Anomaly detection in gene expression via stochastic models of gene regulatory networks

Title: Anomaly detection in gene expression via stochastic models of gene regulatory networks
Authors: Kim, H
Gelenbe, E
Item Type: Journal Article
Abstract: BACKGROUND: The steady-state behaviour of gene regulatory networks (GRNs) can provide crucial evidence for detecting disease-causing genes. However, monitoring the dynamics of GRNs is particularly difficult because biological data only reflects a snapshot of the dynamical behaviour of the living organism. Also most GRN data and methods are used to provide limited structural inferences. RESULTS: In this study, the theory of stochastic GRNs, derived from G-Networks, is applied to GRNs in order to monitor their steady-state behaviours. This approach is applied to a simulation dataset which is generated by using the stochastic gene expression model, and observe that the G-Network properly detects the abnormally expressed genes in the simulation study. In the analysis of real data concerning the cell cycle microarray of budding yeast, our approach finds that the steady-state probability of CLB2 is lower than that of other agents, while most of the genes have similar steady-state probabilities. These results lead to the conclusion that the key regulatory genes of the cell cycle can be expressed in the absence of CLB type cyclines, which was also the conclusion of the original microarray experiment study. CONCLUSION: G-networks provide an efficient way to monitor steady-state of GRNs. Our method produces more reliable results then the conventional t-test in detecting differentially expressed genes. Also G-networks are successfully applied to the yeast GRNs. This study will be the base of further GRN dynamics studies cooperated with conventional GRN inference algorithms.
Issue Date: 3-Dec-2009
Date of Acceptance: 1-Dec-2009
URI: http://hdl.handle.net/10044/1/64879
DOI: https://dx.doi.org/10.1186/1471-2164-10-S3-S26
ISSN: 1471-2164
Publisher: BioMed Central
Journal / Book Title: BMC Genomics
Volume: 10
Issue: Suppl 3
Copyright Statement: © 2009 Kim and Gelenbe; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Keywords: Biometry
Cell Cycle
Gene Expression
Gene Expression Regulation, Fungal
Gene Regulatory Networks
Models, Genetic
Probability
Saccharomycetales
Stochastic Processes
06 Biological Sciences
11 Medical And Health Sciences
08 Information And Computing Sciences
Bioinformatics
Publication Status: Published
Conference Place: England
Article Number: S26
Online Publication Date: 2009-12-03
Appears in Collections:Faculty of Engineering
Electrical and Electronic Engineering



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