Stochastic neural network models for gene regulatory networks

Tian, T. and Burrage, K. (2003) Stochastic neural network models for gene regulatory networks. In: CEC 2003: The 2003 Congress on Evolutionary Computation. Proceedings. Canberra, Australia, 8-12 December 2003. IEEE Computer Society: Piscataway, USA, pp. 162-169. ISBN 9780780378049

Abstract

Recent advances in gene-expression profiling technologies provide large amounts of gene expression data. This raises 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. However, stochastic modelling for large scale gene expression data sets is still in the very early developmental stage. In this paper we present some 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 population of cells. The discussion suggest that stochastic neural network models can give better description of gene regulatory networks and provide criteria for measuring the reasonableness o mathematical models.

• Item Type: Book Sections
• Additional Information: ©2003 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Tian, Dr Tianhai
• Authors: Tian, T., and Burrage, K.
• Subjects: Q Science > QA Mathematics
• College/School: College of Science and Engineering > School of Mathematics and Statistics > Mathematics
• Publisher: IEEE Computer Society
• ISBN: 9780780378049
• Copyright Holders: Copyright © 2003 IEEE Computer Society
• Publisher Policy: Reproduced in accordance with the copyright policy of the publisher.