Validation of Qualitative Models of Genetic Regulatory Networks A Method Based on Formal Verification Techniques Grégory Batt Ph.D. defense -- under supervision of Hidde de Jong, Helix research group INRIA Rhône-Alpes -- Ecole doctorale Mathématiques, Sciences et technologies de l’information, Informatique Université Joseph Fourier
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Validation of Qualitative Models of Genetic Regulatory Networks A Method Based on Formal Verification Techniques Grégory Batt Ph.D. defense -- under supervision.
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Validation of Qualitative Models of Genetic Regulatory Networks
A Method Based on Formal Verification Techniques
Grégory BattPh.D. defense
--
under supervision of Hidde de Jong,
Helix research group
INRIA Rhône-Alpes
--
Ecole doctorale
Mathématiques, Sciences et technologies de l’information, Informatique
Université Joseph Fourier
Stress response in Escherichia coli
Bacteria capable of adapting to a variety of changing environmental conditions
Stress response in E. coli has been much studied
Model for understanding adaptation of pathogenic bacteria to their host
Nutritional stress
Osmotic stress
Heat shock
Cold shock
…
Nutritional stress response in E. coli
Response of E. coli to nutritional stress conditions: transition from exponential phase to stationary phase
Important developmental decision: profound changes of morphology,
metabolism, gene expression,...
log (pop. size)
time
> 4 h
Network controlling stress response Response of E. coli to nutritional stress conditions controlled by
genetic regulatory networkDespite abundant knowledge on network components, no global view of
functioning of network available
rrnP1 P2
CRP
crp
cya
CYA
cAMP•CRP
FIS
TopA
topA
GyrAB
P1-P4P1 P2
P2P1-P’1
P
gyrABP
Signal (carbon starvation)
DNA supercoiling
fis
stable RNAs
protein
gene
promoter
Modeling and simulation
Genetic regulatory network controlling E. coli stress response is large and complex
Modeling and simulation indispensable for dynamical analysis of genetic regulatory networks
Systematic prediction of possible network behaviors
Current constraints on modeling and simulation: knowledge on molecular mechanisms rare quantitative information on kinetic parameters and molecular
concentrations absent
Qualitative methods developed for analysis of genetic networks using coarse-grained models
Model validation
Available information on structure of network controlling E. coli stress response is incomplete
Model is working hypothesis and needs to be tested
Model validation is prerequisite for use of model as predictive and explanatory tool
Check consistency between model predictions and experimental
data
consistency?experimental datanetwork predictions
x = f (x) .
model
Model validation
Available information on structure of network controlling E. coli stress response is incomplete
Model is working hypothesis and needs to be tested
Model validation is prerequisite for use of model as predictive and explanatory tool
Check consistency between model predictions and experimental
data
Current constraints on model validation:
available experimental data essentially qualitative in nature
model validation must be automatic and efficient
Objectives and approach of thesis
Objective of thesis:
Development of automated and efficient method for testing whether
predictions from qualitative models of genetic regulatory networks are
consistent with experimental data on dynamics of system
Approach based on formal verification of hybrid systems qualitative analysis of piecewise-linear models of genetic networks
model checking for testing consistency between predictions and data
Expected contributions: scalable method with sound theoretical basis
implementation of method in user-friendly computer tool
applications to validation of models of networks of biological interest
2. Symbolic analysis using qualitative abstraction
3. Verification of properties by means model-checking techniques
III. Genetic Network Analyzer 6.0
IV. Validation of model of nutritional stress response in E. coli
V. Discussion and conclusions
Summary
Development of automated and efficient method for testing whether predictions from qualitative models of genetic regulatory networks are consistent with experimental data on system dynamics
Use of discrete abstraction that yields predictions well-adapted to comparison with available experimental data
Combination of tailored symbolic analysis and model checking for verification of dynamical properties of hybrid models of large and complex networks
Biological relevance demonstrated on validation of models of networks of biological interest Batt et al., Bioinformatics, 05
Batt et al., IJCAI, 05
Batt et al., HSCC, 05
Discussion
Discrete abstractions used for analysis of continuous and hybrid models
symbolic reachability analysis of hybrid automata models more precise analysis of system dynamics need for complex decision procedures no treatment of discontinuities in vector field
qualitative simulation using qualitative differential equations more general class of model methods are not scalable
Model checking used for analysis of discrete models verification of properties of logical models
intuitive connection between underlying continuous dynamics and discrete representation
no explicit representation of dynamical phenomena at threshold concentrations
Ghosh and Tomlin,
Systems Biology, 04
Heidtke and Schulze-Kremer,
Bioinformatics, 98
Bernot et al.,
J. Theor. Biol., 04
Perspectives
Further integration of model-checking task into GNA
Property specification, verification, interpretation of diagnostics
Exploitation of advanced model-checking techniques
Partial order reduction, graph minimization, modular model checking, ...
Extensions of model validation
model inference: complete partially-specified models
model revision: modify inconsistent models
network design: find model satisfying set of design constraints