# Control of heterogeneous microbial communities using model-based multi-objective optimization

> **NIH NIH R01** · UNIVERSITY OF CONNECTICUT SCH OF MED/DNT · 2020 · $421,753

## Abstract

PROJECT SUMMARY
The project addresses an important biomedical problem: how to control biofilms formed by Candida albicans, a
dimorphic fungus that is an important cause of both topical and systemic fungal infection in humans, in particular
immunocompromised patients. It is responsible for 85-95% of all vaginal infections resulting in doctor visits. C.
albicans biofilms also form on the surface of implantable medical devices, and are a major cause of nosocomial
infections. In recent years, it has been recognized that interactions with bacterial species integrated into biofilms
can affect C. albicans virulence and other properties, It is therefore important to understand the interactions of
C. albicans with bacterial species, in particular metabolic interactions. The next step then is to understand and,
ultimately, control how varying compositions of the different microbial species affect their metabolic state and
their ability to form biofilms. This project approaches the problem through model-based design of optimal
compositions of the bacterial species for control of fungal growth. This will be accomplished through a
combination of the construction of a novel computational model of a heterogeneous biofilm consisting of bacterial
as well as fungal species, and novel mathematical tools for dimension reduction and optimization.
 The outcome of the project will be a better understanding of the relationship between bacterial and fungal
species in a biofilm and its therapeutic potential through the construction of a predictive agent-based
computational model. Another outcome will be a mathematical tool that enables the use of mathematical models
for the purpose of designing optimal controls for fungal growth in heterogeneous biofilms. The applicability of the
results of this project extends far beyond biofilms into all areas of medicine and healthcare that are amenable to
agent-based modeling, such as studies of the human microbiome.

## Key facts

- **NIH application ID:** 10267334
- **Project number:** 5R01GM127909-03
- **Recipient organization:** UNIVERSITY OF CONNECTICUT SCH OF MED/DNT
- **Principal Investigator:** Anna I Dongari-Bagtzoglou
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $421,753
- **Award type:** 5
- **Project period:** 2018-04-01 → 2022-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10267334

## Citation

> US National Institutes of Health, RePORTER application 10267334, Control of heterogeneous microbial communities using model-based multi-objective optimization (5R01GM127909-03). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10267334. Licensed CC0.

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