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Genetic interaction can be defined as a deviation of the phenotypicquantitative effect of a double gene mutation from the effect predicted fromsingle mutations using a simple (e.g., multiplicative or linear additive)statistical model. Experimentally characterized genetic interaction networks inmodel organisms provide important insights into relationships between differentbiological functions. We describe a computational methodology allowing tosystematically and quantitatively characterize a Boolean mathematical model ofa biological network in terms of genetic interactions between all loss offunction and gain of function mutations with respect to all model phenotypes oroutputs. We use the probabilistic framework defined in MaBoSS software, basedon continuous time Markov chains and stochastic simulations. In addition, wesuggest several computational tools for studying the distribution of doublemutants in the space of model phenotype probabilities. We demonstrate thismethodology on three published models for each of which we derive the geneticinteraction networks and analyze their properties. We classify the obtainedinteractions according to their class of epistasis, dependence on the choseninitial conditions and phenotype. The use of this methodology for validatingmathematical models from experimental data and designing new experiments isdiscussed.

Predicting genetic interactions from Boolean models of biological networks