title: Developing stochastic models for spatial inference: bacterial chemotaxis.
abstract: BACKGROUND
  Biological systems are inherently inhomogeneous and spatial effects play a significant role in processes such as pattern formation. At the cellular level proteins are often localised either through static attachment or via a dynamic equilibrium. As well as spatial heterogeneity many cellular processes exhibit stochastic fluctuations and so to make inferences about the location of molecules there is a need for spatial stochastic models. A test case for spatial models has been bacterial chemotaxis which has been studied extensively as a model of signal transduction.
  
  
  RESULTS
  By creating specific models of a cellular system that incorporate the spatial distributions of molecules we have shown how the fit between simulated and experimental data can be used to make inferences about localisation, in the case of bacterial chemotaxis. This method allows the robust comparison of different spatial models through alternative model parameterisations.
  
  
  CONCLUSIONS
  By using detailed statistical analysis we can reliably infer the parameters for the spatial models, and also to evaluate alternative models. The statistical methods employed in this case are particularly powerful as they reduce the need for a large number of simulation replicates. The technique is also particularly useful when only limited molecular level data is available or where molecular data is not quantitative.
authors.affiliation: Department of Statistics, University of Oxford, Oxford, United Kingdom.
authors.forename: Yoon-Dong
authors.initials: YD
authors.lastname: Yu
authors.forename: Yoonjoo
authors.initials: Y
authors.lastname: Choi
authors.forename: Yik-Ying
authors.initials: YY
authors.lastname: Teo
authors.forename: Andrew R
authors.initials: AR
authors.lastname: Dalby
mesh_terms: D000465:Algorithms; D002633:Chemotaxis; D003198:Computer Simulation; D004058:Diffusion; D004926:Escherichia coli / Q000166:cytology*; D007700:Kinetics; D008954:Models, Biological; D013269:Stochastic Processes