# Mathematical ecology models of host-microbiota interaction in auto microbiota transplants (auto-FMT)

> **NIH NIH R01** · SLOAN-KETTERING INST CAN RESEARCH · 2020 · $804,721

## Abstract

Project Summary
Mathematical ecology models of host-microbiota interaction
 in auto microbiota transplants (auto-FMT)
We aim to develop mathematical models for the rational design of microbiota transplants that can restore
compositional diversity and function to the damaged microbiota of antibiotic-treated patients. We will focus on
hospitalized cancer patients receiving allogeneic hematopoietic stem cell transplants (allo-HSCT). Allo-HSCT
is a potentially curative cancer treatment that compromises the immune system, and requires that patients
receive massive antibiotic treatments to prevent and treat life-threatening infections. We will build on a vast
clinical database, in vitro experiments in bioreactors and in vivo experiments with mice to develop dynamic
mathematical models that describe how antibiotics cause changes in the microbial composition, and how that
can impact the recovery of the host's immune system after allo-HSCT. The model expands approaches
pioneered by our team—the Generalized Lotka Volterra Ecological Regression (GLOVER) and agent-based
models—towards a model that can assist in the development of microbiota therapies for patients undergoing
allo-HSCT.
 In aim 1 we will use data from a unique clinical resource available at the Memorial Sloan Kettering
Cancer center—a sample bank obtained from >1,500 allo-HSCT patients (including microbiome 16S rRNA and
shotgun sequencing) and extensive clinical metadata (including time series of complete blood counts and time
and doses of all drugs given while the patients are hospitalized); we will also use data from a first-of-its-kind
controlled randomized trial of autologous fecal microbiota transplant (auto-FMT) undergoing in allo-HSCT
patients. We will use these unique resources to parameterize our models and investigate how the microbiota
composition influences the recovery of the host immune system. In aim 2 we will validate the microbial
component of our mathematical model using experimental data from anaerobic laboratory reactors that
recreate—in vitro—the human microbiota dynamics during antibiotic treatment and auto-FMT in the absence of
a living host. In aim 3 we will develop mouse models to investigate those same microbiota dynamics
experimentally but now in the context of a living host.
 The data obtained from these clinical studies, in vitro experiments and in vivo models will refine our
mathematical models in close cycles of simulation and quantitative experimentation. Our ultimate goal is to
develop models that can define optimal microbial cocktails and reconstitute the perturbed microbiota of allo-
HSCT patients. In the process we hope to uncover general principles of microbiota ecology for future therapies
in other patient populations whose microbiota is damaged by antibiotic treatments.

## Key facts

- **NIH application ID:** 9854885
- **Project number:** 5R01AI137269-02
- **Recipient organization:** SLOAN-KETTERING INST CAN RESEARCH
- **Principal Investigator:** Ying Taur
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $804,721
- **Award type:** 5
- **Project period:** 2019-02-01 → 2024-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9854885

## Citation

> US National Institutes of Health, RePORTER application 9854885, Mathematical ecology models of host-microbiota interaction in auto microbiota transplants (auto-FMT) (5R01AI137269-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9854885. Licensed CC0.

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