MDPbiome: microbiome engineering through prescriptive perturbations

Bioinformatics
Beatriz García-JiménezMark D Wilkinson

Abstract

Recent microbiome dynamics studies highlight the current inability to predict the effects of external perturbations on complex microbial populations. To do so would be particularly advantageous in fields such as medicine, bioremediation or industrial scenarios. MDPbiome statistically models longitudinal metagenomics samples undergoing perturbations as a Markov Decision Process (MDP). Given a starting microbial composition, our MDPbiome system suggests the sequence of external perturbation(s) that will engineer that microbiome to a goal state, for example, a healthier or more performant composition. It also estimates intermediate microbiome states along the path, thus making it possible to avoid particularly undesirable/unhealthy states. We demonstrate MDPbiome performance over three real and distinct datasets, proving its flexibility, and the reliability and universality of its output 'optimal perturbation policy'. For example, an MDP created using a vaginal microbiome time series, with a goal of recovering from bacterial vaginosis, suggested avoidance of perturbations such as lubricants or sex toys; while another MDP provided a quantitative explanation for why salmonella vaccine accelerates gut microbiome maturation in chicks....Continue Reading

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Citations

Apr 4, 2019·PeerJ·Beatriz García-Jiménez, Mark D Wilkinson
Sep 12, 2020·Gut Microbes·Rahwa TaddeseAnnemarie Boleij
May 17, 2019·Entropy·Jie Li, Matteo Convertino
Jun 22, 2020·Current Opinion in Biotechnology·Matthew B AmrofellTae Seok Moon

Related Concepts

Markov Chains
Computer Programs and Programming
Metagenomics
Microbiota (plant)
Pharmaceutical Preparations
Objective (Goal)
Medicine
Vaccines
Bacterial Vaginosis
Salmonella enterica

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