An efficient moments-based inference method for within-host bacterial infection dynamics.
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Publication Date
2017-11Journal Title
PLoS Comput Biol
ISSN
1553-734X
Publisher
Public Library of Science (PLoS)
Volume
13
Issue
11
Pages
e1005841
Language
eng
Type
Article
This Version
VoR
Physical Medium
Electronic-eCollection
Metadata
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Price, D., Breuzé, A., Dybowski, R., Mastroeni, P., & Restif, O. (2017). An efficient moments-based inference method for within-host bacterial infection dynamics.. PLoS Comput Biol, 13 (11), e1005841. https://doi.org/10.1371/journal.pcbi.1005841
Abstract
Over the last ten years, isogenic tagging (IT) has revolutionised the study of bacterial infection dynamics in laboratory animal models. However, quantitative analysis of IT data has been hindered by the piecemeal development of relevant statistical models. The most promising approach relies on stochastic Markovian models of bacterial population dynamics within and among organs. Here we present an efficient numerical method to fit such stochastic dynamic models to in vivo experimental IT data. A common approach to statistical inference with stochastic dynamic models relies on producing large numbers of simulations, but this remains a slow and inefficient method for all but simple problems, especially when tracking bacteria in multiple locations simultaneously. Instead, we derive and solve the systems of ordinary differential equations for the two lower-order moments of the stochastic variables (mean, variance and covariance). For any given model structure, and assuming linear dynamic rates, we demonstrate how the model parameters can be efficiently and accurately estimated by divergence minimisation. We then apply our method to an experimental dataset and compare the estimates and goodness-of-fit to those obtained by maximum likelihood estimation. While both sets of parameter estimates had overlapping confidence regions, the new method produced lower values for the division and death rates of bacteria: these improved the goodness-of-fit at the second time point at the expense of that of the first time point. This flexible framework can easily be applied to a range of experimental systems. Its computational efficiency paves the way for model comparison and optimal experimental design.
Keywords
Animals, Bacterial Infections, Stochastic Processes, Computational Biology, Models, Biological, Host-Pathogen Interactions
Sponsorship
Biotechnology and Biological Sciences Research Council grant BB/M020193/1 awarded to OR, and to support DJP. Biotechnology and Biological Sciences Research Council grant BB/I002189/1 awarded to PM, and to support RD.
Funder references
Biotechnology and Biological Sciences Research Council (BB/M000982/1)
Biotechnology and Biological Sciences Research Council (BB/M020193/1)
Biotechnology and Biological Sciences Research Council (BB/I002189/1)
Identifiers
External DOI: https://doi.org/10.1371/journal.pcbi.1005841
This record's URL: https://www.repository.cam.ac.uk/handle/1810/276586
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