Genetic Regulatory Network Identification Using Monotone Functions Decomposition

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AbstractWe present a method for identification of gene regulatory network topology using a time series of gene expression data. The underlying assumption in our method is that the functions that describe regulatory relations must be continuous, nonnegative and monotonic. This assumption is very general, as it is satisfied by virtually all existing regulatory models. Our method is based on refuting all regulation hypotheses that cannot meet this assumption. This procedure takes the form of a Linear Programming (LP) feasibility problem. We also present two conditions where the regulation hypotheses are irrefutable.