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Stochastic S-system modeling of gene regulatory network

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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.

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Acknowledgments

This work has been funded by Collaborative Research Network (CRN) project of Federation University Australia. Authors would like to acknowledge Dr. Andrew Percy from Federation University Australia (Gippsland campus) for his useful discussion.

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Authors and Affiliations

  1. School of Engineering and Information Technology, Federation University Australia, Churchill, Australia

    Ahsan Raja Chowdhury & Madhu Chetty

  2. Department of Electrical and Electronics Engineering, University of Melbourne, Parkville, Australia

    Rob Evans

  3. Department of Computer Science and Engineering, University of Dhaka, Dhaka, Bangladesh

    Ahsan Raja Chowdhury

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  1. Ahsan Raja Chowdhury
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  2. Madhu Chetty
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  3. Rob Evans
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Correspondence to Ahsan Raja Chowdhury.

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Chowdhury, A.R., Chetty, M. & Evans, R. Stochastic S-system modeling of gene regulatory network. Cogn Neurodyn 9, 535–547 (2015). https://doi.org/10.1007/s11571-015-9346-0

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  • DOI: https://doi.org/10.1007/s11571-015-9346-0

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