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Stochastic neural network models for gene regulatory networks (2003)
| Venue: | CEC ’03. THE 2003 CONGRESS ON 2003, 1:162–169 VOL.1 |
| Citations: | 14 - 0 self |
BibTeX
@INPROCEEDINGS{Tian03stochasticneural,
author = {Tianhai Tian and Kevin Burrage},
title = { Stochastic neural network models for gene regulatory networks},
booktitle = {CEC ’03. THE 2003 CONGRESS ON 2003, 1:162–169 VOL.1},
year = {2003},
publisher = {}
}
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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 a population of cells. The discussion suggests that stochastic neural network models can give better descriptions of gene regulatory networks and provide criteria for measuring the reasonableness of mathematical models.
Keyphrases
gene regulatory network stochastic neural network model stochastic model mathematical modelling statistical distribution neural network model genome dynamic normal random variable gene expression stochastic simulation functional understanding gene expression data expression level expression data mathematical model stochastic process poisson random variable large scale gene network intrinsic noise intermediate regulation gene expression pattern normalized concentration gene-expression profiling technology stochastic modelling chance event stability property large amount early developmental stage recent advance
