Bayesian model-based inference of transcription factor activity

Rogers, S., Khanin, R. and Girolami, M. (2007) Bayesian model-based inference of transcription factor activity. BMC Bioinformatics, 8(Suppl), (doi: 10.1186/1471-2105-8-S2-S2)

Abstract

<b>Background:</b> In many approaches to the inference and modeling of regulatory interactions using microarray data, the expression of the gene coding for the transcription factor is considered to be an accurate surrogate for the true activity of the protein it produces. There are many instances where this is inaccurate due to post-translational modifications of the transcription factor protein. Inference of the activity of the transcription factor from the expression of its targets has predominantly involved linear models that do not reflect the nonlinear nature of transcription. We extend a recent approach to inferring the transcription factor activity based on nonlinear Michaelis-Menten kinetics of transcription from maximum likelihood to fully Bayesian inference and give an example of how the model can be further developed.<p></p> <b>Results:</b> We present results on synthetic and real microarray data. Additionally, we illustrate how gene and replicate specific delays can be incorporated into the model.<p></p> <b>Conclusion:</b> We demonstrate that full Bayesian inference is appropriate in this application and has several benefits over the maximum likelihood approach, especially when the volume of data is limited. We also show the benefits of using a non-linear model over a linear model, particularly in the case of repression.<p></p>

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Rogers, Dr Simon and Khanin, Dr Raya and Girolami, Prof Mark
• Authors: Rogers, S., Khanin, R., and Girolami, M.
• Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science; Q Science > QH Natural history > QH426 Genetics; Q Science > Q Science (General)
• College/School: College of Science and Engineering > School of Computing Science
• Journal Name: BMC Bioinformatics
• Publisher: Biomed Central
• ISSN: 1471-2105
• Copyright Holders: Copyright © 2007 BioMed Central Ltd.
• First Published: First published in BMC Bioinformatics 8(Suppl 2)
• Publisher Policy: Reproduced in accordance with the copyright policy of the publisher

Related URLs

• PubMed

Project Code	Award No	Project Name	Principal Investigator	Funder's Name	Funder Ref	Lead Dept
39684	1	Probabilistic Reconstruction of Signalling Pathways & Identification of Novel Transcription Factors Employing Heterogeneous Genome-Wide data	Mark Girolami	Medical Research Council (MRC)	G0401466	Computing Science
39934	1	Stochastic modelling and statistical inference of gene regulatory pathways - integrating multiple sources of data	Ernst Wit	Engineering & Physical Sciences Research Council (EPSRC)	EP/C010620/1	Statistics