PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Switching Regulatory Models of Cellular Stress Response.
Guido Sanguinetti, Andreas Ruttor and Manfred Opper
Bioinformatics 2009.

Abstract

\section{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. \section{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 {\em 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. \section{Availability} MATLAB and C++ code used in the paper can be downloaded from \texttt{http://www.dcs.shef.ac.uk/~guido/}. \section{Contact}\texttt{guido@dcs.shef.ac.uk}

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
ID Code:5265
Deposited By:Manfred Opper
Deposited On:24 March 2009