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Transcript of Bioinformatics 3 V18 – Kinetic Motifs Mon, Jan 12, 2015.
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Bioinformatics 3
V18 – Kinetic Motifs
Mon, Jan 12, 2015
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Bioinformatics 3 – WS 14/15 V 18 – 2
Modelling of Signalling Pathways
Curr. Op. Cell Biol. 15 (2003) 221
1) How do the magnitudes of signal output and signal duration depend on the kinetic properties of pathway components?(2) Can high signal amplification be coupled with fast signaling?
(3) How are signaling pathways designed to ensure that they are safely off in the absence of stimulation, yet display high signal amplification following receptor activation?(4) How can different agonists stimulate the same pathway in distinct ways to elicit a sustained or a transient response, which can have dramatically different consequences?
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Bioinformatics 3 – WS 14/15 V 18 – 3
Linear Response
E.g., protein synthesis and degradation (see lecture V8)
S = signal (e.g., concentration of mRNA)
R = response (e.g., concentration of a protein)
SRSR
At steady state (which implies S = const):
=>
0 1 20
1
2
S
RS
S
RSS linearly dependent on Sk0 = 1, k1 = k2 = 2
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Bioinformatics 3 – WS 14/15 V 18 – 4
S
RP
SS
phosphorylation/dephosphorylation
„forward“: R is converted to phosphorylated form RP„backward“: RP can be dephosphorylated again to R
RRPSTRRPST
S + R => RP
RP => R + Twith Rtot = R + RP
Find steady state for RP: linear until saturation
0.01 0.1 1 10 1000.01
0.1
1
Rtot = 1, S0 = 1
Output T proportional to RP level:
phosphorylated form
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Bioinformatics 3 - WS 14/15 5
Enzyme: Michaelis-Menten-kinetics
Reaction rate:
Steady state:
SE
EST
kon koff
Total amount of enzyme is constant:
=>
turnover:
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Bioinformatics 3 - WS 14/15 6
The MM-equation
Effective turnover according to MM:
Pro: • analytical formula for turnover• curve can be easily interpreted: Vmax, KM
• enzyme concentration can be ignored
Cons: less kinetic information kon, koff, ET => Vmax, KM
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Bioinformatics 3 – WS 14/15 V 18 – 7
Sigmoidal Characteristics with MM kinetics
0 1 2 30
2
4
6
8
10
RRPSTRRPST Same topology as before with Michaelis-Menten kinetics for phosphorylation and dephosphorylation.
Quadratic equation for RP
S
RP
SS
Rt = 10, R0 = RP0 = 1, k1 = k2 = 1
=> sigmoidal characteristics (threshold behavior)often found in signalling
cascades
this means that S = Rt - RP KM = R0
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Bioinformatics 3 – WS 14/15 V 18 – 8
Graded Response
0 1 20
1
2
S
RS
S
S
RP
SS
0.01 0.1 1 10 1000.01
0.1
1
0 1 2 30
2
4
6
8
10
S
RP
SS
Linear, hyperbolic, and sigmoidal characteristic give the same steady state response independent of the previous history=> no hysteresis
BUT: In fast time-dependent scenarios,delay may lead to a modified response
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Bioinformatics 3 – WS 14/15 V 18 – 9
Time-dependent Sigmoidal Response
Direct implementation:RRPSTRRPST
Parameters: k1 = 1 (mol s)–1, k2 = 1 s–1, R0 = RP0 = 1 molInitial conditions: R = 10 mol, RP = 0
Time courses for S = 1, 1.5, and 2,RP(0) = 0:
equilibrium is reachedfaster for stronger signal
t
RP(t
)
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Bioinformatics 3 – WS 14/15 V 18 – 10
Adaption - „sniffer“SRXSRX Linear response modulated by a second species X
Steady state: Rss independent of S
S
k1 = 30, k2 = 40, k3 = k4 = 5
0 1 2 3 4 50
1
2 S
R
X
R changes transiently when S changes, then goes back to its basal level.found in smell, vision, chemotaxis, …
Note: response strength ΔR depends on rate of change of S.=> non-monotonous relation for R(S)
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Bioinformatics 3 – WS 14/15 V 18 – 11
Positive Feedback
Feedback via R and EP=> high levels of R will stay
"one-way switch" via bifurcation
Found in processes that are "final":frog oocyte maturation, apoptosis, …
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Bioinformatics 3 – WS 14/15 V 18 – 12
Mutual Inhibition - Toggle Switch
Sigmoidal "threshold" in E <=> EP leads to bistable response (hysteresis): toggle switchConverts continuous external stimulus into two well defined stable states:• lac operon in bacteria• activation of M-phase promoting factor in frog eggs
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Bioinformatics 3 – WS 14/15 V 18 – 13
Negative Feedback
S controls the "demand" for R
=> homeostasis found in biochemical pathways, no transient changes in R for steps in S (cf. "sniffer")
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Bioinformatics 3 – WS 14/15 V 18 – 14
Negative Feedback with Delay
Cyclic activation X => YP => RP => X=> Oscillations (in a range of S)
Proposed mechanism for circadian clocks
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Bioinformatics 3 – WS 14/15 V 18 – 15
Circadian Clocks
Ko & Takahashi Hum Mol Genet 15, R271 (2006)
CK1: casein kinase
Rev-erb, ROR: retinoic acid-related orphan nuclear receptors
Cdg: clock-controlled gene(s)
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Bioinformatics 3 – WS 14/15 V 18 – 16
Substrate-Depletion Oscillations
R is produced in an autocatalytic reaction from X, finally depleting X…
Similar to Lotka-Volterra system (autocatalysis for X, too):
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Bioinformatics 3 – WS 14/15 V 18 – 17
The Cell Cycle
Cell division(cytokinesis)
DNA separatio
n(mitosis)
DNA replication
cell growt
h
M-phase
S-phaseG2-phase
G1
-phase
When to take the next step???
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Bioinformatics 3 – WS 14/15 V 18 – 18
Cell Cycle Control
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Bioinformatics 3 – WS 14/15 V 18 – 19
Cell Cycle Control System
Tyson et al, Curr. Op. Cell Biol. 15 (2003) 221
cdc = "cell division cycle"
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Bioinformatics 3 – WS 14/15 V 18 – 20
Feedback loops control cell cycle
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Bioinformatics 3 – WS 14/15 V 18 – 21
G1 => S — Toggle Switch
Mutual inhibition between Cdk1-CycB and CKI(cyclin kinase inhibitor)
Tyson et al, Curr. Op. Cell Biol. 15 (2003) 221
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Bioinformatics 3 – WS 14/15 V 18 – 22
Mutual Inhibition
???
Assume: CycB:Cdk1:CKI is stable <=> dissociation is very slow
=> same topology <=> same bistable behavior (?)
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Bioinformatics 3 – WS 14/15 V 18 – 23
Rate Equations: Toggle Switch
A XR1
R2
R3
R4
Stoichiometric matrix"(C)" = catalyst
R1 R2 R3 R4
A –1
S (C)
R 1 –1 (C)
E (C) –1 1
EP 1 –1
X 1
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Bioinformatics 3 – WS 14/15 V 18 – 24
Rate Equations: G1/S ModuleR1 R2 R3 R4 R5 R6
CycB –1
Cdk1 –1
CycB:Cdk1 1 –1 (C) 1
CKI –1 –1 1 1
CKI:P3 1 –1
CKI:P3 –1
CycB:Cdk1:CKI
1 -1
R1 R2
R3
R4R5
R6
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Bioinformatics 3 – WS 14/15 V 18 – 25
Comparison: Matrices
A XR1
R2
R3
R4
R1 R2 R3 R4
A –1
S (C)
R 1 –1 (C)
E (C) –1 1
EP 1 –1
X 1
R1 R2
R3
R4R5
Difference: catalysts vs. substrates
R1 R2 R3 R4 R5 R6
CycB –1
Cdk1 –1
CycB:Cdk1 1 –1 (C) 1
CKI –1 –1 1 1
CKI:P3 1 –1
CKI:P3 –1
CycB:Cdk1:CKI
1 -1
R6
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Bioinformatics 3 – WS 14/15 V 18 – 26
Comparison: Equations
A XR1
R2
R3
R4
R1
R2
R3
R4
R5
Rename species => same rate equations => same behavior
R6
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Bioinformatics 3 – WS 14/15 V 18 – 27
Predicted Behavior: G1 => SSignal: cell growth = concentration of CycB, Cdk1
Response: activity (concentration) of CycB:Cdk1
Toggle switch:=> above critical cell size CycB:Cdk1 activity will switch on
Tyson et al, Curr. Op. Cell Biol. 15 (2003) 221
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Bioinformatics 3 – WS 14/15 V 18 – 28
G2 => M
Toggle switch:• mutual activation between CycB:Cdk1 and Cdc25 (phosphatase that activates the dimer)• mutual inhibition between CycB:Cdk1 and Wee1 (kinase that inactivates the dimer)
=>when the cell grows further during the second gap phase G2, the activity of CycB:Cdk1 will increase by a further step
Tyson et al, Curr. Op. Cell Biol. 15 (2003) 221
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Bioinformatics 3 – WS 14/15 V 18 – 29
M => G1
Negative feedback looposcillator
i) CycB:Cdk1 activates anaphase promoting complex (APC)ii) APC activates Cdc20iii) Cdc20 degrades CycB
Behavior:at a critical cell size CycB:Cdk1 activity increases and decreases again=> at low CycB:Cdk1 level, the G1/S toggle switches off again, => cell cycle completed
Tyson et al, Curr. Op. Cell Biol. 15 (2003) 221
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Bioinformatics 3 – WS 14/15 V 18 – 30
Overall Behavior
Cell divides at size 1.46
=> daughters start growing from size 0.73
=> switches to replication at size 1.25
G1/S toggle => bistability
G2/M toggle => bistability
M/G1 oscillator
Tyson et al, Curr. Op. Cell Biol. 15 (2003) 221
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Bioinformatics 3 – WS 14/15 V 18 – 31
Preventing Cross-Talk
Many enzymes are used in multiple pathways
=> how can different signals cross the same kinase?
=> different temporal signature (slow vs. transient)=> Dynamic modelling!
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Bioinformatics 3 – WS 14/15 V 18 – 32
Summary
Today:
Behavior of cell cycle control circuitry from its modules:two toggle switches + one oscillator=> map biological system onto motif via • stoichiometric matrices • rate equations