ArticleBMC systems biology2015
Incorporating prior knowledge improves detection of differences in bacterial growth rate.
Article in BMC systems biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
3 citing papers in PubMed.
- Modelling Growth and Decline in a Two-Species Model System: PathogenicFoods (Basel, Switzerland) · 2020Article
- Data assimilation and multisource decision-making in systems biology based on unobtrusive Internet-of-Things devices.Biomedical engineering online · 2018Article
- Epigenetics knocks on synthetic biology's door.FEMS microbiology letters · 2016Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
backgroundRobust 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.
resultsWe 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.
conclusionsWe have demonstrated that by making use of existing experimental knowledge, it is possible to significantly improve detection of differences in bacterial growth rate.
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Registered trials
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