Optimizing static thermodynamic models of transcriptional regulation

Bauer, D. C. and Bailey, T. L. (2009) Optimizing static thermodynamic models of transcriptional regulation. BIOINFORMATICS, 25 13: 1640-1646. doi:10.1093/bioinformatics/btp283


Author Bauer, D. C.
Bailey, T. L.
Title Optimizing static thermodynamic models of transcriptional regulation
Journal name BIOINFORMATICS   Check publisher's open access policy
ISSN 1367-4803
Publication date 2009-04-01
Sub-type Article (original research)
DOI 10.1093/bioinformatics/btp283
Open Access Status DOI
Volume 25
Issue 13
Start page 1640
End page 1646
Total pages 7
Editor Bateman, A
Place of publication Oxford , U.K
Publisher Oxford University Press
Language eng
Subject C1
97 Expanding Knowledge
970106 Expanding Knowledge in the Biological Sciences
06 Biological Sciences
060405 Gene Expression (incl. Microarray and other genome-wide approaches)
Abstract Motivation: Modeling transcriptional regulation using thermodynamic modeling approaches has become increasingly relevant as a way to gain a detailed understanding of transcriptional regulation. Thermodynamic models are able to model the interactions between transcription factors (TFs) and DNA that lead to a specific transcriptional output of the target gene. Such models can be `trained' by fitting their free parameters to data on the transcription rate of a gene and the concentrations of its regulating factors. However, the parameter fitting process is computationally very expensive and this limits the number of alternative types of model that can be explored. Results: In this study, we evaluate the 'optimization landscape' of a class of static, quantitative models of regulation and explore the efficiency of a range of optimization methods. We evaluate eight optimization methods: two variants of simulated annealing (SA), four variants of gradient descent (GD), a hybrid SA/GD algorithm and a genetic algorithm. We show that the optimization landscape has numerous local optima, resulting in poor performance for the GD methods. SA with a simple geometric cooling schedule performs best among all tested methods. In particular, we see no advantage to using the more sophisticated 'LAM' cooling schedule. Overall, a good approximate solution is achievable in minutes using SA with a simple cooling schedule.
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: 2010 Higher Education Research Data Collection
Institute for Molecular Bioscience - Publications
 
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Citation counts: TR Web of Science Citation Count  Cited 6 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 6 times in Scopus Article | Citations
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Created: Thu, 03 Sep 2009, 17:56:25 EST by Mr Andrew Martlew on behalf of Institute for Molecular Bioscience