Multilevel modelling for inference of genetic regulatory networks

Ng, Shu-Kay, Wang, Kui and McLachlan, Geoffrey J. (2006). Multilevel modelling for inference of genetic regulatory networks. In: Axel Bender, Complex Systems, Brisbane, Australia, (S390-S390). 11-14 December 2005. doi:10.1117/12.638449

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Author Ng, Shu-Kay
Wang, Kui
McLachlan, Geoffrey J.
Title of paper Multilevel modelling for inference of genetic regulatory networks
Conference name Complex Systems
Conference location Brisbane, Australia
Conference dates 11-14 December 2005
Journal name Proceedings of SPIE - International Society for Optical Engineering   Check publisher's open access policy
Place of Publication Bellingham, WA, United States
Publisher SPIE - International Society for Optical Engineering
Publication Year 2006
Sub-type Fully published paper
DOI 10.1117/12.638449
Open Access Status File (Publisher version)
ISBN 0-8194-6070-2
ISSN 0277-786X
Editor Axel Bender
Volume 6039
Start page S390
End page S390
Total pages 12
Collection year 2006
Language eng
Abstract/Summary Time-course experiments with microarrays are often used to study dynamic biological systems and genetic regulatory networks (GRNs) that model how genes influence each other in cell-level development of organisms. The inference for GRNs provides important insights into the fundamental biological processes such as growth and is useful in disease diagnosis and genomic drug design. Due to the experimental design, multilevel data hierarchies are often present in time-course gene expression data. Most existing methods, however, ignore the dependency of the expression measurements over time and the correlation among gene expression profiles. Such independence assumptions violate regulatory interactions and can result in overlooking certain important subject effects and lead to spurious inference for regulatory networks or mechanisms. In this paper, a multilevel mixed-effects model is adopted to incorporate data hierarchies in the analysis of time-course data, where temporal and subject effects are both assumed to be random. The method starts with the clustering of genes by fitting the mixture model within the multilevel random-effects model framework using the expectation-maximization (EM) algorithm. The network of regulatory interactions is then determined by searching for regulatory control elements (activators and inhibitors) shared by the clusters of co-expressed genes, based on a time-lagged correlation coefficients measurement. The method is applied to two real time-course datasets from the budding yeast (Saccharomyces cerevisiae) genome. It is shown that the proposed method provides clusters of cell-cycle regulated genes that are supported by existing gene function annotations, and hence enables inference on regulatory interactions for the genetic network.
Subjects E1
230204 Applied Statistics
270201 Gene Expression
321011 Medical Genetics
780101 Mathematical sciences
780105 Biological sciences
730305 Diagnostic methods
Q-Index Code E1
Q-Index Status Provisional Code
Institutional Status UQ
Additional Notes Copyright 2006 Society of Photo-Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.

 
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Created: Thu, 23 Aug 2007, 22:03:15 EST