MDPBiome - AI for engineering microbiomes through perturbations

MBP Biome Diagram




MDPBiome - Artificial Intelligence applied to Microbiome Engineering Our recently published algorithm MDPBiome is a methodology for guiding the evolution of amicrobiome through successive perturbations, where the algorithm calculates the most likely response of the microbiometo that intervention. The latest studies on the dynamics of the microbiome highlight that it is currently not possibleto 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 timeas 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 performant composition; as well as avoiding undesirable states, such asthose associated with a pathology. The study demonstrates the flexibility of MDPbiome applied tovarying sets of longitudinal microbiome data where meta-data on disturbances were known (knowledge that is not usually collected and / or published). 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 impactpolicies to develop a sustainable agriculture.