title: Incorporating prior knowledge improves detection of differences in bacterial growth rate.
abstract: BACKGROUND
  Robust statistical detection of differences in the bacterial growth rate can be challenging, particularly when dealing with small differences or noisy data. The Bayesian approach provides a consistent framework for inferring model parameters and comparing hypotheses. The method captures the full uncertainty of parameter values, whilst making effective use of prior knowledge about a given system to improve estimation.
  
  
  RESULTS
  We demonstrated the application of Bayesian analysis to bacterial growth curve comparison. Following extensive testing of the method, the analysis was applied to the large dataset of bacterial responses which are freely available at the web-resource, ComBase. Detection was found to be improved by using prior knowledge from clusters of previously analysed experimental results at similar environmental conditions. A comparison was also made to a more traditional statistical testing method, the F-test, and Bayesian analysis was found to perform more conclusively and to be capable of attributing significance to more subtle differences in growth rate.
  
  
  CONCLUSIONS
  We have demonstrated that by making use of existing experimental knowledge, it is possible to significantly improve detection of differences in bacterial growth rate.
authors.affiliation: John Innes Centre, Norwich Research Park, Norwich, NR4 7UH, UK. lydia.rickett@sainsbury-laboratory.ac.uk.|The Sainsbury Laboratory, Norwich Research Park, Colney Lane, Norwich, NR4 7RG, UK. lydia.rickett@sainsbury-laboratory.ac.uk.
authors.forename: Lydia M
authors.initials: LM
authors.lastname: Rickett
authors.affiliation: John Innes Centre, Norwich Research Park, Norwich, NR4 7UH, UK. Nick.Pullen@jic.ac.uk.
authors.forename: Nick
authors.initials: N
authors.lastname: Pullen
authors.affiliation: John Innes Centre, Norwich Research Park, Norwich, NR4 7UH, UK. Matthew.Hartley@jic.ac.uk.
authors.forename: Matthew
authors.initials: M
authors.lastname: Hartley
authors.affiliation: The Sainsbury Laboratory, Norwich Research Park, Colney Lane, Norwich, NR4 7RG, UK. Cyril.Zipfel@sainsbury-laboratory.ac.uk.
authors.forename: Cyril
authors.initials: C
authors.lastname: Zipfel
authors.affiliation: The Sainsbury Laboratory, Norwich Research Park, Colney Lane, Norwich, NR4 7RG, UK. Sophien.Kamoun@sainsbury-laboratory.ac.uk.
authors.forename: Sophien
authors.initials: S
authors.lastname: Kamoun
authors.affiliation: Institute of Food Research, Norwich Research Park, Colney Lane, Norwich, NR4 7UA, UK. Jozsef.Baranyi@ifr.ac.uk.
authors.forename: József
authors.initials: J
authors.lastname: Baranyi
authors.affiliation: John Innes Centre, Norwich Research Park, Norwich, NR4 7UH, UK. Richard.Morris@jic.ac.uk.
authors.forename: Richard J
authors.initials: RJ
authors.lastname: Morris
mesh_terms: D001419:Bacteria / Q000254:growth & development*; D001499:Bayes Theorem; D016013:Likelihood Functions; D008954:Models, Biological; D009010:Monte Carlo Method