# Multi-scale, model-driven exploration of sub-generational gene expression in bacteria: individual consequences, population benefits

> **NIH NIH R01** · STANFORD UNIVERSITY · 2023 · $548,372

## Abstract

Research Summary/Abstract
Our goal is to decipher how a molecular-level event or property can create heterogeneous behavior within a
population, and how this heterogeneity leads to advantages for the population as a whole that are not available
to individual members. We propose to determine how sub-generational gene expression - not only of individual
genes, but also of entire operons containing multiple genes with coordinated functions - creates mixed
populations that are more fit to respond to various environmental cues. This proposal, which deeply integrates
computational modeling and experimental measurement, arose out of our efforts in “whole-cell” modeling of E.
coli, which were reported in Science earlier this year. The E. coli model has predicted a number of surprising
behaviors; most relevant is the finding that a clear majority of the genes in E. coli are transcribed at a rate of
less than once per cell cycle - a phenomenon we call “sub-generational gene expression”. Such expression
can have negative consequences for individual bacteria, but benefits the bacterial population as a whole.
Because bacteria are unable to reliably anticipate future conditions, the population must always be prepared
for any environmental change - but no single bacterium is able to express all of the genes required to respond
to any environment at sufficient levels. Instead, our working hypothesis is that the population is heterogeneous,
comprised of individual members who are each prepared for a small number of possible environments. Thus,
while no single cell is ready for all environments, as a whole the population is prepared for most eventualities.
The colony is thus dominated by individuals, emerging stochastically via expression of sub-generationally
expressed genes, who are the most fit to survive at any given moment. Our groups combine expertise in both
whole-cell and agent-based models, and have been working towards whole-cell population simulations, in
which hundreds or thousands of cells each run an instantiation of the E. coli model. Our Aims are to: (1)
confirm that model-predicted genes are expressed sub-generationally; (2) computationally predict and
experimentally determine the effect of operon structure on sub-generational expression of functionally related
gene pairs; and (3) computationally predict and experimentally determine the phenotypic heterogeneity created
by operon separation in cell populations. The most impactful and pioneering aspects of our proposal are that
we will uncover a fundamental new role for operon structure in prokaryotic gene regulation; that we will
produce an expanded whole-cell model of previously unseen complexity, as well as highly innovative new
modeling technology; and finally, that this work will be the first to utilize a novel multi-scale simulation platform
that combines whole-cell models with agent-based models, including the most exciting experimental
demonstration of whole-cell and whole-colony modeling’s ...

## Key facts

- **NIH application ID:** 10654847
- **Project number:** 5R01GM140008-03
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Markus W Covert
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $548,372
- **Award type:** 5
- **Project period:** 2021-09-22 → 2025-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10654847

## Citation

> US National Institutes of Health, RePORTER application 10654847, Multi-scale, model-driven exploration of sub-generational gene expression in bacteria: individual consequences, population benefits (5R01GM140008-03). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10654847. Licensed CC0.

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