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Constraint-based modeling in systems biology Alexander Bockmayr WCB-10, 21 July 2010 DFG Research Center Matheon Mathematics for key technologies

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Page 1: Constraint-based modeling in systems biology · 2015-07-28 · Constraint-based modeling in systems biology • State constraints on a biological network structure/topology dynamics

Constraint-based modeling in systems biology

Alexander Bockmayr

WCB-10, 21 July 2010

DFG Research Center MatheonMathematics for key technologies

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Alexander Bockmayr, FU Berlin/Matheon 2

Outline

I. Systems biology

II. Constraint-based modeling

III. Regulatory networks: structure

IV. Regulatory networks: dynamics

V. Temporal logic and model checking

VI. Temporal constraints and time delays

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I. Systems biology

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Alexander Bockmayr, FU Berlin/Matheon 4

Molecular biology

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Systems biology

Molecularbiology

Systemsbiology

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Molecular networks

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Modeling in systems biology

Various network types• metabolic• regulatory• signaling, …

Various modeling approaches• continuous (ordinary/partial differential equations)• stochastic (chemical master equation) • discrete (logic, Petri nets, process calculi, …)• hybrid (continuous/stochastic, discrete/continuous)

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Traditional approach

• Kinetic modeling

• Deterministic or stochastic mathematical model

• Numerical simulation (ODE, Gillespie, …)

• Requires detailed knowledge of the network (rate laws, kinetic parameters, …)

• Often not available

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II. Constraint-based modeling

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Constraint-based methods in systems biology

Palsson, Nature Biotech.,2000

“ Because biological information is incomplete, it is necessary to take into account the fact that cells are subject to certain constraints that limit their possible behaviors. By imposing these constraints in a model, one can then determine what is possible and what is not, and determine how a cell is likely to behave, but never predict its behavior precisely.”

COBRA toolbox

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Alexander Bockmayr, FU Berlin/Matheon 11

Constraints in computer science

Constraint system = system of inference with pieces of partial information

… in systems biology

Saraswat´89

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Constraint-based modeling in systems biology

• State constraints on a biological networkstructure/topologydynamics

• Make inferences about the set of possible behaviors non-determinism

• Forward + backward reasoning structure dynamicsdynamics structure

• Formal reasoning vs. numerical simulation

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Constraint-based modeling of regulatory networks

Discrete modeling formalism of René Thomas (1973)• Interaction graphs Structure• State transition graphs Dynamics

Constraint-based analysis of the dynamics• Temporal logic• Model checking Formal reasoning

Adding time delays• Temporal constraints• Hybrid discrete-continuous modeling

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Alexander Bockmayr, FU Berlin/Matheon 14

III. Regulatory networks: structure

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Regulatory network

http://www.zaik.uni-koeln.de/AFS/

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Interaction graph

+_

_+

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Regulatory networks

Regulatory components: Variables

represents activity level of component j.

Regulatory interactions: Activation/inhibition

Component i is influenced by component j only if the activity level is above a certain threshold .

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Thomas/Snoussi 88

If component j acts on nj other components

(up to) nj thresholds :

: activity level of component j is above

the k-th threshold and below the (k+1)-th.

Thresholds and activity levels

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: weight given to the action on

Xi by Xj

: weight given to the

common action on Xi by Xj and Xk

finitely many possible parameter values

discrete update function

Logical parameters

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Annotated interaction graph

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Alexander Bockmayr, FU Berlin/Matheon 21

IV. Regulatory networks: dynamics

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Asynchronous dynamics

Thomas 73, Thomas/Snoussi 89

State

State transitions

if resp.

(where is the update value for ).

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Synchronous vs. asynchronous

Only one variable updated at a time.

Nondeterminism: Several successor states possible

asynchronous synchronous

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State transition graph

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Set of possible behaviors

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Stable states and cycles

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Example

• X1 ∈ {0,1}• X2 ∈ {0,1,2}• Assume θ12< θ22, i.e., when

activated, X2 acts first on X1, then on itself.

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K12=1, K21 = 0, K22 = K21+22 = 2

2 stable states

no cycle

2 separate domains

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K12=1, K21 = 1, K22 = K21+22 = 2

1 stable state

1 cycle

2 separate domains

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Continuous model

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V. Temporal logic and model checking

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State transition graph (Kripke model)

exponentially large

check dynamics properties expressed in suitable temporal logic

Model checking

p q

r

q rp q

p q

r

rr

q r

Infinite computation tree

Clarke/Emerson and Sifakis 81

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Computation Tree Logic (CTL)

Atomic formulae : p, q, r, …, e.g.

Linear time operators :• X p : p holds next time• F p : p holds sometimes in the future• G p : p holds globally in the future• p U q : p holds until q holds

Path quantifiers :• A : for every path• E : there exists a path

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Linear time operators

p

Xp

Fp

Gp

pUq

p

p

p p p p p p

p p p q

Nowp

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Path quantifiers

AGp p

p

p

p

p p p

AFp

p

p p

EGp p

p

p

EFp

p

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Input• Interaction graph / state transition graph• Temporal logic formula (CTL)

Output

Set of states in which the formula is true

Very efficient software available (e.g. NuSMV)

Forward reasoning: Structure dynamics

What are the possible trajectories/dynamics compatible with the given structure ?

CTL model checking

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Specify dynamic properties using CTL formulas

Find compatible logical parameter values (SMBioNet)

Alternative: Infer constraints on logical parameters

Backward reasoning: Dynamics structure

Network inference

Bernot/Comet/Richard/Guespin 04

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VI. Temporal constraints on time delays

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Time delays

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Reducing non-determinism

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Temporal constraints

• Time delays may depend on • the component• the activity level• activation/inhibition

• Temporal constraints on time delays

• Temporal constraints along a pathway

• Hybrid discrete/continuous modeling

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Formal analysis using timed automataSiebert/Bockmayr 08

reduce non-determinism

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Biological applications

• Elimination of pathways violating temporal constraints

• Feasibility, stability of certain dynamic behaviors

• Additional information on gene activitystationaryintermediate

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Conclusion

• Constraint-based modeling of regulatory networks

• Interaction graph Structure

• State transition graph Dynamics

• Temporal logic and model checking

• Reducing non-determinism: time delays, temporal constraints, hybrid automata

• Deterministic/stochastic vs. constraint-based modeling

• Numerical simulation vs. formal reasoning

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