA-SH{mitochondria} -> "A CoA"; Thiolase
7
Obesity
Re 82
Re 82: Glycerol3P + FA -> Triglyceride
Table5.1: Condition based reactions in HEPNet
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Building an Algorithm-Hybrid Approach and Validation
5.2 Tool and Methodology used
5.2.1 COPASI
Simulation and modeling is becoming a standard approach to understand complex biochemical
processes. We have used software tools which allow access to diverse simulation and modeling
methods as well as support for the usage of these methods. COPASI is complex pathway
simulator.
COPASI is a software application for simulation and analysis of biochemical networks and their
dynamics. COPASI as shown in Figure 5.1 is a stand-alone program that supports models in the
SBML standard and can simulate their behaviour using Gillespie's stochastic simulation
algorithm or ODEs; arbitrary discrete events can also be included in such simulations.
COPASI carries out several analyses of the network and its dynamics and has extensive support
for optimization and parameter estimation. COPASI provides means to visualize data in
customizable plots, histograms and animations of network diagrams. COPASI can read models
in SBML format. COPASI can write models in several different formats including the SBML.
Some Features of COPASI are:Model

Chemical reaction network.

Arbitrary kinetic functions.

Ordinary Differential Equations for species compartments and species

Assigning values and parameters for compartments and species

Initial assignments for compartments and species
Analysis:

Time course simulation : Deterministic and Stochastic

Metabolic control analysis

Sensitivity analysis.
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CHAPTER-5

Mass conservation analysis.

Time scale separation analysis

Parameter scan
Building an Algorithm-Hybrid Approach and Validation
Model:
Models are defined as chemical reactions between the molecular species. Rate laws associated
with individual reactions determine the dynamics of the model is determined by. Models can also
include events, compartments, and other global variables that can help in specifying the
dynamics of the system.
Tasks:
Tasks are different types of analysis that can be performed on a model. They include steady-state
analysis, time course simulation using deterministic and stochastic simulation algorithms,
stoichiometric analysis, metabolic control analysis, time scale separation, optimization,
parameter scans, and parameter estimation.
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Figure 5.1: Screenshot of the COPASI user interface
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Time course simulation supported by COPASI is of three types:

Deterministic

Stochastic

Hybrid
Time course simulation helps in calculating the trajectory for the species in our model over a
given time interval. The different methods are selected to be used according to the model; there
may be a case where more than one method is appropriate for the simulation.
LSODA algorithm is used to perform the deterministic simulation. For model with particle
number, stochastic simulation is better than deterministic. Here deterministic approach is used as
the model contains concentrations. LSODA is a part of ODEPACK library written by Linda R.
Petzold and Alan C. Hindmarsh. The method in COPASI to calculate a time course is LSODA
by default. It solves systems dy/dt = f (t,y) with a dense or banded Jacobian when the problem is
stiff, but it automatically selects between stiff (BDF) and non-stiff (Adams) methods. Initially it
uses the non-stiff method, and monitors the data dynamically in order to decide which method to
use.
The advantage of using deterministic model is that it is less time consuming than stochastic
simulation of model. The method which tries to combine the advantages of both the deterministic
and stochastic simulation is termed as hybrid simulation. COPASI supports both the simulation
algorithms, and the hybrid method which can be used where deterministic simulation does not
gives the correct results, but hybrid will give correct results and is computationally less
demanding.
5.2.2 SOSlib (SBML ODE Solver Library)
SOSlib is a command-line application as well as programming library both and is used for
symbolic and numerical analysis of a system of ordinary differential equations derived from a
chemical reaction network encoded in the SBML.
The native Application Programming Interface provides fine-grained interfaces to all internal
data structures, enabling the construction of more powerful and with efficient analytical
applications. In this project the solver used is SOSlib with CellDesigner.
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5.2.3 Methodology
Extensive literature investigation revealed interesting facts and a large number of research
progress in the area. The problem was selected based upon its relevance in the concerned field. It
was found that TCA plays a central role in catabolism of the fuel molecule and production of
ATP. The dynamics of TCA is still an unanswered puzzle and gaining the interest of scientists all
over the world. The rate limiting enzyme of TCA, alpha-ketoglutarate, and its role in brain cell
metabolism by following a modelling approach was studied in this project.

First step was to get familiar with the CellDesigner software, which is used for modelling
and simulation. Online available literatures of the software were studied. A model of
TCA was created by taking reference from several books and journals.

Next step was to collect the concentrations of various substrates of the TCA. Rigorous
data search was done and information had to be scrutinized to see if it did fit the model or
not. Concentrations were entered for each substrate in CellDesigner.

Now kinetic laws were generated by the tool SBML squeezer.

SBML2LATEX was used to generate the PDF of the model in human readable format. It
converts SBML files to pdf format.
 The final step was of performing simulation using, COPASI, SOSlib and CellDesigner.
Plots were generated by changing the concentration of enzymes. Changes, both minor
and major were minutely studied and analysed.
5.2.4 Creating the model

Download CellDesigner version 4.3

Open CellDesigner window

Go to file  new , a window will appear, file in the name of the model and the size of the
panel (Figure 5.2)
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Figure 5.2: Creating model in CellDesigner
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
Building an Algorithm-Hybrid Approach and Validation
Choose the species and the compartment

Substrates were denoted by “simple molecules”, while the proteins were denoted
by “generic proteins”, ions were denoted by “ion”.

“State transition” arrow represented conversion, “transport” arrow denoted
transport, and “association” arrow denoted association and “dissociation” arrow
denoted dissociation. Reversibility of the reaction could be set by checking the
reversible option to true or false.

Enzymes were connected using the “catalysis” arrow or the “inhibition” arrow as
per its activity.

Reactants and products were added using the arrows “add reactant” and “add
product”.

A compartment for showing mitochondria was chosen, another compartment is
considered as the cytoplasm.

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Complete model was created by linking each step with another.
CHAPTER-5
Sl. No.
1
Building an Algorithm-Hybrid Approach and Validation
Metabolite
Urea
Value/Range in uM
Biofluid
Reference
6500.0 (4000.0-9000.0)
Blood
[86]
2
Cellular
Xylulose 5-phosphate
3
0.43
Cytoplasm
[87]
155.0 +/- 113.0
Blood
[88]
Glucose 6-phosphate
29.1 +/- 6.8
Blood
[89]
Fructose 6-phosphate
10.2 +/- 1.8
Blood
[89]
1.2 +/- 0.4
Blood
[89]
15.6 +/- 4.56
Blood
[90]
4.8 +/- 1.6
Blood
[89]
Uridine
diphosphate
glucose
4
5
6
Fructose
1,6-
bisphosphate
7
Dihydroxyacetone
phosphate
8
D-Glyceraldehyde
3-
phosphate
9
Glyceric
acid
1,3-
biphosphate
10
3-Phosphoglyceric acid
Cellular
0.4
Cytoplasm
[86]
47.2 +/- 7.4
Blood
[89]
11
Cellular
2-Phosphoglyceric acid
14.0 (9.0-19.0)
Cytoplasm
[86]
acid
17.4 +/- 3.8
blood
[89]
13
Pyruvic acid
64 (22-258)
Blood
[91]
14
Citric acid
190.0 (30.0-400.0)
Blood
[92]
15
cis-Aconitic acid
13 (2.7-44)
Urine
[93]
16
Isocitric acid
6.0 (0.0-10.0)
Blood
[92]
17
Fumaric acid
1.5 (0.0-4.0)
Blood
[92]
18
L-Malic acid
3.2 +/- 0.9
Blood
[89]
12
Phosphoenolpyruvic
19
Cellular
Oxalacetic acid
61
Cytoplasm
[93]
20
Glycogen
43.3 +/- 3.4
Blood
[89]
21
Glycerol 3-phosphate
30.0 +/- 3.0
Blood
[89]
22
D-Fructose
31.0 +/- 3.0
Blood
[89]
23
6-Phosphonoglucono-Dlactone
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Cellular
0.00762
Cytoplasm
[86]
CHAPTER-5
Building an Algorithm-Hybrid Approach and Validation
24
25
Cellular
6-Phosphogluconic acid
2720
Cytoplasm
[86]
D-Ribulose 5-phosphate
1.58 +/- 1.31
Blood
[90]
26
Cellular
Xylulose 5-phosphate
27
D-Sedoheptulose
D-Erythrose
Cytoplasm
[86]
0.89 +/- 0.41
Blood
[90]
7-
phosphate
28
0.43
4-
phosphate
Cellular
1770
Cytoplasm
[86]
1.45 (0.63-3.45)
Urine
[89]
88.3 +/- 34.7
Blood
[89]
31.0 (0.0-57.0)
Blood
[89]
29
Alpha-Lactose
30
D-Galactose
31
Galactose 1-phosphate
32
L-Lactic acid
740.0 +/- 2400.0
Blood
[89]
33
L-Glutamine
586.0 (502.0-670.0)
Blood
[94]
34
L-Alanine
333.0 (259.0-407.0)
Blood
[94]
35
Citrulline
38.0 (30.0-46.0)
Blood
[94]
36
Argininosuccinic acid
0.0032(0.00-0.0065)
Urine
[95]
37
L-Arginine
99.00 +/- 22.8
Blood
[96]
38
D-Ornithine
89.0 +/- 28.0
Blood
[97]
39
Oleic acid
11.42 +/- 1.67
Blood
[98]
40
L-carnitine
43.0 (26.0-79.0)
Blood
[99]
41
UDP
41.0 +/- 12.0
Blood
[89]
42
Triglyceride
43.2 +/- 9.2599946
Blood
[100]
43
Trehalose
0.056
Blood
[101]
44
Cerebrospinal
TPP
0.0032 +/- 0.0022
Fluid (CSF)
[102]
1.8 +/- 1.2
Blood
[89]
16.0 (0.00-32.0)
Blood
[103]
20-60
Blood
[104]
45
Sucrose
46
Succinate
47
Succinyl CoA
48
D-Ribose 5 phosphate
13.2 +/- 4.8
Blood
[89]
49
QH2
7.4 +/- 2.7
Blood
[105]
50
Pi
379.1 +/- 31.6
Blood
[106]
51
Ppi
1.8 (0.64-2.96)
Blood
[89]
52
OAA
61
Cellular
[88]
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Cytoplasm
53
NADPH
51.0 (34.0-81.0)
Blood
[89]
54
NADH
22.0 (14.0-40.0)
Blood
[89]
55
NAD+
24.00 (23.00-25.6)
Blood
[107]
56
Mg2+
850.00 (700.00-1000.00)
Blood
[108]
57
Lipoate
0.077 +/- 0.017
Blood
[89]
58
HMG-CoA
0.25 (0.0-0.5)
Blood
[109]
59
HCO3-
24900.0 +/- 1790.0
Blood
[89]
60
H2O
55,000,000
Blood
[110][111][112][113]
61
Glyceraldehyde
1476.0 +/- 655.0
Blood
[114]
62
Glutamate
7.9 +/- 3.9
Blood
[115]
63
Glucose
4440.0 +/- 370.0
Blood
[89]
64
GTP
56.0 +/- 7.0
Blood
[89]
65
GDP
15.0 +/- 2.0
Blood
[89]
66
G1P
5
Blood
[116]
67
FMN
0.0075 (0.004-0.011)
Blood
[117]
68
FAD
0.075 (0.056-0.097)
Blood
[117]
69
F1P
0.0005
Blood
[118]
70
Beta hydroxybutyrate
36.0 (13.0-95.0)
Blood
[89]
71
CO2
21600.0 +/- 600.0
Blood
[89]
72
Alpha Keto glutamate
8.9 +/- 2.7
Blood
[89]
73
Acetone
30.0 +/- 20.0
Blood
[89]
74
Acetoacetate
21.0 (0.0-86.0)
Blood
[85]
75
ATP
1390.0 +/- 170.0
Blood
[89]
76
AMP
51.0 (10.0-92.0)
Blood
[89]
77
ADP
160.0 +/- 14.0
Blood
[89]
78
6-Phosphonoglucono-Dlactone
79
O2
Cellular
0.00762
Cytoplasm
[87]
6960.0 +/- 410.0
Blood
[89]
Table5.2: Concentrations of species (metabolites) in both male and female of above 18 years
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Building an Algorithm-Hybrid Approach and Validation
5.2.5 Performing time-course analysis in CellDesigner

Go to simulation  control panel on the tool bar (Figure 5.3).

Control panel window will appear. Set the time span as 2.5; select the solver as COPASI
or SOSlib.

In the interactive simulation section, the range could be defined for all species, it could be
changed to study the effect of alteration in enzyme activity

Click on execute button
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Building an Algorithm-Hybrid Approach and Validation
Figure 5.3: Performing simulation using CellDesigner
5.2.6 Performing time-course analysis in COPASI
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Building an Algorithm-Hybrid Approach and Validation
1. Import SBML model from CellDesigner
2. Same time interval is set as used in CellDesigner

The interval size and no of intervals is adjusted as per the need.
3. Select the algorithm for simulation, which could be deterministic (LSODA), Hybrid
(LSODA), stochastic (gibsonbruck), stochastic (direct method). Deterministic method
(LSODA) is used here for simulation.
4. Now output assistant is used to create some of the predefined plot types like
a) Concentrations, Volumes, and Global Quantity Values- A plot of the variable species
concentrations, variable compartment volumes, and variable global quantity values vs.
time.
b) Concentration Rates, Volume Rates, and Global Quantity Rates-A plot of the rate of
change of concentrations of species, compartment volume, and global quantities, which
are determined by ODEs or reactions vs. time.
c) Reaction Fluxes-A plot of the fluxes of all reactions vs. time, in concentration/time unit.

Select the plot according to the model and run the time course simulation.

The plots windows will be generated with different options like to save, print and
zoom the plot. The data used for the plot generation can also be saved in text format.

The difference between the stochastic and deterministic simulation can be expected
only in certain conditions.
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Substrate
Enzyme
km
Temperature
pH
Reference
UDP-glucose-hexose-1phosphate
UDP- glucose
uridylyltransferase
0.13
37
8.7
[32]
UDP-galactose
UDP-glucose 4-epimerase
0.069
37
8.8
[33]
UTP-glucose-1-phosphate
0.05 to
UDP-glucose
uridylyltransferase
0.066
37
7.5
[34]
ATP+D-Glyceraldehyde
Triokinase
0.31
30
7.1
[35]
1.37
30
7
[36]
0.15 to
Alpha trehalose
Trehalase
Phosphogluconate
NADP+ + 6 phospho D-gluconate
dehydrogenase
0.157
37
8
[37]
Acetoacetate
Acetoacetate decarboxylase
1.28
37
7.4
[38]
Citrate
aconitase
2900
75
8.4
[39]
NADP+ + crotonoyl CoA
Acyl CoA dehydrogenase
3
25
7
[40]
Fructose 1,6-bisphosphate
Aldolase
1.7
10
7.4
[41]
NAD+ + 2-Oxoglutarate + Coenzyme
Alpha ketoglutarate
A
dehydrogenase
490
unknown
unknown
[42]
p-Nitrophenyl-alpha-L-fucoside
Alpha 1,6 glycosidase
0.08
37
4.5
[43]
L-Aspartate + 2-Oxoglutarate
aspartate aminotransferase
2.06
37
6.8
[44]
18.7
unknown
7
[45]
34.5
32
5
[45]
2
25
9.5
[46]
Beta-KetoacylCoA
H+ + Acetoacetyl-CoA + NADH
dehydrogenase
Beta hydroxy acyl coa
H+ + Acetoacetyl-CoA + NADH
dehydrogenase
Acetaldehyde+nad+ +H2O
Kegg: C01470
NH3 + NH4Cl + ATP +
-
0.08
24
7.4
[47]
L-Ornithine+Hydrogencarbonate
-
unknown
unknown
unknown
-
Citrate+coenzyme A+ATP
Citrate synthase
0.11
unknown
unknown
[48]
Beta hydroxybutyrate
NAD+ + (R)-3-Hydroxybutanoate
dehydrogenase
12.6
25
7.5
[49]
2-Phospho-D-glycerate
Enolase
300
25
6.8
[50]
H2O + 2-Methylprop-2-enoyl-CoA
Enoylcoa hydratase
100
30
8
[51]
UDP galactose
Epimerase
140
unknown
8.5
[52]
D-fructose 1,6 bisphosphate + H2O
Fructose 1,6 bisphosphatase
1.3
37
7.5
[53]
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Protein: FMN
-
27
37
8
[54]
Protein: FeS
-
0.7
unknown
7.5
[55]
ATP+ D-fructose 6 phosphate
Fructokinase
0.58
25
8
[56]
Fumarate
Fumarase
0.013
30
7.3
[57]
H2O+ Glucose 6 phosphate
G-6-P Phosphatase
2
37
6.5
[58]
D-glyceraldehyde 3 phosphate
G3P DEHYDROGENASE
33
23
8.6
[59]
Glucose 6 phosphate
G6PDehydrogenase
0.04
37
7.4
[60]
D-galactose
Galactokinase
970
37
8
[61]
Glycogen synthase
0.08
32
7.2
[62]
Glucosyl)n
Glycogen phosphorylase
0.1
38
7.2
[63]
Acetoacetyl-CoA + H2O + Acetyl-
Hydroxymethylglutaryl-CoA
CoA
synthase
294
30
8.2
[64]
d-glucose
Hexokinase
0.048
37
7.25
[65]
0.22(mn+)
25
7.2
[66]
18.2
28
6.8
[67]
3.80E-04
unknown
10
[68]
UDPglucose + (1,4-alpha-DGlucosyl)n
Phosphate + (1,4-alpha-D-
73(nad+),
Isocitrate
Isocitrate dehydrogenase
d-gulonolactone
Lactonase
l-malate
Malate dehydrogenase
Phosphoenol pyruvate
PEP carboxykinase
791
37
7.2
[69]
Glycerate 3 phosphate
Phosphoglycerate kinase
300
unknown
8
[70]
Glycerate 3 phosphate
PGA mutase
0.1
30
7.1
[71]
d-glyceraldehyde 3 phosphate
Triose-phosphate isomerase
13.6
25
7.5
[72]
Pyruvate
Pyruvate carboxylase
0.11
30
7.4
[73]
Pyruvate+coenzyme A+NAD+
Pyruvate dehydrogenase
30 to 40
37
7.8
[74]
Phosphoenol pyruvate
Pyruvate kinase
5.8
37
7.5
[75]
Glucose-1 phosphate
Phosphoglucomutase
0.045
38
7.2
[76]
2.13E-05
37
8
[77]
Glucose 6 phosphate
d-fructose 6 phosphate
isomerase
Ribose 5 phosphate
Ribose 5 phosphate
isomerase
1.78
25
8.6
[78]
Succinate
Succinylcoa synthase
0.09
37
7.4
[79]
Acetaldehyde
Succinate dehydrogenase
875
25
9
[80]
Acetyl CoA
Thiolase
16
37
8
[81]
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Rubp and Xubp
Transketolase
0.19
37
7.6
[82]
Sucrose
Sucrase
10
37
6
[83]
HCO3-+NH4+
Carbamoyl phosphatase I
13
37
7.2
[84]
GA3P
Transaldolase
0.13
37
7.2
[85]
Table5.3: HEPNet species km value chart with their corresponding references.
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5.2.7 Plotting in COPASI
1. Import models.
2. Go to plots, change the plot title and double click on new plot (Figure 5.4).
3. Log value checkbox can be checked for plotting graph between log values.
4. Now from curve specifications, either new curve or a histogram can be made.
5. A new window will open which will contain the different variables to be plotted for x
axis and y axis (X, Y coordinates).
6. Similarly, histogram can also be plotted.
7. Now after selecting variables click on OK and Commit the Task, click run and plot
window will be generated in front of COPASI user interface window.
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Figure 5.4: A. Performing simulation in COPASI; B. Generating COPASI Plots
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5.3 Results and Discussion
5.3.1 Glycaemia
5.3.1.1 Introduction
Glycaemia is a condition that refers to the concentration of sugar or the level of glucose in the
body. The control of glycaemia is achieved by a number of physiological processes. After eating
a meal, the glycaemic index increases as carbohydrates are broken down into simple sugars and
then the glucose is released into the blood stream. Likewise, when we perform rigorous exercise,
the glycaemic index drops down as energy is utilized. Glycaemic index is one of the most
important factors in the maintenance of homeostasis. A number of important hormones like
insulin, glucagon and epinephrine play different roles in the positive/negative regulation of
glycaemia. Glycaemia can be further categorized as hyperglycaemia and hypoglycaemia.
Hyperglycaemia is the condition where the level of blood glucose increases in our body and
hence the homeostasis is affected. One of the major reasons is that our body is unable to get rid
of the ketone bodies. Hyperglycaemia is a serious ailment if it remains ignored and untreated. It
can also lead to a condition called ketoacidosis when insulin is very scarce in our body. In the
absence of insulin, glucose break down does not function properly and then body fats start
getting utilized. The fats produce excessive ketone bodies and they accumulate resulting in the
condition of ketoacidosis. Hyperglycaemia is linked with another condition called
atherosclerosis.
Hypoglycaemia is defined as the condition of abnormally low levels of blood glucose in the
body. In some cases, it is also referred to as insulin shock. The common symptoms of
hypoglycaemia include shaking, anxiety, etc. Hypoglycaemia can lead to a severe condition if it
is not treated. The worst case is having seizures and unconsciousness. Glucagon is the hormone
that tells the liver to release glucose when the level of glucose in the blood is too low. So, in case
of hypoglycaemia, a person can be injected with glucagon in order to bring back the
homeostasis.
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5.3.1.2 Results and Discussion
A. Glycemic.
(i)
Integrating w.r.t. x
(ii)
(iii)
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(Glucose factor)
*Glucose factor is that parameter in the equation which is based on the Glucose concentration
pertaining to hyper/hypo glycaemic condition. That is if we consider the rate of glucose:
We see that it is being consumed as being denoted by the negative sign. Also, irrespective of the
default flux value, Vdefault, a Km value of 0.048 is responsible. Under steady state condition, the
elementary flux modes result can be found, but we may ignore it since all these are constants.
What plays the major role is the absolute concentration of glucose at a specific instant. Thus,
the Glucose factor is calculated by using the specific concentration of glucose where the other
components of the equation remains constant and do not require to be changed.
Hypoglycaemic:
(iv)
Hyperglycaemic:
(v)
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Comparing the Glucose factor in both the two states of glycaemia, it is well understandable that
till a precision of 10–3 there is no change. But from 10–5 there are significant changes. This may
sound that at such small levels of precision the equation does not change remarkably. But in
actual physiological state, a minute change even in picomolar concentration may lead to severe
glycaemic condition as we can see through the following Figure 5.5 generated by Sigma Plot.
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Figure 5.5: A. Illustration of rate of progress of glucose focus as for the measure of glucose
accessible in hypoglycaemia. d [Glucose]/dt versus Glucose fixation in μM is plot where a straight line
is acquired surmising a continuous increment in the Glucose variable with time. B. Illustration of rate
of progress of glucose focus as for the measure of glucose accessible in hyperglycaemia.
d[Glucose]/dt versus Glucose focus in μM is plot where a straight line is acquired yet the
hyperglycaemic condition begins when the rate of Glucose variable change goes to 1.1e+8 where the
Glucose fixation is close to 7000 μM.. C. Graph showing hypoglycaemia to hyperglycaemia.
Illustration of rate of progress of glucose fixation as for the measure of glucose accessible, a move from
hypoglycaemia to hyperglycaemia. D. Graph depicting variation in ATP concentration. Represents
the diminishing rate of ATP fixation because of utilization as for accessible ATP focus. E. ATP
concentration varying with time. Outlines the starting drop in rate of ATP fixation as it is remunerated
by| the
framework, glycolysis and Krebs cycle.
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5.3.2 Starvation
5.3.2.1 Introduction
Starvation is defined as the condition when our body gets extremely calorie deficient. Starvation
can also be known as the extreme form of malnutrition. Also, a period of long duration of
starvation causes damage to our organs. The condition arises due to a disturbance in the body’s
demand and supply of energy. This can occur due to a number of reasons including medical
disorders. A person when stops eating the food for a long duration enter the condition called
starvation. A response initially is generated in earlier periods of starvation. For a day or two, the
body uses its stored reserve i.e. glycogen. But the liver glycogen reserve is very scarce and gets
replenished soon. Then, gluconeogenesis is the way the glucose requirement is met by the body
in cases of short term starvation which is also called as fasting. As the body enters into long term
starvation or prolonged fasting, metabolic function starts getting disrupted by reduced activity of
the muscle. As a result, the ketone body concentration also increases and protein catabolism
falls. Starvation response at a later stage is mostly generated through endocrine hormones. These
hormones are classified as activity increasing hormones like growth hormones, androgens,
insulin and glucagon.
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5.3.2.2 Results & Discussion
Starvation was evaluated on the basis of the simplified ODE
The solution to the equation above obtained is
Considering the above equation which is logarithmic and also has an arbitrary constant C1, it is
well clear that, a negative factor of -2.1586 plays the role in starvation. This constant value
termed as Starvation factor is derived from the glucose metabolite and plays a key role. Apart
from ODE in general, 6 reactions play a major role in starvation but it is the glucose whose
limiting nature makes the study important. This distinguishes its role from that of the glycaemic
study as we see the difference in both the factors.
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5.3.3 Exercise
5.3.3.1 Introduction
When we are at a resting condition, the total amount of ATP is produced through aerobic
metabolism and the lactate levels in blood are low. As we start exercising, there is an immediate
transition of ATP production. The production increases and so does the oxygen uptake. The
initial requirement is not through anaerobic pathway like glycolysis. After a steady state is
established, the body’s ATP demand is met through aerobic metabolism. When we do more
exercise, our body temperature increases and also blood concentration of lactic acid. Also there
are higher levels of blood epinephrine and non-epinephrine in the body. When we do prolonged
exercise, i.e. more than 10 times, uptake of oxygen increases in a linear manner until a stage of
maximum uptake is reached. There comes a point called as lactate threshold where the blood
lactic acid rises systematically. In low intensity exercise, fats are the main fuel. At high intensity
exercise, the carbohydrates are the main fuel. The shift occurs due to the increase in the levels of
epinephrine in the body. The chief source of carbohydrate when we do high intensity exercise is
the muscle glycogen and during low intensity exercise, the blood glucose supplies the energy
through liver glycogenolysis. To summarize, the selection of the appropriate fuel for exercise is
regulated by diet and the intensity and duration of the exercise. During prolonged exercise for
more than an hour there is a shift from carbohydrate metabolism to fat metabolism.
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5.3.3.2 Results and Discussion
Exercise was evaluated on the basis of the simplified ODE
The solution to the equation above obtained is
Considering the above equation which is logarithmic and also has an arbitrary constant C1, it is
well clear that, a negative factor of -1.9976 plays the role in exercise. This constant value termed
as Exercise factor is derived from the acetyl CoA metabolite and plays a key role. Apart from
the ODE, in general, 16 reactions play a major role in starvation but it is the acetyl CoA whose
limiting nature makes the study important.
5.3.4 Obesity
5.3.4.1 Introduction
Obesity is defined as the condition where there is accumulation of excess fats in the body.
Obesity is also associated with many critical disorders like diabetes, cardiac disorders and
cancer. We can measure obesity in simpler terms using the Body Mass Index (BMI). But more
accurately, fat analyzers can be used to check the obesity levels. The fat deposits are mainly
concentrated either in the central abdominal area or around the gluteal region. The central
abdominal area is more prone to disorders and is highly risky. The abdominal fat contains a
larger size of adipose cells. The main constituent of adipocytes is fat cells or triglycerides. Fat
cells once they are gained are never lost. Reduction of weight occurs only through the reduction
of the size of adipose cells. Obesity is caused due to energy imbalance in the body. The number
of calories consumed is not equal to calories used. Adipocytes also send signals that cause a
number of not so good metabolic changes such as increase in the level of high triglycerides and
low level of HDL. It also causes intolerance of glucose. Glucose gets build up in the body and in
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the worst conditions it causes insulin resistance. A compound called leptin increase metabolic
rate and decreases appetite in human which helps reducing the obesity in the body. The balance
between the food consumption and the expenditure is maintained through biochemical processes
that are required to maintain cellular vitality.
Results and Discussion
Obesity was evaluated on the basis of the simplified ODE
The solution to the equation above obtained is
Considering the above equation which is logarithmic and also has an arbitrary constant C1, it is
well clear that, a product of -2 plays the role in the obesity.
This constant value termed as Obesity factor is derived from the fact that triglycerides are stored
in liver. Triglycerides are generated due to the presence of both glycerol-3-phosphate and fatty
acids. The accumulation of triglycerides hence is responsible for obese condition. Only Re82
defines the behaviour of the reaction and is responsible for obesity.
5.3.5 Uremia
5.3.5.1 Introduction
Uremia, therapeutic condition delivered by the poisonous impacts of unusually high
centralizations of nitrogenous substances in the blood as an aftereffect of the kidney's inability to
oust waste items by method for the pee. The final results of protein digestion system amass in the
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blood yet are ordinarily sifted through when the blood goes through the kidneys. Uremia can
come about because of any issue that disables the working of the kidneys or that ruins the
discharge of pee from the body.
The indications of uremia are assorted. Weariness, languor, and a loss of mental fixation may be
among the first signs. The patient may encounter diligent tingling sensations, alongside muscle
jerking. The skin gets to be dry and flaky and swings yellowish to tan. The mouth has a dry
metallic taste, and the breath has particular smelling salts like scent. Loss of craving advances to
queasiness and retching; scenes of loose bowels and clogging may happen. In the more serious
phases of uremia, the development of waste items in the circulatory system and tissues causes a
far reaching disturbance of the cardiovascular and respiratory frameworks and can prompt
edema, hypertension (high blood pressure), convulsions (seizures), heart failure, and demise.
The main reason for uremia is harm to the kidneys, which has a mixed bag of reasons. Maladies
that can influence kidney capacity incorporate Bright sickness (glomerulonephritis), incessant
hypertension, and diabetes mellitus. Blockages of the stream of pee because of urinary stones or,
in guys, extended prostate organs can likewise bring about uremia. The treatment of uremia lays
on the distinguishing proof and treatment of the issue that is the hidden reason. Patients whose
kidneys are unhealthy and who are sitting tight for kidney transplants frequently endure
fluctuating degrees of uremia. In such cases, treatment commonly is with dialysis—i.e., the
manufactured sifting of the blood by a machine outside the body [119].
The uremic disorder can be characterized as a crumbling of biochemical and physiologic
capacities, in parallel with the movement of renal disappointment, bringing about mind boggling
and variable symptomatology .The intensifies that amass in the uremic blood and tissues amid
the advancement of end-stage renal ailment (ESRD), specifically or in a roundabout way because
of a lacking renal freedom, are called uremic maintenance solutes. These maintenance solutes
may adjust biochemical or physiologic capacities; on the off chance that they do as such, they
add to the uremic disorder. Just a couple of solutes have a set up part as uremic poisons. As
indicated by Bergoström, aside from inorganic mixes, urea, oxalic corrosive, parathyroid
hormone (PTH), and β2-microglobulin fit in with the strictest meaning of uremic poisons. Be
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that as it may, this does not block a potential dangerous part for different other maintenance
solutes.
The accompanying components, which are not generally considered, may influence uremic
solute focus and their effect on biologic capacities (1) notwithstanding traditional wellsprings of
uremic solutes, for example, dietary protein breakdown, option sources, for example,
environment, home grown medications, or hallucinogenic medications may assume a part in
uremic poisonous quality. Numerous solutes with dangerous limit enter the body through the
digestive system. Changes in the organization of intestinal greenery, or changes in intestinal
generation and assimilation, may modify their serum fixation. Some uremic solutes meddle with
capacities that specifically influence the biochemical activity of different solutes: the outflow of
PTH receptors, the reaction to 1,25 (OH)2 vitamin D3, and additionally the protein tying and
breakdown of a few different solutes. Most uremic patients are endorsed a large group of
medications. Impedance of medications with protein tying and/or tubular emission of uremic
solutes will impact their biologic impact. Lipophilic mixes may be mindful at any rate to a
limited extent for practical modifications in uremia. The effect of remaining renal capacity on
uremic solute maintenance ought not to be dismissed. The fundamental method that has been
utilized something like now to diminish uremic solute fixation is dialysis, however dialysis is
nonspecific and evacuates crucial mixes also. Uremic solutes aggregate in the plasma as well as
in the cells, where the vast majority of the biologic movement is applied. Evacuation of
intracellular mixes amid dialysis through the cell layer may be hampered, bringing about multi
compartmental energy and lacking detoxification. It is of note that lower dreariness and mortality
are seen in patients submitted to long dialysis sessions. Mixes may be cleared all the more
proficiently with persistent or dependable low productivity methods, on the grounds that
evacuation is more progressive. Our perspectives on the uremic disorder and a few uremic
solutes have changed considerably amid the most recent decade. In this way, it was thought
opportune to abridge the current situation with information about the biochemical, physiologic,
and/or clinical effect of those aggravates that have been subjected to moderately careful
assessment amid these most recent 10 years. Particular consideration was additionally paid to era
and evacuation patterns [120].
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5.3.5.2 Results and Discussion
A plot of dynamic simulation of the constructed model was performed showing comparative
time course simulation in CellDesigner and COPASI as shown in Figure 5.6. A plot showing
concentration rates as a function of time was plotted using COPASI (Figure 5.7). From the
graph, it could easily be inferred that reactions tend to attain a constant rate as the slope becomes
zero. A concentration versus reaction time of NADH and NAD+ was plotted simultaneously
where the green line denotes NAD+ and blue line denotes concentration change of NADH with
respect to time in seconds (Figure 5.8). A change in concentrations of C22 trans-enoyl-CoA and
C22 L3Hydroxy acyl-CoA was plotted over period of time (Figure 5.9). C22 trans-enoyl-CoA is
denoted in blue and C22 L3 hydroxy acyl-CoA is denoted in green. This denotes the second step
of beta-oxidation after conversion of C22 acetyl-CoA to C22 trans-enoyl-CoA. The
concentration of the substrate decreases initially faster but later equilibrium is attained. Similarly
the product concentration increases with time as shown in Figures 5.10, 5.11, 5.12, 5.13 and
5.14. COPASI has more algorithms whereas Graphical User Interface of CellDesigner is better.
Plotting is better in COPASI as intuitive scaling if done by the simulator.
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Figure5.6: A plot of dynamic simulation using A. CellDesigner, B. COPASI: Plots
showing comparative time course simulation for CellDesigner and COPASI. The graphs
are plotted to prove that simulation results are reliable. Same results are produced using
any of these softwares.
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Figure 5.7: A plot showing concentration rates as a function of time. From the graph
it easily be inferred that all reaction tends to obtain a constant rate of reaction as the
slope becomes zero. This graph is plotted in COPASI.
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Figure 5.8: A. Time vs. concentration plot of NADH reveals that its concentration
increases with time. B. A NAD+ plot of concentration as a function of time using
COPASI output assistant. In agreement with the concentration increase of NADH, the
concentration of NAD+ decreases with time (as shown in plot of NADH with time). C.
A concentration vs. time plot of NADH and NAD+ shown together. Green line
denotes NAD+ and blue line denotes concentration change of NADH with respect to
time.
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Figure 5.9: A plot of change in concentrations of C22 trans-enoyl-CoA and C22
L3Hydroxy acyl-CoA over period of time. C22 trans-enoyl-CoA is denoted in blue
and C22 L3Hydroxy acyl-CoA is denoted in green. This is the second step of beta
oxidation after conversion of C22 acetyl-CoA to C22 trans-enoyl-CoA. The
concentration of substrate decreases initially very fast but later equilibrium is attained.
Similarly the product concentration increases with time.
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Figure 5.10: A scatter plot of C22Acyl-CoA concentration (x-axis) and Carnitine
in Matrix (y-axis)
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Figure 5.11: A scatter plot of C22 Hydroxy Acyl-CoA (x-axis) vs. C22 Trans-enoylCoA (y-axis). It shows that these two have negative correlation. This is logically
validated by the fact that with metabolic cycle progression concentration of C22 Transenoyl-CoA decreases whereas that of Hydoxy-Acyl-CoA increases.
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Figure 5.12: A concentration vs. time plot of all substrates and products of the
model in uremic condition.
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Figure 5.13: A 2D Bar chart depicting relative concentrations of Long Chain
carnitine and Long Chain acyl-CoA in normal and diseased conditions.
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Figure 5.14: A 2D Bar chart depicting relative concentrations of Long Chain acylcarnitine and free carnitine in normal and diseased conditions. The results are in
accordance with the disease condition of Uremia.
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5.3.6 DLDD
5.3.6.1 Introduction
Dihydrolipoamide dehydrogenase deficiency (DLD), also known as maple syrup urine disease
type III, is a metabolic disease caused by an enzyme deficiency that results in accumulation of
certain nutrients, called amino acids, in the brain and other organs. Affected infants frequently do
not survive their initial age, or die within the first few years of life during a recurrent metabolic
decompensation. It is an autosomal recessive metabolic disorder distinguished biochemically by
a combined deficiency of 3 enzymes which are branched-chain alpha-keto acid dehydrogenase
complex (BCKDC), AKGD complex (KGDC), and pyruvate dehydrogenase complex (PDC).
Clinically affected individuals suffer from neurological deterioration and lactic acidosis due to
sensitivity of the central nervous system to defects in oxidative metabolism. E3 deficiency is
often associated with increased urinary excretion of pyruvate. Dihydrolipoamide dehydrogenase
(DLD) is a mitochondrial protein that assumes an indispensable part in vitality digestion system
in eukaryotes. This chemical is needed for the complete response of no less than five diverse
multi-catalyst edifices. In case of humans, transformations in DLD are connected to a serious
issue of outset with inability to flourish, hypotonia, and metabolic acidosis. DLD lack shows
itself in an awesome level of variability, which has been credited to shifting impacts of diverse
DLD changes on the strength of the protein and its capacity to dimerize or associate with
different segments of the three α-ketoacid dehydrogenase complexes. With its proteolytic
capacity, DLD causes an insufficiency in frataxin, which prompts the neurodegenerative and
cardiovascular ailment, Friedreich ataxia [19]. Future exploration would like to survey how the
proteolytic movement of DLD adds to the indications of DLD inadequacy, Friedreich ataxia, and
ischemia reperfusion harm and whether this action could be an objective for treatment for these
conditions. Administration of DLD inadequacy is troublesome because of the different metabolic
pathways influenced. Administration of the early-onset neurologic presentation depends on
empiric treatment of the three secluded protein complex insufficiencies.
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5.3.6.2 Results and Discussion
DLD lack is an autosomal passive metabolic issue portrayed biochemically by a joined
insufficiency of the stretched chain alpha-keto corrosive dehydrogenase complex, pyruvate
dehydrogenase perplexing and alpha-ketoglutarate dehydrogenase complex [121]. The
comparative reaction fluxes in COPASI and CellDesigner are shown in Figure 5.15 and Figure
5.16.
Change of succinyl CoA focus with time
A reaction plot was created portraying the change of succynl CoA (SCoA) in TCA cycle as for
time as indicated in Figure 5.17A and Figure 5.17B. An essential diagram was plotted with the
time of 1000s. It was watched that amassing of SCoA reductions at first at exponential rate. An
increment is seen steadily and the level abatements marginally as the response advances until it
obtains a consistent rate. To further watch the relationship with time, the time size of 400 times
lesser than the first one gives better knowledge about the marvel. SCoA falls as the starting
amassing of SCoA is a great deal more than chemical focus then it achieves a level stage where
SCoA fixation decays even beneath the discriminating level.
Simulated concentration of alpha-ketoglutarate as a function of time
A plot of simulated convergence of alpha-ketoglutarate (AKG) as a component of time
demonstrates a beginning increment, as it goes about as a substrate in the rate constraining stride
as indicated in Figure 5.18A. Next, a chart is plotted portraying time course re-enactment of
AKG with time as indicated in Figure 5.18B; since it is the rate constraining step, amassing of
AKG increments with time. Continuously as the time advances, the substrate response expands
exponentially and the rate of transformation of AKG into SCoA additionally increments, till it
achieves a balance state.
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Correlation between NAD+, AKG and NADH
The plots of focus as demonstrated in Figure 5.21 and Figure 5.22 portray the progressions as for
time between NAD+ (purple), AKG (blue) and NADH (yellow). This incorporated plot
demonstrates that rate of progress of NADH with NAD+ and AKG. It obviously demonstrates
that rate of NADH arrangement relies on AKG.
Next, a plot as indicated in Figure 5.18 for fluctuating convergences of AKGDHC was produced,
and an adjustment in inclines was watched. These plots are of response AKG →SCoA with time.
With diminishing centralization of catalyst AKGDHC, slant likewise diminishes. Almost at
around 20% (Figure 5.24 and Figure 5.25) restraint the increment in substrate focus causes the
response flux to be kept up to a certain degree. This is on the grounds that the reduction in
protein reasons increment in AKG focuses which remunerates the adjustment in flux, and the
AKG level enacts the remaining dehydrogenase chemical. This increment can likewise be taken
as marker for AKGDH weakness as it prompts height of alpha-ketoglutarate level in blood and
pee. The diminishing response flux is striking at 40 percent. The flux bend begins to frame a
straight line at 60 percent restraint deducing the diminishing response flux. Further reduction in
slant is obvious at 80 percent diminish. At around 95 for each penny the response flux
diminishes definitely and it can be accepted that ATP generation falls as needs be consequently
the vitality substance of cell reductions. The rate of ATP era as for time as delineated in Figure
5.19 and Figure 5.23 demonstrate the drop in level of ATP fixation with lessening in AKGDHC,
underpins the introductory theory that ATP era is reliant on AKGDHC. An eminent indicative
element of DLDD where pyruvate is conversely corresponding to AKGDHC is outlined by the
plot between the diverse convergence of AKGDHC and pyruvate.
To comprehend the impact of lessened AKGDHC on mitochondrial vitality digestion system by
TCA cycle, an exhaustive SBML model was produced. This model is progression over the
accessible models of TCA and can be utilized for concentrating on the impact of AKGDH on the
TCA cycle and its substrates. In concurrence with the test discoveries, the model re-enactments
affirm a decrease in response fluxes and NADH level. The discovering recommends that it is the
rate restricting stride of the TCA As has been shown before. Since ATP generation is likewise
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influenced by NADH creation rate it can be securely accepted that lessening in NADH
additionally causes change in the rate of ATP creation.
Diminishing in AKGDH likewise connects with loss of glutamatergic neurons as found in
Alzheimer's and Parkinson's infection. The recreation proposes deviations from typical course
may prompt the revelation of steps in charge of the infection. Additionally, change in pyruvate
fixation on changing the centralization of AKGDH likewise supports the significance of the
chemicals included in DLDD. Reproductions unmistakably demonstrate that AKGDH
inadequacy may bring about expansion in pyruvate fixation.
In HEPNet we have made the relentless state suspicion. The demonstrated framework has
entered an unfaltering state, where the amassing of metabolite no more change, i.e. in every
metabolite hub the delivering and devouring fluxes counteract one another. The unfaltering state
supposition diminishes the framework to an arrangement of straight comparisons, which is then
illuminated to discover a flux dissemination that fulfils the consistent state condition subject to
the stoichiometric limitations while amplifying the estimation of a pseudo-response (the goal
capacity).
It is inferable that the flux diminishes exponentially in the initial 0.02 seconds from Figure 5.26.
In the quick 0.02 seconds the flux settles to a steady estimation of 25E+7 (Figure 5.26) and in a
quick compass of 0.03 seconds it grounds to zero (Figure 5.26). A negative slant is watched
when the capacity produced is plotted with glucose fixation over a reach in hypoglycaemic
condition. A negative incline with a straight line therefore shows that the rate of progress ATP
focus diminishes with expanding ATP fixation proposing the usage of ATP all the while. It can
likewise be seen that in unfaltering state method of operation, the rate of progress of ATP focus
is contrarily related. In spite of the fact that toward the starting this speculation is false since
there is a bend. Be that as it may, it settles once the ATP fixation has expanded to 8 μM. This
conduct of the bend is because of the generation of ATP in different procedures also its usage in
the phosphagen, glycolysis and Krebs cycle. In spite of the fact that the arrangement of ODE
holds a log element, yet, the diagram is a straight line with a positive incline in. This
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demonstrates that the rate of utilization of glucose increments in the body as the convergence of
glucose increments from the hypo to hyperglycaemic state.
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Figure 5.15: Comparative plots generated by A. CellDesigner and B. COPASI
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Figure 5.16: Time course Simulation of concentration of species.
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Figure 5.17: A. A plot of change of Succinyl CoA concentration with time for a time
period of 1000 seconds. B. A plot of Succinyl CoA concentration with time for a time
period of 2.5 seconds.
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Figure 5.18: A. Simulated concentration of alpha ketoglutarate as a function of time,
increases initially as it is a substrate in rate limiting step of TCA. B. Complete graph
showing time course simulation of model for AKG
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Figure 5.19: Dynamic changes in simulated concentration of A. ATP and B. ADP
with respect to time i.e. rate of change of concentration of ATP and ADP.
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Figure 5.20: Change of concentration of A. NAD+ with time and B. NADH with time
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Figure 5.21: A. A plot of NADH with time. B. The plots of NADH (in blue) and
AKG (in yellow) with time
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Figure 5.22: A. The plots of concentration change with respect to time between NAD +
(purple), AKG (blue) and NADH (yellow). B. SCoA and CoA-SH flux comparison
graph
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Figure 5.23: A plot showing ATP A. concentration as a function of time B. particle
number as a function of time respectively using COPASI.
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Figure 5.24: A. Scatter chart of ATP and ADP B. Scatter chart of NADH (on x-axis)
and KG (on y-axis)
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Figure 5.25: A. Normal, B. 20%, C. 40%, D.60%, E.80%, F. 95% inhibition of AKGDHC
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0.01508
0.01503
Normal
0.01498
20%
0.01493
40%
60%
0.01488
80%
0.01483
95%
0.01478
0.01473
0
10
20
30
40
50
Figure 5.26: Graph between different concentration of AKGDHC and A. Rate of ATP
generation with respect to time and B. Pyruvate concentration with time.
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5.3.7 Simulation of the buffering of Creatine phosphate
A few metabolic pathways endure a setback because of absence of glucose prompting
unevenness in homeostasis in spite of the fact that the rate is relatively low in hypoglycemia.
An exceptional derivation to the creatine phosphate can be exhibited from the flux produced. It is
realized that, creatine phosphate acts like an ATP store and amid the body's need it gives ATP.
Correspondingly, it additionally breaks to give creatine and return back ADP where the
regressive response is catalyzed by creatine phosphokinase. After watching the flux produced
(Figure 5.27), we watch an exponential step plot. Since, this cushion framework is connected to
the focal metabolic pool of the Krebs cycle, where it goes about as a control we watch a
progression of steps. Acting, as a criticism regulation, ATP-ADP acts synergistically to control
the flux. Along these lines, the conduct of a stage like bend as watched.
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Figure 5.27: Simulation of the buffering of Creatine phosphate. Reaction 182
demonstrates an exponential steps plot, which signifies the role of creatine phosphate as an
energy buffer system. During the need of energy in the system, there is synthesis of ATP
and a reversal of the reaction to give ADP by creatine kinase. The buffer-ability of the
system is maintained duly by the synergistic role of ATP-ADP and a feedback by the
Krebs cycle accomplishes the need of the cell for energy.
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