Construction Method of Probabilistic Boolean Networks Based on Imperfect Information

Abstract

A probabilistic Boolean network (PBN) is well known as one of the mathematical models of gene regulatory networks. In a Boolean network, expression of a gene is approximated by a binary value, and its time evolution is expressed by Boolean functions. In a PBN, a Boolean function is probabilistically chosen from candidates of Boolean functions. One of the authors has proposed a method to construct a PBN from imperfect information. However, there is a weakness that the number of candidates of Boolean functions may be redundant. In this paper, this construction method is improved to efficiently utilize given information. To derive Boolean functions and those selection probabilities, the linear programming problem is solved. Here, we introduce the objective function to reduce the number of candidates. The proposed method is demonstrated by a numerical example.

New results on model reconstruction of Boolean networks with application to gene regulatory networks

“…Afterwards, literature 31 further investigated the model construction of BNs with premised attractors based on incomplete information, that is, under the assumption that a portion of Boolean functions is given in advance, the remaining Boolean functions need to be designed. Subsequently, the construction of probabilistic BNs 32,33 was also considered based on the work in Kobayashi and Hiraishi. 31 Motivated by the analysis above, we extend and further investigate the model reconstruction problem mentioned in literature 31 by resorting to STP method.…”