# Revealing Stochastic Switches in Bacteria

> **NIH NIH R01** · NEW YORK UNIVERSITY · 2020 · $370,597

## Abstract

PROJECT SUMMARY
Stochastic switches are a broad class of genetic mechanisms that enable single cells to switch certain genes
on and off randomly, without responding to their environment. Such switches are prevalent in pathogenic
bacteria, where they are often involved in generating diverse surface protein repertoires across the bacterial
population, which enables a subset of cells to avoid detection by the immune system. In general, stochastic
switches provide a strategy for survival in fluctuating environments, by maintaining subpopulations of cells in
pre-adapted states that are prepared for future, possibly unpredictable, environmental stresses. In particular,
these strategies are known to be important in antibiotic persistence, a bacterial phenotypic state consisting of
slow growth and enhanced tolerance for antibiotics.
This grant applies highly sensitive single-cell measurements combined with mathematical models to study
three major facets of stochastic switching. We use synthetic stochastic switches to drive antibiotic resistance
genes, and by measuring the population dynamics under antibiotic pulses over multi-day experiments, we
quantify and model the emergence of resistance, a process of major clinical importance. We use stochastic
switches as a model to study the evolutionary pressures that populations experience when transferred from
one environment to another through population bottlenecks, a key component of disease transmission. And,
we study antibiotic persistence in Escherichia coli, where a continuum of growth states across a bacterial
population can confer varying degrees of antibiotic tolerance. By using novel methods for analysis of single cell
population data mapped with phenotypic information, we investigate the genetic network that underlies
bacterial persistence.
The proposed research will substantially advance understanding of the role of stochasticity in bacterial
adaptation. Through its emphasis on predictive mathematical modeling, the research will provide the ability to
predict the impact of treatment protocols on the emergence of antibiotic resistance and on levels of
persistence, and to identify new ways of slowing down or reversing these complex, biomedically relevant
processes.

## Key facts

- **NIH application ID:** 9836856
- **Project number:** 5R01GM097356-09
- **Recipient organization:** NEW YORK UNIVERSITY
- **Principal Investigator:** EDO L KUSSELL
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $370,597
- **Award type:** 5
- **Project period:** 2011-09-15 → 2022-09-24

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9836856

## Citation

> US National Institutes of Health, RePORTER application 9836856, Revealing Stochastic Switches in Bacteria (5R01GM097356-09). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9836856. Licensed CC0.

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