title: Stochastic S-system modeling of gene regulatory network.
abstract: Microarray gene expression data can provide insights into biological processes at a system-wide level and is commonly used for reverse engineering gene regulatory networks (GRN). Due to the amalgamation of noise from different sources, microarray expression profiles become inherently noisy leading to significant impact on the GRN reconstruction process. Microarray replicates (both biological and technical), generated to increase the reliability of data obtained under noisy conditions, have limited influence in enhancing the accuracy of reconstruction . Therefore, instead of the conventional GRN modeling approaches which are deterministic, stochastic techniques are becoming increasingly necessary for inferring GRN from noisy microarray data. In this paper, we propose a new stochastic GRN model by investigating incorporation of various standard noise measurements in the deterministic S-system model. Experimental evaluations performed for varying sizes of synthetic network, representing different stochastic processes, demonstrate the effect of noise on the accuracy of genetic network modeling and the significance of stochastic modeling for GRN reconstruction . The proposed stochastic model is subsequently applied to infer the regulations among genes in two real life networks: (1) the well-studied IRMA network, a real-life in-vivo synthetic network constructed within the Saccharomyces cerevisiae yeast, and (2) the SOS DNA repair network in Escherichia coli.
authors.affiliation: School of Engineering and Information Technology, Federation University Australia, Churchill, Australia ; Department of Computer Science and Engineering, University of Dhaka, Dhaka, Bangladesh.
authors.forename: Ahsan Raja
authors.initials: AR
authors.lastname: Chowdhury
authors.affiliation: School of Engineering and Information Technology, Federation University Australia, Churchill, Australia.
authors.forename: Madhu
authors.initials: M
authors.lastname: Chetty
authors.affiliation: Department of Electrical and Electronics Engineering, University of Melbourne, Parkville, Australia.
authors.forename: Rob
authors.initials: R
authors.lastname: Evans
keywords: Deterministic model; S-system; Stochastic model