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Bioinformatics Advance Access first published online on March 11, 2009
This version published online on March 13, 2009
Bioinformatics, 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 a,b, Andreas Ruttor c, Manfred Opper c and Cedric Archambeau d

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

To whom correspondence should be addressed. Dr. Guido Sanguinetti, E-mail: guido{at}dcs.shef.ac.uk


  Abstract

Motivation Stress response in cells is often mediated by quick activation of transcription factors. Given the difficulty in experimentally assaying transcription factor 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 transcription factors mediate stress response in cells. The model is based on the assumption that sensory transcription factors quickly transit between active and inactive states. We therefore model mRNA production using a bi-stable 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 E. 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 paper can be downloaded from http://www.dcs.shef.ac.uk/~guido/.

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

Associate Editor: Prof. David Rocke

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

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