title: Switching regulatory models of cellular stress response.
abstract: BACKGROUND
  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.
  
  
  BACKGROUND
  MATLAB and C++ code used in the article can be downloaded from http://www.dcs.shef.ac.uk/~guido/.
authors.affiliation: Department of Computer Science, University of Sheffield, Regent Court, 211 Portobello Road, Sheffield, S1 4DP, UK. guido@dcs.shef.ac.uk
authors.forename: Guido
authors.initials: G
authors.lastname: Sanguinetti
authors.forename: Andreas
authors.initials: A
authors.lastname: Ruttor
authors.forename: Manfred
authors.initials: M
authors.lastname: Opper
authors.forename: Cedric
authors.initials: C
authors.lastname: Archambeau
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D004926:Escherichia coli / Q000378:metabolism; D020869:Gene Expression Profiling; D015233:Models, Statistical; D010100:Oxygen / Q000378:metabolism; D012333:RNA, Messenger / Q000378:metabolism; D013312:Stress, Physiological / Q000235:genetics*; D014157:Transcription Factors / Q000378:metabolism
chemical_list: D012333:RNA, Messenger; D014157:Transcription Factors; D010100:Oxygen