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Identification of predictors of Boolean networks from observed attractor states

Predictors of Boolean networks are of significance for biologists to target their research on gene regulation and control. This paper aims to investigate how to determine predictors of Boolean networks from observed attractor states by solving logical equations. The proposed method consists of four steps. First, all possible cycles formed by known attractor states are constructed. Then, for each possible cycle, all data‐permitted predictors of each node are identified according to the known attractor states. Subsequently, the data‐permitted predictors are incorporated with some common biological constraints to generate logical equations that describe whether such possible predictors can ultimately be chosen as valid ones by the biological constraints. Finally, solve the logical equations; the solutions determine a family of predictors satisfying the known attractor states. The approach is quite different from others such as computer algorithm‐based and provides a new angle and means to understand and analyze the structures of Boolean networks.