Networks of Biological
Signaling Pathways
信号传递网络
康海岐
高方远
马欣荣
一、生物体内的信号传递
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1. The sense of signal transduction：
intercelluar information exchange,regulation of
metobolism, on body level
2. Type of signals：
neuroregulation: neurotransmitter(乙酰胆碱,胺类
氨基酸,调节肽类等),neuroregulator
chemical signals：cAMP, Ca2+ , hormone，
3. Mechanisms:
3.1 pr. ←→pr.,
3.2 E reaction(±p )
3.3 E activity
3.4 pr. degradation
3.5 intracelluar messager
3.6 seconder messager
E
cell
一、生物体内的信号传递
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4. Signaling pathways：
4.1 Ca2+
4.2 cAMP
4.3 tyrosine kinase: EGFR,insulinR
4.4 other pr. kinase cascade:PKC,PKA,PKG
4.5 intracelluar protease cascade
Signal transmission occur:
i. Pr.—pr. Interaction
ii. Enzymatic reaction: ±p
iii. Pr. Degradation
iiii. Production of intracellular messager
一、生物体内的信号传递
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5. cytoplasm membrane receptor：
5.1 neurotransmitter-dependention channel
(依赖神经递质的离子通道)：
nAChR(烟碱型乙酰胆碱受体)
GABA(γ -氨基丁酸)
GlyR(甘氨酸受体)
5.2 receptor connecting to signal transduction protein
(G,N protein →second messenger →activate E.):
mAChR(毒蕈碱型乙酰胆碱受体)
adrenergic α-,β-receptor (肾上腺素能 α-,β-受体)
5.3 growth factor receptor(tyrosine kinase activity)：
PDGFR(血小板衍生的生长因子受体)，
EGFR(表皮生长因子受体)，insulin R(胰岛素受体)
Peptide Signaling in Plants
PNAS, Nov. 6, 2001, vol.98 no. 23
•
In plants, only a few peptide have been identified
that act as signaling molecules.
•
In contrast, signaling peptides are major players
in all aspects of the life cycle in animals and yeast.
•
suggests that signaling mechanisms across the
eukaryotic kingdom are fundamentally different.
1. 目前有关植物中信号肽的研究主要基于以下5种：
番茄systemin
18 aa
PSK
ENOD40
10-13 aa
CLV3
72-75 aa
2. 最近分离到另外3种活性信号肽：
RALF: rapid alkalinization factor, 5 kd;
Tobacco systemin: Tob sys I,
Tob sys II
SCR
53-55 aa
3. 功能：
1）tomato systemin: 由食草动物损伤后引起的系统
损伤反应( a systemic wounding response)
• 在悬浮培养细胞中可以激活促细胞分裂蛋白激酶
[mitogen-activated protein(MAP) kinase]
• 并诱导培养基地碱化(alkalinization)
• 诱导蛋白酶抑制蛋白编码基因的表达(induce
expression of proteinase-inhibitor
protein-encoding genes)
2）tobacco systemin Tob I and Tob II:
激活 MAP kinase，但不诱导蛋白酶抑制蛋白编码
基因的表达
3）RALF (rapid alkalinizaton factor):
•
激活 MAP kinase，但不诱导蛋白酶抑制蛋白编码
基因的表达;
•
快速引起 medium 碱化
4. 信号调控网络
From the followings support the idea that
peptide and nonpeptide hormone-activated
signaling cascades are linked in plants as they
•
•
are in animals:
植物生长素类似5－羟色胺，乙烯类似一氧化碳，
油菜素类固醇是类固醇，茉莉酮酸与前列腺素相关；
Systemin-induced wound response is
regulated through the octadecanoid pathway,
involving jasmonic acid;
•
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PSK-induced cell proliferation requires
the hormones auxin or cytokinin;
Some of the developmental distortions in
roots induced on addition of RALF are
reminiscent of impaired nonpeptide
hormone-controlled processes.
因此，揭开两种信号cascades之间关系，将是非常
有趣的事。
一、生物体内的信号传递
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6.2 IP3 system
Hermone/neurotransmitter
mAChR,EGFR,insulinR,adrenergicαR ,组胺R,5-羟色胺R,多肽激素R
激活蛋
白激酶
活性，
自身与
tyrosine
残基磷
酸化,促
进cell
AA
G protein
GC
PIP2
PLC
IP3+DAG
生长和
分化。
[cGMP]
CaM
[Ca2+]
多种酶及
依赖cGMP
的蛋白激
酶。
激活多种酶
和依赖cGMP
的蛋白激酶
而发挥生理
作用。
Ca2+ /CaM
PKC等蛋白激酶，磷
酸酯酶，核苷酸环化
酶，离子通道蛋白，
肌肉收缩蛋白等依赖
Ca2+ /CaM的蛋白。
PKC*
使各种受体，膜蛋白，收缩蛋白，细胞骨架蛋白，
核蛋白和酶类的丝氨酸或苏氨酸残基磷酸化，从而
影响细胞代谢、生长和分化。
二、海马趾CA1神经元区室化模型
中的15个信号途径
A:EGF,SOS
B:GEF,Ras
C:cAMP,AC1,AC2
D:G
E: AA, PLA2
F: PLC, PLC
G: DAG, IP3
H: MAPK Cascade
I: CaMKII
J: PKA
K: PKC
L: Ca, IP3
M: CaM
N: CaN
O: PP1
Reaction A:EGF,SOS
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Reaction B:GEF,Ras
Reaction C:cAMP,AC1,AC2
Reaction D:G
Reaction E: AA, PLA2
Reactions F,G: PLC, PLC, DAG, IP3
Reaction H: MAPK Cascade
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The various phosphorylation
states of CaMKII have
different enzyme kinetics,
and each of these were
explicitly modeled. For
simplicity the
autophosphorylation steps
are represented by a single
enzyme arrow in this figure,
with CaMKII_a as the
combined activity of the
various phosphorylation
states. The individual kinetic
terms used in the model are
indicated by the multiple rate
references on the arrows.
Reaction I: CaMKII
Reaction J: PKA
Reaction K: PKC
Reaction L: Ca, IP3
Reaction M: CaM
Reaction N: CaN
Reaction O: PP1
三、establishing the individual pathways
1. steps
1. Set up model activation of single component.
2.generate the model for an individual signaling pathway.
3. Obtain a good empirical model which fit the experimental data.
4.examine experimentally defined combination of 2 or
3 such individual signaling pathways.
5.test these combined models.
2. Materials and methord
(1). Hippocampal CA1 neuron(in GENSIS),
(2).NMDAR[on dendritic spine(树突棘) on the model]
(3).Synaptic input(3 tetanic bursts at 100HZ,1s each) →LTP
→Ca2+
waveforms
3. Computation formulation
Genesis formulation:
S + E <--k2---k1--> SE ---k3---> P + E
Vmax = max velocity = k3.
Substrate is saturating, so all of E is in SE form.
So Vmax.[Etot] = [SE].k3 == [Etot].k3
Km = (k3 + k2)/k1
k2 = k3 * 4
Kd=Kb/Kf
If [A]*[Bhalf]*Kf=[Chalf=Bhalf]*Kb
then [A]=Kb/Kf=Kd
Ka=Kf/Kb=1/Kd
4.verification
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(i). Model simple kinetic schemes
that could be calculated analytically,
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compare simulated results with analytical
results.
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(ii). Use the law of mass conservation and
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microscopic reversibility principles(微观可逆性
原理)
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→test accuracy in complex reaction schemes.
(iii). Run the same model at different time steps,
compare the resulting simulated values.
5. Protein Kinase C modeling example
Simulation parameters: PKC
Reaction K: PKC
References
Figure
Reac #
kf
kb
K
1
1
50
K
2
2E-10
0.1
K
3
1.2705
3.5026
K
4
0.000000002
0.1
K
5
1
0.1
K
6
2
0.2
K
7
0.000001
0.5
K
8
1.3333E-08
8.6348
K
9
0.000000001
0.1
K
10
0.00000003
2
References
1.
Review: Y. Nishizuka, Nature
334, 661 (1988)
Concs K: PKC
References
Figure
K
Name
PKC_inactive
2.
J. D. Schaechter and L. I.
Benowitz, J. Neurosci.13, 4361 (1993)
Conc
1
3.
T. Shinomura, Y. Asaoka, M.
Oka, K. Yoshida, Y. Nishizuka, Proc.
Natl. Acad. Sci. U.S.A. 88, 5149
(1991)
U. Kikkawa, Y. Takai, R. Minakuchi, S.
Inohara, Y. Nishizuka, J. Biol. Chem.
257, 13341 (1982).
A. Block diagram of activation for PKC pathway
by Ca2+, AA and DAG.
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built up simulations iteratively:
First: matched AA activation of
PKC at zero Ca.
Then: matched activation of PKC
with Ca at zero AA,
Third: matched the curves in B
with 1 uM Ca and varying AA.
Four: test the match for C, with
varying Ca and 50 uM AA.
Last:incorporated
DAG
interactions into the model.
B: Activation of PKC by AA, with (triangles) or
without (squares) 1 mM Ca2+.
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Open symbols and dashed
lines represent simulations,
solid symbols and solid
lines are experimental data.
Shows:Ca2+ is necessary
for the activation of PKC.
•experimental concentration-effect curves from two main sources:
•J. D. Schaechter and L. I. Benowitz, J. Neurosci. 13, 4361 (1993);
•T. Shinomura, Y. Asaoka, M. Oka, K. Yoshida, Y. Nishizuka, Proc. Natl. Acad. Sci.
U.S.A. 88, 5149 (1991)
C: Activation of PKC by Ca2+, with (triangles)
or without (squares) 50 mM AA.
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The curve in the presence of
50 mM AA (triangles) was
predicted
from
the
parameters obtained by
matching the curves in B and
the
curve
without
AA
(squares) in C, without
further adjustment.
D: Activation of PKC by DAG, with (triangles) or
without (squares) 50 mM AA.
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Both curves in D were
obtained in the presence of
1 mM Ca2+. Due to
different methods for
estimating DAG
concentrations the levels
of DAG used in the model
are scaled 15-fold up with
respect to the
experimental conditions
from Shinomura et al.
四、develope the network model in stages
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First : model individual pathways
Then: examin experimentally defined combinations of
two or three such individual pathways and test these
combined models against published data.
Third: repeat this process using larger assemblies of
pathways until the entire network model of interacting
pathways was
formed.
Pathways were linked by two kinds of interactions:
(i) Second messengers such as AA and DAG,
produced by one pathway were used as inputs to other
pathways.
(ii) Enzymes whose activation was regulated by one
pathway were coupled to substrates belonging to other
1、one Signaling pathways exampleS
(1).EGF’s stimulation of MAPK1,2
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Fig. 2. EGF
receptor
signaling pathways.
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(A). Block diagram of signaling
 pathways. Rectangles represent
enzymes, and circles represent
messenger
 molecules. This model used
modules shown in Fig. 1, reaction
A(EGF), B(Ca2+/CaM),
E(AA,PLA2),
H(PKC),F(PLCγ,DAG,IP3),
H(MAPK ascade), K(PKC),
I(CaMKII), L(Ca,IP3).
Fig.2B the time course of
activation of MAPK by EGF
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(B) Predicted (open triangle)
and experimental (filled triangles)
time course of response of
MAPK to a steady EGF stimulus
of 100 nM.
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the y axis represents fractional
activation.
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The fall in the MAPK activity
after the initial stimulation is due
to a combination of EGF
receptor internalization and
MAPK phosphorylation
and inactivation of SoS.
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1、one Signaling pathways exampleS
(2). Activation of PLCγ by Ca2+ in the presence
(triangles) or absence (squares) of EGF.
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(C) Concentration-effect
curves.
Dashed lines are model data,
and solid lines are
experimental data. The y axis
represents activation.
2、Two connected pathways
(1). Activation of the fractional feedback loop by EGF
receptor : (D) Activation of feedback loop by EGF.
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Three stimulus conditions:
10 min at 5 nM EGF (short bar,
circles),
100 min at 2 nM EGF (long
bar,squares),
100 min at 5 nM EGF (long bar,
triangles).
Only the third condition succeeds
in causing activation of the
feedback loop.
Why?
2.(1) Activation of the fractional feedback loop
by EGF receptor : (E) Bistability plot for feedback loop
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B (basal), T (threshold), and
A(active).
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Point A represents high activity
for
both PKC and MAPK, whereas
point B represents low activity.
Both of these points represent
distinct steady-state levels. Such
a system with two distinct steady
states is a bistable system. The
bifurcation point T is important
because it defines threshold
stimulation.
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2.(1) Activation of the fractional feedback loop by
EGF receptor : (F) estimated experimetal uncertainty in E
parameters
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Bistability is present
over
 a range comparable to
the experimental
uncertainty, indicating
that the
 phenomenon is robust.
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(Horizontal stripes: experimental
uncertainty in concentration;
diagonal
 stripes, simulated bistability range
for concentrations.)
 MAPK has a particularly large
uncertainty in concentration range
because of large differences in
tissue distributions.
2.(1)Activation of the fractional feedback loop by EGF
receptor: (G) Inactivation of feedback loop by MKP-1.
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initially activating: a suprathreshold
stimulus, and then one of three
inhibitory inputs was applied: 10 min
at 8 nM (short bar, circles), 20 min at 4
nM (long bar, squares), and 20 min at
8 nM (long bar, triangles.).
Only the third condition is able to
inactivate the feedback loop.
The rebound in the first two cases is
due to two factors: the persistence of
AA due to a relatively slow time
course of removal and the time course
of
dephosphorylation of activated
kinases in the MAPK cascade.
2.(1) Activation of the fractional feedback loop by EGF
receptor: (H) Thresholds for inactivation of feedback loop.
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MKP was applied for varying
times
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and amounts. At
high MKP
levels, inactivation
occurs more quickly, but
there is a minimum
threshold of nearly 10
min. Conversely, when
MKP is applied for very
long times, at least 2
nM MKP is required to
inactivate the feedback
loop.
Some conclusions for EGFR
signaling pathways
(1).100 nM EGF can activate MAPK.
 (2).Ca2+ activate PLCγ,which has more high activity
under 0.1uM EGF.
 (3).100 min at 5 nM EGF activated the feedback loop.
 (4).Activation of MAPK and PKC by EGF has a
threshold(point T).
 (5).The phenomenon is robust as comparing with Sim and
Expt on Km and Conc.
 (6).MPK-1(20 min,8nM) can inactivate the feedback loop.
 (7).High MKP level ,necessary for nearly 10 min.
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Long time application of MKP requires at least 2nM

MKP.
About bistable system
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(1). Such a bistable system has the potential to store
information. Signaling events [the initial stimulation (amplitude
and duration)] that push the levels of either activated PKC or
activated MAPK past the intersection point T will cause the
system to flip from one state to another. This analysis can be
generalized to any combination of pathways in a feedback
loop.
 (2). The emergent properties of this feedback system define
not only the amplitude and duration of the extracellular signal
required to activate the system but also the magnitude and
duration of processes such as phosphatase action required to
deactivate the system.
 (3). These properties make a feedback system, once
activated, capable of delivering a constant output in a manner
unaffected by small fluctuations caused by activating or
deactivating events.
 This capability to deliver a stimulus-triggered constant output
2.(2) CaMKII (Ca2+/calmodulin-dependent protein kinase II )
functions in LTP of synaptic responses in the
hippocampus.
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The cAMP pathway gates CaMKII
signaling through the regulation
of protein phosphatases.
NMDAR and Ca influx are
modeled in a compartmental
model of a CA1 neuron with a
series of three tetanic
stimuli at 100 Hz, lasting 1 s
each, separated by 10 min. This
model used modules shown in
Fig. 1, C,
I, J, M, N, and O(B to E).
Open squares: full model;
Filled triangles:cAMP(fixed at
resting concentrations → prevent
PKA activity ↑).
2.(2) (B) Activation of CaMKII.
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The initial increase in intracellular
Ca2+ caused an activation of CaMKII,
AC1,and CaN through CaM binding
and of PKA through increase in cAMP
produced through activation of AC1AC8.

cAMP ↑→ PKA activation→
PP1↓ → CaMKII ↑
The presence of a cAMPoperated gate leads to a large
increase in the amplitude of the
CaMKII response and
prolongation of its activity.
Nevertheless, it does not lead to
a persistent activation of CaMKII.
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2.(2) (C) Activation of PKA.
AC1-AC8 binding to Ca/CaM
↓
producing cAMP.
↓
PKA activity rises sharply
Otherwise,its activity: don’t rise
2.(2)
(D) Activity of PP1.
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[ Ca/CaM ↑ + cAMP(fixed)] → CaN
activation ↑ →smalltransients

cAMP fixed → PKA activation↓
cAMP unfixed → PKAactivation↑
→ PP1 activity↓
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Active PP1 →dephosphorylate
CaMKII(Thr286) →CaMKII ↓.
2.(2)
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(E) CaN (PP2B) activation by
Ca/CaM elevation.
The full model–cAMP fixed
curves overlap almost
erfectly.

↓
 CaN uninfluenced by
cAMP
四、3. A model for interaction between 4
signaling pathways: form a network
( PKC、 MAPK pathways + CaMKII、cAMP pathways )
Glu(+postsynaptic depolarization) →Ca2+ influx through NMDAR→ [Ca2+]↑
→postsynaptic PK(CaMKII,PKC,PKA,MAPK) ↑
四、3. Combined model with feedback loop,
synaptic input, and CaMKII activity and
Regulation.
cPLA2(held activity) →less AA→ FB
OFF
MKP(timer of FB in early LTP of synapse)→FBOFF
cPLA2(activity↑) → AA↑→ FB
ON
四、3. Activity profile of major
enzymes in pathway
Fig
B to G
▲：full
model(FBON)
□：feedback
blocked(FBOFF)
(AA fixed at resting
concentrations)
FBON : present feedback
FBOFF : absence feedback
四、3. (B) Activity profile of PKC
FB : →PKC↓
 FBON →larger successive


OFF
spikes
(initial spike+FBON )
→DAG+AA→PKC↑↑
四、3. (C)
Activity profile of MAPK
FBON →MAPK turn on
 FB →MAPK turn off

OFF

(initial spike+FBON ) →DAG+AA

→PKC↑↑→MAPK↑(steady)

四、3. (D) Activity profile of PKA.

Ca2+ inflow→AC1,8↑→PKA↑ Ca2+
→ identical PKA ↑
 FBON : PKC→ AC2↑→
cAMP↑→PKA ↑↑


sustained PKC→sustained
PKA activity
Several emergent properties of network

(1).Extended signal duration.
 (2).Activation of feedback loop.
 (3).Definition of threshold stimulation for

biological effects.
 (4).Multiple signal outputs.
四、3. (E) Activity profile of
CaMKII.
Ca2+ inflow→CaMKII↑
 Ca2+ → identical CaMKII ↑
 FBON : PKC→ AC2↑→
cAMP↑→PKA baseline
↑(twofold)
 PKA↑→PP1↓→CaMKII↑
 { [dephosphorylate CaMKII(Thr )]

286
→CaMKII autophosphorylation↓}
四、3. (F) Activity profile of PP1.
Ca2+→PP1↓

(overlap:FBON,FBOFF)

FBON→PKA↑(sustained)→P
P1↓→ PP1 (sustained)
 CaMKII↑

四、(G) Activity profile of CaN (PP2B).


FBOFF
or FBON：
CaN is
naffected
 →its’ effect on
PP1 limited to the
duration of the
initial signals.
On Network

(1).Network→sustained PK activity(after initial stimulus)

correspond to early LTP
 (2). MKP induction→ Other transcriptional events be initiated →

gene products→reach the active synapse with
MKP

(3).FBloop may gate incorporation of these products into the
cytoskeleton.

act as bridge between extremly short stimuli and
longer term synaptic change and also between local
synaptic events and cell wide production of synaptic
On the model
(1).Such a model facilitates “thought experiments” on

involved signaling pathways to predict
hierarchies.
 (2). The model also provides a framework for
understanding

biological consequences of multiple modes of

stimulating a single component.
 (3).Such models provide insights into the possible roles of

isoform diversity.

[CaM→AC1,PKC→AC2(connection,sustain CaMKII activation)]

On the model




(4).Limitations:
The biochemical parameters are not unaltered with
the cell. Given these uncertainties, models such as these
should not be considered as definitive descriptions of
networks within the cell, but rather as one approach that
allows us to understand the capabilities of complex
systems and devise experiments to test these capabilities.
(5). Conclusion:
simple biochemical reactions can, with appropriate
coupling, be used to store information. Thus, reactions
within signaling pathways may constitute one locus for the
biochemical basis for learning and memory.