Abstract
Boolean networks are important models of gene regulatory networks. Such models are sometimes built from: (1) a gene interaction graph and (2) a set of biological constraints. A gene interaction graph is a directed graph representing positive and negative gene regulations. Depending on the biological problem being solved, the set of biological constraints can vary, and may include, for example, a desired set of stationary states. We present a symbolic, SAT-based, method for inferring synchronous Boolean networks from interaction graphs augmented with constraints. Our method first constructs Boolean formulas in such a way that each truth assignment satisfying these formulas corresponds to a Boolean network modeling the given information. Next, we employ a SAT solver to obtain desired Boolean networks. Through a prototype, we show results illustrating the use of our method in the analysis of Boolean gene regulatory networks of the Arabidopsis thaliana root stem cell niche.
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Rosenblueth, D.A., Muñoz, S., Carrillo, M., Azpeitia, E. (2014). Inference of Boolean Networks from Gene Interaction Graphs Using a SAT Solver. In: Dediu, AH., Martín-Vide, C., Truthe, B. (eds) Algorithms for Computational Biology. AlCoB 2014. Lecture Notes in Computer Science(), vol 8542. Springer, Cham. https://doi.org/10.1007/978-3-319-07953-0_19
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DOI: https://doi.org/10.1007/978-3-319-07953-0_19
Publisher Name: Springer, Cham
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