Statistical Model Checking in BioLab: Applications to the Automated Analysis of T-Cell Receptor Signaling Pathway

Authors:
Edmund M. Clarke;James R. Faeder;Christopher J. Langmead;Leonard A. Harris;Sumit Kumar Jha;Axel Legay
Affiliations:
Computer Science Department, Carnegie Mellon University, Pittsburgh,;Department of Computational Biology, University of Pittsburgh School of Medicine, Pittsburgh,;Computer Science Department, Carnegie Mellon University, Pittsburgh,;Department of Computational Biology, University of Pittsburgh School of Medicine, Pittsburgh,;Computer Science Department, Carnegie Mellon University, Pittsburgh,;Computer Science Department, Carnegie Mellon University, Pittsburgh,
Venue:
CMSB '08 Proceedings of the 6th International Conference on Computational Methods in Systems Biology
Year:
2008

Citing 15
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Abstract

We present an algorithm, called BioLab, for verifying temporal properties of rule-based models of cellular signalling networks.BioLabmodels are encoded in the BioNetGenlanguage, and properties are expressed as formulae in probabilistic bounded linear temporal logic. Temporal logic is a formalism for representing and reasoning about propositions qualified in terms of time. Properties are then verified using sequential hypothesis testing on executions generated using stochastic simulation. BioLabis optimal, in the sense that it generates the minimum number of executions necessary to verify the given property. BioLabalso provides guarantees on the probability of it generating Type-I (i.e., false-positive) and Type-II (i.e., false-negative) errors. Moreover, these error bounds are pre-specified by the user. We demonstrate BioLabby verifying stochastic effects and bistability in the dynamics of the T-cell receptor signaling network.