Incorporating prior knowledge improves detection of differences in bacterial growth rate

Rickett, Lydia M, Pullen, Nick, Hartley, Matthew, Zipfel, Cyril, Kamoun, Sophien ORCID: https://orcid.org/0000-0002-0290-0315, Baranyi, József and Morris, Richard J (2015) Incorporating prior knowledge improves detection of differences in bacterial growth rate. BMC Systems Biology, 9. ISSN 1752-0509

Preview

PDF (Rickett et al) - Published Version Available under License Creative Commons Attribution. Download (738kB) | Preview

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.

Item Type:	Article
Faculty \ School:	Faculty of Science > School of Biological Sciences Faculty of Science > The Sainsbury Laboratory Faculty of Science > School of Computing Sciences Faculty of Science > School of Mathematics
UEA Research Groups:	Faculty of Science > Research Groups > Plant Sciences
Depositing User:	Pure Connector
Date Deposited:	09 Mar 2016 17:00
Last Modified:	22 Oct 2022 00:53
URI:	https://ueaeprints.uea.ac.uk/id/eprint/57406
DOI:	10.1186/s12918-015-0204-9

Downloads

Actions (login required)

View Item