From fuzzy logic theory to Fuzzy Petri Nets predicting changes in gene expression level

This paper proposes a new approach for biologically inspired computing on the basis of gene regulatory networks (GRNs). It is important to note that the choice of an appropriate modeling formalism is dependent upon the aim of the study. GRNs are models of genes and gene interactions at the expression level. The advent of microarray technology has challenged computer scientists to develop better algorithms for modeling the underlying regulatory relationship in between the genes. Fuzzy system has an ability to search microarray datasets for activator/repressor regulatory relationship. In this paper, we present a fuzzy reasoning model based on the fuzzy Petri net. The model considers the regulatory triplets by means of predicting changes in expression level of the target based on input expression level. This method eliminates possible false predictions from the classical fuzzy model thereby allowing a wider search space for inferring regulatory relationship. Through formalization of fuzzy reasoning, we propose an approach to construct a rule-based reasoning system. The experimental results show the proposed approach is feasible and acceptable to predict changes in expression level of the target gene.