Contributed Talk 3: A functional population model of fiber degradation by the human intestinal microbiota
Duration: 22 mins 32 secs
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| Description: |
Laroche, BA (INRA - Institut National de la Recherche Agronomique)
Thursday 11 September 2014, 15:10-15:25 |
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| Created: | 2014-09-15 14:24 |
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| Collection: | Understanding Microbial Communities; Function, Structure and Dynamics |
| Publisher: | Isaac Newton Institute |
| Copyright: | Laroche, BA |
| Language: | eng (English) |
| Distribution: |
World
(downloadable)
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| Explicit content: | No |
| Aspect Ratio: | 16:9 |
| Screencast: | No |
| Bumper: | UCS Default |
| Trailer: | UCS Default |
| Abstract: | Co-authors: Marion LECLERC (MICALIS,INRA), Sebastien RAGUIDEAU (MIA-J, INRA)
The human intestinal microbiota is a complex microbial ecosystem that plays a crucial role in several aspects of human health. It is particularly involved in the metabolism of residual fibres, through anaerobic digestion, thus providing significant energy (Short Chain Fatty Acids, simple sugars) and vitamins to the host. This ecosystem remains largely unexplored as a result of its limited accessibility and its complexity. We will present the ongoing development of an in silico model of the ecosystem, in interaction with its environment and its host. Our aim is to provide a virtual platform for knowledge and data integration, and simulation. A focus will be made on the structuration of the ecosystem into functional populations. We show how Whole Genome Sequencing (WGS) data from metagenomic analyses can be used to analyse the structure of carbohydrates degradation-related functions. These data give an insight of the gene content of an entire microbial community, even if the organisms that compose it cannot be cultivated. In addition to the potential of conventional molecular inventory techniques (such as targeting ADNr16s), which allows an analysis of diversity, WGS approaches provide an access to the functions. We show how the information obtained can be integrated in the model and discuss the underlying modelling hypotheses. |
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