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Bioinformatics Advance Access originally published online on March 11, 2009
Bioinformatics 2009 25(10):1280-1286; doi:10.1093/bioinformatics/btp138
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© The Author 2009. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Switching regulatory models of cellular stress response

Guido Sanguinetti 1,2,*, Andreas Ruttor 3, Manfred Opper 3 and Cedric Archambeau 4

1Department of Computer Science, University of Sheffield, Regent Court, 211 Portobello Road, Sheffield, S1 4DP, 2Biological and Environmental Systems Group, Department of Chemical and Process Engineering, University of Sheffield, Mappin Street, Sheffield, S1 3JD, UK, 3Department of Computer Science, Technische Universität Berlin, D-10587 Berlin, Germany and 4Department of Computer Science University College London, Gower Street, WC1E 6BT, UK

*To whom correspondence should be addressed.


  Abstract

Motivation: Stress response in cells is often mediated by quick activation of transcription factors (TFs). Given the difficulty in experimentally assaying TF activities, several statistical approaches have been proposed to infer them from microarray time courses. However, these approaches often rely on prior assumptions which rule out the rapid responses observed during stress response.

Results: We present a novel statistical model to infer how TFs mediate stress response in cells. The model is based on the assumption that sensory TFs quickly transit between active and inactive states. We therefore model mRNA production using a bistable dynamical systems whose behaviour is described by a system of differential equations driven by a latent stochastic process. We assume the stochastic process to be a two-state continuous time jump process, and devise both an exact solution for the inference problem as well as an efficient approximate algorithm. We evaluate the method on both simulated data and real data describing Escherichia coli's response to sudden oxygen starvation. This highlights both the accuracy of the proposed method and its potential for generating novel hypotheses and testable predictions.

Availability: MATLAB and C++ code used in the article can be downloaded from http://www.dcs.shef.ac.uk/~guido/.

Contact: guido{at}dcs.shef.ac.uk

Supplementary information: Supplementary data are available at Bioinformatics online.

Associate Editor: David Rocke

Received on November 9, 2008; revised on February 4, 2009; accepted on March 8, 2009

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