Abstract
Deciphering the developmental program of an embryo is a fundamental question in biology. Landmark papers [9,10] have recently shown how computational models of gene regulatory networks provide system-level causal understanding of the developmental processes of the sea urchin, and enable powerful predictive capabilities. A crucial aspect of the work is empirically deriving plausible models that explain all the known experimental data, a task that becomes infeasible in practice due to the inherent complexity of the biological systems. We present a generic Satisfiability Modulo Theories based approach to analyze and synthesize data constrained models. We apply our approach to the sea urchin embryo, and successfully improve the state-of-the-art by synthesizing, for the first time, models that explain all the experimental observations in [10]. A strength of the proposed approach is the combination of accurate synthesis procedures for deriving biologically plausible models with the ability to prove inconsistency results, showing that for a given set of experiments and possible class of models no solution exists, and thus enabling practical refutation of biological models.
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Paoletti, N., Yordanov, B., Hamadi, Y., Wintersteiger, C.M., Kugler, H. (2014). Analyzing and Synthesizing Genomic Logic Functions. In: Biere, A., Bloem, R. (eds) Computer Aided Verification. CAV 2014. Lecture Notes in Computer Science, vol 8559. Springer, Cham. https://doi.org/10.1007/978-3-319-08867-9_23
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DOI: https://doi.org/10.1007/978-3-319-08867-9_23
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