Reverse Engineering of Asynchronous Boolean Networks via Minimum Explanatory Set and Maximum Likelihood

In this paper, we propose an approach for reconstructing asynchronous Boolean networks from observed data. We find the causal relationships in Boolean networks using an asynchronous evolution approach. In our approach, we first find a minimum explanatory set for a node to reduce complexity of candidate Boolean functions, and then we choose a Boolean function for the node based on the maximum likelihood. This approach is stimulated by the task SIGNET of the causal challenge #2 pot-luck (Jenkins, 2009). Besides the data set SIGNET, we also applied our approach to two other datasets to evaluate our approach: one is generated by Professor Isabelle Guyon and the other generated ourselves from the signal transduction network of Abscisic acid in guard cell.