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Modelling the emergent dynamics and major metabolites of the human colonic microbiota

Helen Kettle; Petra Louis; Grietje Holtrop; Sylvia H. Duncan; Harry J. Flint
Environmental Microbiology · Vol. 17, Issue 5 · pp. 1615-1630 · 2015

Abstract

Summary We present here a first attempt at modelling microbial dynamics in the human colon incorporating both uncertainty and adaptation. This is based on the development of a M onod‐equation based, differential equation model, which produces computer simulations of the population dynamics and major metabolites of microbial communities from the human colon. To reduce the complexity of the system, we divide the bacterial community into 10 bacterial functional groups ( BFGs ) each distinguished by its substrate preferences, metabolic pathways and its preferred pH range. The model simulates the growth of a large number of bacterial strains and incorporates variation in microbiota composition between people, while also allowing succession and enabling adaptation to environmental changes. The model is shown to reproduce many of the observed changes in major phylogenetic groups and key metabolites such as butyrate, acetate and propionate in response to a one unit pH shift in experimental continuous flow fermentors inoculated with human faecal microbiota. Nevertheless, it should be regarded as a learning tool to be updated as our knowledge of bacterial groups and their interactions expands. Given the difficulty of accessing the colon, modelling can play an extremely important role in interpreting experimental data and predicting the consequences of dietary modulation.

Bibliographic Information

JournalEnvironmental Microbiology
PublisherWiley
Publication Date2015-05-01
Publication Year2015
Volume17
Issue5
Pages1615-1630
Document TypeJournal Article
Print ISSN1462-2912
eISSN1462-2920
DOI10.1111/1462-2920.12599
SubjectMicrobial Ecology

Access Information

NARA Access Coverage1999-01-01~Current
Journal Homepagehttps://onlinelibrary.wiley.com/loi/14622920
Publisher PageOpen Publisher Page
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