# Bayesian Machine Learning Tools for Analyzing Microbiome Dynamics

> **NIH NIH R01** · BRIGHAM AND WOMEN'S HOSPITAL · 2021 · $312,939

## Abstract

The human microbiota plays an important role in health and disease, and its therapeutic manipulation is being
actively investigated for a wide range of diseases that span every NIH institute. Our microbiota are inherently
dynamic, and analyzing these time-dependent properties is key to robustly linking the microbiota to disease,
and predicting the effects of therapies targeting the microbiota; indeed, longitudinal microbiome data is being
acquired with increasing frequency, and is a major component of many NIH-funded projects. However, there is
currently a dearth of computational tools for analyzing microbiome time-series data, which presents several
special challenges including high measurement noise, irregular and sparse temporal sampling, and complex
dependencies between variables. The objective of this proposal is to introduce new capabilities, improve on,
and provide state-of-the-art implementations of tools for analyzing dynamics, or patterns of change in
microbiome time-series data. The tools we develop will use Bayesian machine learning methods, which are
well-recognized for their strong conceptual and practical advantages, particularly in biomedical domains. Tools
will be rigorously tested and validated on synthetic and real human microbiome data, including publicly
available datasets and those from collaborators providing 16S rRNA sequencing, metagenomic, and
metabolomics data. We propose three specific aims. For Aim 1, we will develop integrated Bayesian machine
learning tools for predicting population dynamics of the microbiome and its responses to perturbations. These
tools will include a new model that simultaneously learns groups of microbes with similar interaction structure
and predicts their behavior over time, and that incorporates prior phylogenetic information. The model will be
further improved by incorporating stochastic microbial dynamics and errors in measurements throughout the
model. For Aim 2, we will develop Bayesian machine learning tools to predict host status from microbiome
dynamics. The tools will learn easily interpretable, human-readable rules that predict host status from
microbiome time-series data, and will be further extended to handle a variety of longitudinal study designs. For
Aim 3, we will engineer our microbiome dynamics analysis software tools for optimal performance, ease-of-
use, maintainability, extensibility, and dissemination to the community. In total, the proposed work will yield a
suite of contemporary software tools for analyzing microbiome dynamics, with expected broad use and major
impact. The software will allow investigators to answer important scientific and translational questions about
the microbiome, including discovering which microbial taxa or their metagenomes are affected over time by
perturbations such as changes in diet or invasion by pathogens; predicting the effects of these perturbations
over time, including changes in composition or stability of the gut microbiota; and find...

## Key facts

- **NIH application ID:** 10245080
- **Project number:** 5R01GM130777-04
- **Recipient organization:** BRIGHAM AND WOMEN'S HOSPITAL
- **Principal Investigator:** Georg Kurt Gerber
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $312,939
- **Award type:** 5
- **Project period:** 2018-09-20 → 2023-08-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10245080

## Citation

> US National Institutes of Health, RePORTER application 10245080, Bayesian Machine Learning Tools for Analyzing Microbiome Dynamics (5R01GM130777-04). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10245080. Licensed CC0.

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