# Probabilistic deep learning models and integrated biological experiments for analyzing dynamic and heterogeneous microbiomes

> **NIH NIH R35** · BRIGHAM AND WOMEN'S HOSPITAL · 2024 · $447,500

## Abstract

Our microbiomes, or the collections of trillions of micro-organisms that live on and within us, are highly dynamic
and have been implicated in a variety of human diseases. Sophisticated computational approaches are critical
for analyzing increasing quantities and types of microbiome data, including time-series, assays for non-bacterial
components of the microbiome, and multiple measurement modalities such as metabolite and gene expression
levels. Another exciting recent trend in the field has been translational applications, particularly live bacterial
therapies for treating human diseases. In parallel, the field of machine learning has been advancing with deep
learning technologies that have dramatically improved applications such as speech and image recognition. My
lab develops novel machine learning methods and experimental approaches for understanding the microbiome,
with a particular focus on microbial dynamics and bacteriotherapies. In the past five years, we have developed
new computational methods and released open-source software tools for assessing the consistency of changes
in the microbiome induced by therapeutics, forecasting population dynamics of microbiomes, and predicting the
status (e.g., presence of disease) of the human host from changes in the microbiome over time. I have also led
experimental efforts to delineate the role of bacteriophages in microbiome dynamics and to develop gut
metabolite-based biomarker assays to predict recurrence of C. difficile infection. Additionally, with collaborators,
we have developed candidate bacteriotherapies for C. difficile infection and food allergies. My overall vision for
my lab in the next five years is to leverage deep learning technologies to advance the microbiome field beyond
finding associations in data, to accurately predicting the effects of perturbations on microbiota, elucidating
mechanisms through which the microbiota affects the host, and improving bacteriotherapies to enable their
success in the clinic. I plan to accomplish this by developing new deep learning models that address specific
challenges for the microbiome, including noisy/small datasets, highly heterogenous human microbiomes, the
need for direct interpretability of model outputs, complex multi-modal datasets, and constraints imposed by
biological principles. My plan is to directly couple computational models and biological experiments through
reinforcing cycles of predicting, testing predictions with new experiments, and improving models. Approaches I
will pursue include incorporating into deep learning models probability, embeddings of microbes and other
entities using rich information (such as gene content or chemical structure), decomposition of multi-modal data
into interpretable and interacting groups, and automated design of new biological experiments in gnotobiotic
mice that seek to maximize information for computational models and ultimately improve engraftment and
efficacy of candidate bacteriotherapies....

## Key facts

- **NIH application ID:** 10907420
- **Project number:** 5R35GM149270-02
- **Recipient organization:** BRIGHAM AND WOMEN'S HOSPITAL
- **Principal Investigator:** Georg Kurt Gerber
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $447,500
- **Award type:** 5
- **Project period:** 2023-09-01 → 2028-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10907420

## Citation

> US National Institutes of Health, RePORTER application 10907420, Probabilistic deep learning models and integrated biological experiments for analyzing dynamic and heterogeneous microbiomes (5R35GM149270-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10907420. Licensed CC0.

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