Intelligent method of Petri net formal computational modeling of biological networks

Abstract

In this paper we present a weighted fuzzy production rules method incorporating the concepts of local weight with fuzzy Petri net. An improved method to compute the fuzzy value of the gene expression levels of the consequent part and a better way to interpret the linguistic meaning of the consequent are proposed here. Our approach offers the advantages of enhancing the knowledge representation power of a fuzzy production rules, reducing the undesirable effects when computing the consequent part by the graphical representation of fuzzy Petri net. In the proposed model, a gene expression profile is first transformed into a mapping form and then the transformed data are mapped into the fuzzy inference system. We have built the fuzzy Petri net model and classified the input data in terms of time point and obtained the output data, so the system can be viewed as the two-input of five sets (very low, low, medium, high, and very high) and one output system.