# Information Integration and Energy Expenditure in Eukaryotic Gene Regulation

> **NIH NIH R01** · HARVARD MEDICAL SCHOOL · 2024 · $470,251

## Abstract

Project Abstract
Gene regulation – how genes are turned on in the right place, at the right time and in the right amount – is a
problem central to most areas of biology and medicine. Our understanding of gene regulation arose from
classical studies in bacteria: proteins called “transcription factors” (TFs) bind to regulatory DNA sequences and
recruit RNA polymerase (RNAP). The situation in eukaryotes is far more complicated. For example, eukaryotic
DNA is packaged around nucleosomes into chromatin and external sources of energy, such as ATP, are used
to reorganise chromatin, remodel nucleosomes and post-translationally modify regulatory proteins. Pioneering
studies from several laboratories have identified many of the molecular components involved in this regulatory
complexity. However, the quantitative concepts used to reason about eukaryotic gene regulation are still
largely based on the bacterial paradigm. Our work focuses on addressing this alarming gap. Previously, we
developed a strategy of “following the energy” by integrating mathematical models rooted in physics with
quantitative and synthetic experiments in the early Drosophila embryo. The fruit fly offers an unrivaled model
system for measuring and perturbing gene regulation in a living organism. The mathematics exploits a graph-
based approach to Markov processes that permits algebraic calculation of required quantities. This allowed us
to identify the functional limits to energy expenditure, while avoiding fitting models to data or numerically
simulating differential equations. We have provided strong evidence that energy expenditure away from
thermodynamic equilibrium is essential for the functional properties of eukaryotic genes. In the present
proposal, we build on this previous strategy. We hypothesize that data from the Drosophila hunchback gene
cannot be accounted for by any thermodynamic equilibrium model of regulated recruitment of RNAP, no matter
how complicated the molecular details. We believe we can exploit a method of “coarse graining” within the
linear framework to establish this remarkably powerful result. We will then extend our experimental methods
and modeling beyond regulated recruitment, to analyze the dynamics of RNAP itself and the stochastic
production of mRNA. We will introduce real-time imaging of mRNA and optogenetic perturbations of TFs to
measure quantitative aspects of gene expression, and will extend our algebraic methods to accommodate
such data. We hypothesize that energy expenditure in gene regulation is essential to modulate RNAP
dynamics and generate the observed stochastic patterns of hunchback mRNA expression. Our efforts will
formulate a new model of hunchback that integrates regulation, energy expenditure, RNAP dynamics and
mRNA stochasticity.

## Key facts

- **NIH application ID:** 10913461
- **Project number:** 5R01GM122928-08
- **Recipient organization:** HARVARD MEDICAL SCHOOL
- **Principal Investigator:** Angela H DePace
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $470,251
- **Award type:** 5
- **Project period:** 2017-04-10 → 2025-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10913461

## Citation

> US National Institutes of Health, RePORTER application 10913461, Information Integration and Energy Expenditure in Eukaryotic Gene Regulation (5R01GM122928-08). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10913461. Licensed CC0.

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