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MDPbiome: engineering microbiomes

The latest studies on the dynamics of the microbiome, highlight that it is currently not possible to predict the effect on a complex microbial community of a specific external perturbation. MDPbiome contributes to addressing this challenge, modeling the effect of perturbations in a microbiome over time as a Markov Decision Process (MDP). Given an initial microbial composition, in any ecological niche or cavity, MDPbiome suggests the sequence of external disturbances that will guide/modulate the microbiome towards an objective state, such as a healthier or more efficient composition; as well as avoiding undesirable states, such as those associated with a pathology.

The published study demonstrates the flexibility of MDPbiome applied to varying sets of longitudinal microbiome data where meta-data on disturbances were known (knowledge that is not usually collected and / or published). For example, MDPbiome suggests the use of salmonella vaccine (not supplemented with probiotics) to accelerate the maturation of the gut microbiome of chickens. Measures are also provided to evaluate the performance in terms of reliability and universality of the recommendations proposed by MDPbiome in each case.

The potential of MDPbiome will improve in the coming years, as the availability of longitudinal microbiome datasets, and the rich metadata associated with them, increases. Microbial communities associated with plants are also amenable to this approach, to improve their health or nutrition through MDPbiome recommendations, for example, by optimizing soil fertility or proposing low impact policies to develop a sustainable agriculture.

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Original Paper:

García-Jiménez, B; de la Rosa, T; Wilkinson, MD. 2018. "MDPbiome: microbiome engineering through prescriptive perturbations". Bioinformatics. DOI: 10.1093/bioinformatics/bty562".

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