Gene expression modelling with the use of Boolean network and artificial neural network

Recent progress in molecular biology has enabled exploration of the mechanisms of genetic information processing in organisms. With the use of new technologies it is possible to observe a state of the cell at different time steps, assemble and disassemble genetic information carriers, etc. With new tools available there is a chance to answer the question that has motivated our predecessors. There is a chance to find out "how it all works?" In this paper we study a method of gene networks modelling. A gene network is a mass of genes interacting with one another through expression. The model is used to infer a gene expression mechanism on the basis of gene expression measurements. In our approach we employed two network models: a Boolean network model and an artificial neural network model. We have shown that large data can be handled efficiently with the aid of already developed methods and algorithms. Thanks to them, drawing meaningful inferences from large gene expression data may be converted into simple tasks.