# Combining chemical and computational tools for predictive models of microbiome communities

> **NIH NIH R35** · UNIVERSITY OF PENNSYLVANIA · 2020 · $342,713

## Abstract

ABSTRACT
The gut microbiome has a tremendous impact on health and disease, actively contributing to obesity, diabetes,
inflammatory bowel disease, cardiovascular diseases, and several poorly understood neurological disorders.
We do not yet have the necessary tools to precisely probe these microbial communities, though such tools
could unlock extensive benefits to human health. Elucidating the contributions of individual species or consortia
of bacteria would provide a rational basis for understanding microbiota-controlled disease and lead to novel
therapies. To carry out the fundamental research planned in this proposal, we will tackle three major problems:
First, we will build the first set of molecular tools that effectively and precisely modulate the microbiome
bacteria; second, we will analyze the multiscale dynamics of microbial communities; and third, we will construct
an ingestible biosensor for real-time monitoring of microbiome populations. Although antibiotics and fecal
transplants can reconfigure microbial consortia, they do not precisely target individual bacteria. Conversely,
antimicrobial peptides (AMPs) have evolved to selectively attack pathogenic bacteria but do not target
microbiome bacteria, constituting desirable scaffolds for molecular engineering and potential sources of
microbiome-targeting agents. We will develop a new computational peptide design methodology, based on
classical and hybrid-quantum mechanical molecular dynamics (MD) simulations, to create a groundbreaking
assessment of the dynamical and emergent properties of AMPs. Chemical synthesis and large-scale screening
will confirm predicted selectivity against microbiome species, and a machine learning workflow will connect
sequences of individual peptides to their dynamics and activity. We will then apply the synthetic AMPs to
interrogate the human microbiome by selectively removing species during bacterial consortia experiments, to be
carried out in bioreactors, under regular or anaerobic conditions. We will pair our experiments with whole-cell
metabolic network models, providing a systems biology perspective to the analysis of inter-species interactions.
An integrated ingestible biosensing device will be developed to monitor the microbiome by electrochemically
sensing unique biomarkers from gut microbes. This will provide the first real-time measurements of microbiome
composition and will be integrated to our bioreactors for testing, to ultimately be used for in vivo tests. This
work will build the first set of molecular and computational tools for microbiome engineering and will lay the
foundation to address critical gaps in our understanding of the gut micro-environment, and of the contributions
of gut bacteria to the etiology of disease. Grounded in our demonstrated expertise in synthetic biology,
computer science, microbiology, and electrical engineering, this project will provide a computational-
experimental framework for developing a peptide encyclopedia f...

## Key facts

- **NIH application ID:** 10029354
- **Project number:** 1R35GM138201-01
- **Recipient organization:** UNIVERSITY OF PENNSYLVANIA
- **Principal Investigator:** Cesar de la Fuente
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $342,713
- **Award type:** 1
- **Project period:** 2020-09-05 → 2025-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10029354

## Citation

> US National Institutes of Health, RePORTER application 10029354, Combining chemical and computational tools for predictive models of microbiome communities (1R35GM138201-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10029354. Licensed CC0.

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