Stochastic neural network models for gene regulatory networks

Tian T. and Burrage K. (2003). Stochastic neural network models for gene regulatory networks. In: 2003 Congress on Evolutionary Computation, CEC 2003 - Proceedings. 2003 Congress on Evolutionary Computation, CEC 2003, Canberra, ACT, (162-169). December 8, 2003-December 12, 2003. doi:10.1109/CEC.2003.1299570


Author Tian T.
Burrage K.
Title of paper Stochastic neural network models for gene regulatory networks
Conference name 2003 Congress on Evolutionary Computation, CEC 2003
Conference location Canberra, ACT
Conference dates December 8, 2003-December 12, 2003
Proceedings title 2003 Congress on Evolutionary Computation, CEC 2003 - Proceedings
Journal name 2003 Congress on Evolutionary Computation, CEC 2003 - Proceedings
Series 2003 Congress on Evolutionary Computation, CEC 2003 - Proceedings
Publisher IEEE Computer Society
Publication Year 2003
Sub-type Fully published paper
DOI 10.1109/CEC.2003.1299570
Volume 1
Start page 162
End page 169
Total pages 8
Abstract/Summary 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.
Subjects 2605 Computational Mathematics
Q-Index Code E1
Q-Index Status Provisional Code
Institutional Status Unknown

Document type: Conference Paper
Collection: Scopus Import
 
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Created: Tue, 12 Jul 2016, 02:08:12 EST by System User