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Constraint-based modeling of microbial communities and their interaction with the host

Presented by: 
A Heinken Université du Luxembourg
Tuesday 4th November 2014 - 11:45 to 12:00
INI Seminar Room 1
Co-author: Ines Thiele (Luxembourg Centre for Systems Biomedicine)

The gut microbiota performs a central role in human well-being and disease, yet few constraint-based efforts have investigated the metabolic interaction between the host and this important ecosystem. In a first effort, we constructed an in silico model of a gnotobiotic mouse colonized with a single microbial species by joining a genome-scale, manually curated and validated reconstruction of the prominent human and mouse gut symbiont Bacteroides thetaiotaomicron and a previously published mouse reconstruction, iMM1415. We depicted the trade-off between host and microbe biomass and predicted that the microbe rescued lethal host phenotypes (1). Subsequently, we developed a framework of alternating in silico and in vitro steps to elucidate the metabolic potential of a poorly studied gut microbe relevant for human health, Faecalibacterium prausnitzii. The combination of metabolic modeling and in vitro culture revealed novel carbon sources and secretion products and allowed the definition of a chemically defined growth medium for the microbe (2). We then constructed an in silico gut microbial community model consisting of 11 microbes spanning three phyla. The community model was joined with the global human reconstruction Recon2 and the effects of the microbes on human metabolic phenotypes were systematically predicted. The microbes had a global effect on the predicted host body fluid metabolome that was significantly more pronounced for commensal than for pathogenic microbes. Finally, we discuss challenges that need to be considered when constructing a well-curated, representative gut microbial community model. We demonstrate that constraint-based multi-species modeling can accurately capture the interaction between the gut microbiota and the human host and should prove useful for modeling the influence of the microbiota in human health and disease.

1. A. Heinken et al., Gut Microbes 4, 28-40 (2013). 2. A. Heinken et al., J Bacteriol, (2014).

Presentation Material: 
University of Cambridge Research Councils UK
    Clay Mathematics Institute London Mathematical Society NM Rothschild and Sons