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Scalable Multi-core Model Checking: Technology & Applications of - - PowerPoint PPT Presentation

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UNIVERSITY OF TWENTE. Formal Methods & Tools. Scalable Multi-core Model Checking: Technology & Applications of Brute Force Part IV: Biology Jaco van de Pol 30, 31 October 2014 VTSA 2014, Luxembourg ... Signalling ANIMO In Silico

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Scalable Multi-core Model Checking: Technology & Applications of Brute Force Part IV: Biology UNIVERSITY OF TWENTE.

Formal Methods & Tools. Jaco van de Pol 30, 31 October 2014

VTSA 2014, Luxembourg

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Table of Contents

1 Modeling Signaling Networks in Cell Biology 2 ANIMO: Interactive Modeling and Analysis 3 In Silico Experiment: Osteoarthritis

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Signaling Pathway in Cell Biology

Kinase pathways: spreading the Phosphor token

◮ Biochemical equilibrium reactions:

◮ E + S + ATP ⇋ ES + ATP → ESP + ADP ⇋ E + SP + ADP

◮ Simplify to one interaction (here activiation): E −

→ S

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Signaling Network in Cell Biology

Complex network dynamics

◮ Node interactions:

◮ activation ◮ inhibition

◮ Crosstalk and Feedback ◮ Ultimate questions:

◮ understand & control ◮ key to finding a cure

  • f “system” diseases

◮ cancer, diabetes,

arthritis

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How to model signaling networks?

Mathematical models (ODE)

[Gillespie ’77]

dA dt = k1·B − k2·C − k3·A ◮ A, B, C are molecule concentrations ◮ k1, k2, k3 are kinetic parameters ◮ Precise, strong tools (simulation, stability) ◮ Difficult, too many parameters are unknown

Boolean networks

[Kauffman’69]

B ∧ ¬C = ⇒ A

◮ Easy to handle, biologically relevant ◮ No timing, no concentrations at all ◮ So how to execute this?

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Simplified version of Timed Automata

goDown? goUp? reactant := reactant + 1 reactant := reactant - 1 reacting notReacting goDown? c := 0 goUp! c >= LB && reactant >= MAX - 1 c <= UB goUp! c := 0 c >= LB && reactant < MAX - 1 reacting notReacting goUp? c := 0 goDown! c >= LB && reactant <= 1 c <= UB goDown! c := 0 c >= LB && reactant > 1

Basic modeling ideas

◮ Discretized activity levels ◮ Clocks constrained by upperbound and lowerbound ◮ Activation/Deactivation is communicated over channels

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Using Networks of Timed Automata

Modeling Assumptions

◮ Every reactant is modeled by a Timed Automaton ◮ It maintains a discrete activation level:

  • active

active+inactive

  • ◮ Clocks trigger when the activation level goes up or down

◮ Activation/Inhibition: broadcast communication between automata

reacting[3]? reacting[0]! reacting[1]? reacting[2]? reacting[1]? stubborn update(), c:= 0 update(), c:= 0 reacting[2]? react(), c := 0 reacting[3]? update(), c:= 0 cant_react() c >= T c < T can_react() c >= T start updating not_reacting c > T waiting c <= T c <= T update() update() c := T

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time T depends on activation levels: L[r1][r2] and U[r1][r2]

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Table of Contents

1 Modeling Signaling Networks in Cell Biology 2 ANIMO: Interactive Modeling and Analysis 3 In Silico Experiment: Osteoarthritis

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ANIMO: Analysis of Networks by Interactive Modeling

Schivo, Scholma, Karperien, Langerak, vdPol, Post, Urquidi, Vet, Wanders, (FMT, HMI, BioEng) [BIBE’12] [GENE’13] [J-BHI’14]

ANIMO is a Cytoscape plugin, running UPPAAL in the background

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ANIMO workflow

Draw topology, initial conditions, and investigate the behaviour Node colors/edges show activation level; view as graphs, heatmap.

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Model Validation by Wet-Lab Experiments

Phosphorylation of proteins in human chondrocytes: Time series under three experimental conditions.

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Validation by Wet-Lab Experiments

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Table of Contents

1 Modeling Signaling Networks in Cell Biology 2 ANIMO: Interactive Modeling and Analysis 3 In Silico Experiment: Osteoarthritis

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Osteoarthritis

Osteoarthritis

◮ Mesenchymal stem cells can

differentiate to

◮ either Osteoblasts (bone) ◮ or Chondrocytes (cartilage)

◮ Osteoarthritis: articular cartilage

dries, wears out, forms bone

◮ Pain in “bone-to-bone” joints ◮ 60% of the population (> 65 years)

will show symptoms

◮ Characterized by transcription

factors: SOX9 or RUNX2

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Development of Chondrocyte (cell fate)

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ECHO: the Executable Chondrocyte

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Put ECHO in ANIMO

Size

◮ 7 inputs ◮ 123 nodes ◮ 354 links ◮ Sox9, Runx2

as output Starting point: Boolean Network Kerkhofs et al (U Leuven), PLoS One 7(4), 2012

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Validation by simulating results from literature

Basic validation by simulation

◮ Exhaustive / Monte Carlo simulation with 37 input conditions ◮ Knock-out or overexpress individual nodes in the network ◮ There are only two stable

states (SOX9 and RUNX2)

◮ WNT protein pushes

SOX9 stable states to RUNX2

◮ DKK, FRZB and GREM

stabilize healthy cartilage

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In search for new knowledge: parameter sweeps

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Multi-core Model Checking for Biological Applications?

Key questions (biological relevant answers)

◮ Which input combination/series causes a switch RUNX2 → SOX9 ◮ Which interactions should be inhibited to prevent SOX9 → RUNX2

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The Empirical Research Cycle/Spiral

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Literature on ANIMO

Signaling Networks in Biology

◮ http://fmt.cs.utwente.nl/tools/animo/ ◮ Stefano Schivo, Jetse Scholma, B. Wanders, R. Urquidi, P. van der Vet,

  • M. Karperien, R. Langerak, J. van de Pol, J.N. Post, (BIBE’12, J-BHI’14)

Modelling biological pathway dynamics with Timed Automata

◮ Jetse Scholma, Stefano Schivo, R. Urquidi, J. van de Pol, M. Karperien,

  • J. Post, . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . GENE 533 (2013)

Biological networks 101: computational modeling for molecular biologists

◮ Stefano Schivo, Jetse Scholma, Marcel Karperien, Janine N. Post, Jaco

van de Pol, Rom Langerak, . . . . . . . . . . . . . . . . . . . . . . . . . . . . (SynCoP 2014) Setting Parameters for Biological Models With ANIMO

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