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Comparison of evolutionary algorithms in gene regulatory network model inference

Sîrbu, Alina and Ruskin, Heather J. and Crane, Martin (2010) Comparison of evolutionary algorithms in gene regulatory network model inference. BMC Bioinformatics, 11 (59). pp. 1471-2205. ISSN 1471-2105

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Background: The evolution of high throughput technologies that measure gene expression levels has created a data base for inferring GRNs (a process also known as reverse engineering of GRNs). However, the nature of these data has made this process very di±cult. At the moment, several methods of discovering qualitative causal relationships between genes with high accuracy from microarray data exist, but large scale quantitative analysis on real biological datasets cannot be performed, to date, as existing approaches are not suitable for real microarray data which are noisy and insu±cient. Results: This paper performs an analysis of several existing evolutionary algorithms for quantitative gene regulatory network modelling. The aim is to present the techniques used and o®er a comprehensive comparison of approaches, under a common framework. Algorithms are applied to both synthetic and real gene expression data from DNA microarrays, and ability to reproduce biological behaviour, scalability and robustness to noise are assessed and compared. Conclusions: Presented is a comparison framework for assessment of evolutionary algorithms, used to infer gene regulatory networks. Promising methods are identi¯ed and a platform for development of appropriate model formalisms is established.

Item Type:Article (Published)
Uncontrolled Keywords:microarray data analysis; time course data; genetic regulatory networks;
Subjects:Biological Sciences > Bioinformatics
Mathematics > Mathematical models
Computer Science > Artificial intelligence
Physical Sciences > Statistical physics
Computer Science > Computer simulation
DCU Faculties and Centres:Research Initiatives and Centres > Scientific Computing and Complex Systems Modelling (Sci-Sym)
Publisher:BioMed Central
Official URL:
Copyright Information:© 2010 Sîrbu et al
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:Irish Research Council for Science Engineering and Technology, EMBARK Scholarship Programme
ID Code:15254
Deposited On:05 Mar 2010 09:55 by Martin Crane. Last Modified 26 Apr 2010 11:21

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