title: From qualitative data to quantitative models: analysis of the phage shock protein stress response in Escherichia coli.
abstract: BACKGROUND
  Bacteria have evolved a rich set of mechanisms for sensing and adapting to adverse conditions in their environment. These are crucial for their survival, which requires them to react to extracellular stresses such as heat shock, ethanol treatment or phage infection. Here we focus on studying the phage shock protein (Psp) stress response in Escherichia coli induced by a phage infection or other damage to the bacterial membrane. This system has not yet been theoretically modelled or analysed in silico.
  
  
  RESULTS
  We develop a model of the Psp response system, and illustrate how such models can be constructed and analyzed in light of available sparse and qualitative information in order to generate novel biological hypotheses about their dynamical behaviour. We analyze this model using tools from Petri-net theory and study its dynamical range that is consistent with currently available knowledge by conditioning model parameters on the available data in an approximate Bayesian computation (ABC) framework. Within this ABC approach we analyze stochastic and deterministic dynamics. This analysis allows us to identify different types of behaviour and these mechanistic insights can in turn be used to design new, more detailed and time-resolved experiments.
  
  
  CONCLUSIONS
  We have developed the first mechanistic model of the Psp response in E. coli. This model allows us to predict the possible qualitative stochastic and deterministic dynamic behaviours of key molecular players in the stress response. Our inferential approach can be applied to stress response and signalling systems more generally: in the ABC framework we can condition mathematical models on qualitative data in order to delimit e.g. parameter ranges or the qualitative system dynamics in light of available end-point or qualitative information.
authors.affiliation: Division of Molecular Biosciences, Imperial College London, South Kensington, London SW7 2AZ, UK. ttoni@imperial.ac.uk
authors.forename: Tina
authors.initials: T
authors.lastname: Toni
authors.forename: Goran
authors.initials: G
authors.lastname: Jovanovic
authors.forename: Maxime
authors.initials: M
authors.lastname: Huvet
authors.forename: Martin
authors.initials: M
authors.lastname: Buck
authors.forename: Michael P H
authors.initials: MP
authors.lastname: Stumpf
mesh_terms: D000465:Algorithms; D001426:Bacterial Proteins / Q000737:chemistry; D001435:Bacteriophages / Q000378:metabolism; D001499:Bayes Theorem; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D004926:Escherichia coli / Q000235:genetics* / Q000378:metabolism; D029968:Escherichia coli Proteins / Q000378:metabolism*; D015964:Gene Expression Regulation, Bacterial; D006360:Heat-Shock Proteins / Q000378:metabolism; D008954:Models, Biological; D009010:Monte Carlo Method; D011336:Probability; D013269:Stochastic Processes; D049490:Systems Biology / Q000379:methods
chemical_list: D001426:Bacterial Proteins; D029968:Escherichia coli Proteins; D006360:Heat-Shock Proteins