A Heuristic Method for Generating Probabilistic Boolean Networks from a Prescribed Transition Probability Matrix

摘要

Probabilistic Boolean Networks (PBNs) have received much attention for modeling ge-netic regulatory networks. In this paper, we propose efficient algorithms for constructing a proba-bilistic Boolean network when its transition probability matrix is given. This is an important inverseproblem in network inference from steady-state data, as most microarray data sets are assumed tobe obtained from sampling the steady-state.