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Related Experiment Video

Updated: Feb 2, 2026

Oral Biofilm Sampling for Microbiome Analysis in Healthy Children

Oral Biofilm Sampling for Microbiome Analysis in Healthy Children

Published on: December 31, 2017

MDPbiome: microbiome engineering through prescriptive perturbations.

Beatriz García-Jiménez¹, Tomás de la Rosa², Mark D Wilkinson¹

¹Center for Plant Biotechnology and Genomics UPM - INIA, Universidad Politecnica de Madrid, Madrid, Spain.

Bioinformatics (Oxford, England)

|November 14, 2018

Summary

This summary is machine-generated.

Predicting microbiome changes after perturbations is difficult. MDPbiome models these dynamics, suggesting optimal interventions to reach desired microbial states and avoid negative ones.

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Area of Science:

Microbiology
Computational Biology
Systems Biology

Background:

Current microbiome dynamics studies struggle to predict external perturbation effects on microbial communities.
Predictive modeling is crucial for applications in medicine, bioremediation, and industrial settings.

Purpose of the Study:

To develop a statistical modeling framework, MDPbiome, for predicting microbiome dynamics under perturbations.
To enable the engineering of microbiomes towards desired states by suggesting optimal intervention sequences.

Main Methods:

MDPbiome models longitudinal metagenomics data as a Markov Decision Process (MDP).
It identifies sequences of perturbations to transition a microbiome to a target composition.
The system also estimates intermediate states to avoid undesirable outcomes.

Main Results:

MDPbiome demonstrated flexibility and reliability across three distinct real-world datasets.
An optimal perturbation policy was successfully derived for each dataset.
Specific examples include guiding vaginal microbiome recovery and explaining Salmonella vaccine effects on chick gut maturation.

Conclusions:

MDPbiome offers a novel analytical approach for microbiome engineering.
It has significant potential applications in clinical interventions for maintaining or restoring healthy microbial populations.
The framework can also guide strategies to avoid detrimental microbiome states.