Model Checking Gene Regulatory Networks
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.
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- 2.Baier, C., Katoen, J.-P.: Principles of model checking, pp. 1–975. MIT Press (2008)Google Scholar
- 4.Barrett, C.W., Sebastiani, R., Seshia, S.A., Tinelli, C.: Satisfiability modulo theories. Handbook of satisfiability 185, 825–885 (2009)Google Scholar
- 9.Cardelli, L., Csikász-Nagy, A.: The cell cycle switch computes approximate majority. Scientific reports 2 (2012)Google Scholar
- 10.Ciliberti, S., Martin, O.C., Wagner, A.: Robustness can evolve gradually in complex regulatory gene networks with varying topology. PLoS Computational Biology 3(2) (2007)Google Scholar
- 14.Giacobbe, M., Guet, C.C., Gupta, A., Henzinger, T.A., Paixao, T., Petrov, T.: Model checking gene regulatory networks. arXiv preprint arXiv:1410.7704 (2014)Google Scholar
- 19.Rizk, A., Batt, G., Fages, F., Soliman, S.: A general computational method for robustness analysis with applications to synthetic gene networks. Bioinformatics 25(12), i169–i178 (2009)Google Scholar
- 20.Schlitt, T., Brazma, A.: Current approaches to gene regulatory network modelling. BMC Bioinformatics 8(Suppl 6), S9 (2007)Google Scholar
- 23.Zhang, L., Madigan, C.F., Moskewicz, M.H., Malik, S.: Efficient conflict driven learning in a boolean satisfiability solver. In: Computer Aided Verification, pp. 279–285. IEEE Press (2001)Google Scholar