Quantitative Modeling of Gene Regulatory Network:Identifying the Network by Means of Genetic Algorithms

Abstract

The progress on gene expression profiling technology has allowed us to venture into the underlying regulatory rules and suggest many gene network models. Among them, the quantitative models are probably the most potent to achieve the highest precision and sensitivity. But the practical limitation of the approach is the requirement of lengthy data points and vulnerability to noise in the data. With present DNA micro-array technology, the large data set is very costly, and the noise is inevitably significant in expression levels. We have focused on using a nonlinear matrix model to represent the network regulation, in which the regulatory input to a gene’s expression level is represented by the summation of the regulation state of other genes. We propose two approaches, which allow us to analyze the data sufficiently with noise and small amount of data, implemented with genetic algorisms. The method was applied to simulated data and Rat Cervical Spinal Cord development data to generate the analysis of the expression profile.