Stochastic Neural Network Models for Gene Regulatory Networks

Tianhai Tian1, Kevin Burrage2, ACMC, University of Queensland;, ACMC University of Queensland

Recent advances in gene-expression profiling technologies provide large amounts of gene expression data. This data raise the possibility for a functional understanding of genome dynamics by means of mathematical modelling. As gene expression involves intrinsic noise, stochastic models are essential for better descriptions of gene regulatory networks. We will present stochastic models by introducing stochastic processes into neural network models that can describe intermediate regulation for large scale gene networks. Poisson random variables are used to represent chance events in the processes of synthesis and degradation. For expression data with normalized concentrations, exponential or normal random variables are used to realize fluctuations. Using a network with three genes, we show how to use stochastic simulations for studying robustness and stability properties of gene expression patterns under the influence of noise, and how to use stochastic models to predict statistical distributions of expression levels in a population of cells.