Abstract
The behaviour of gene regulatory networks (GRNs) is typically analysed using simulation-based statistical testing-like methods. In this paper, we demonstrate that we can replace this approach by a formal verification-like method that gives higher assurance and scalability. We focus on Wagner’s weighted GRN model with varying weights, which is used in evolutionary biology. In the model, weight parameters represent the gene interaction strength that may change due to genetic mutations. For a property of interest, we synthesise the constraints over the parameter space that represent the set of GRNs satisfying the property. We experimentally show that our parameter synthesis procedure computes the mutational robustness of GRNs -an important problem of interest in evolutionary biology- more efficiently than the classical simulation method. We specify the property in linear temporal logics. We employ symbolic bounded model checking and SMT solving to compute the space of GRNs that satisfy the property, which amounts to synthesizing a set of linear constraints on the weights.
This research was supported by the European Research Council (ERC) under grant 267989 (QUAREM), the Austrian Science Fund (FWF) under grants S11402-N23 (RiSE) and Z211-N23 (Wittgenstein Award), the European Union’s SAGE grant agreement no. 618091, ERC Advanced Grant ERC-2009-AdG-250152, the People Programme (Marie Curie Actions) of the European Union’s Seventh Framework Programme (FP7/2007-2013) under REA grant agreement no. 291734, and the SNSF Early Postdoc.Mobility Fellowship, the grant number P2EZP2_148797.
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Giacobbe, M., Guet, C.C., Gupta, A., Henzinger, T.A., Paixão, T., Petrov, T. (2015). Model Checking Gene Regulatory Networks. In: Baier, C., Tinelli, C. (eds) Tools and Algorithms for the Construction and Analysis of Systems. TACAS 2015. Lecture Notes in Computer Science(), vol 9035. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-46681-0_47
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