# The contribution of phenotypic diversity and temporal variability to population signal transduction

> **NIH NIH R01** · YALE UNIVERSITY · 2020 · $343,050

## Abstract

Project Summary:
Biological functions are typically performed by groups of cells that predominantly express the same genes yet
display a continuum of phenotypes. The long-term goal of this project is to understand how such variations
influence functional properties at the population level, which is a fundamental problem in cell biology with critical
implications for public health. As a model system, we have been using the bacterial chemotaxis system of
Escherichia coli because it involves non-trivial functions, such as signal detection, amplification, memory and
adaptation, and it is well-characterized molecularly.
During the previous funding cycle, we developed microfluidics and computational technology to measure protein
abundance, swimming behavior, and performance of the same individual cells in a race up a gradient of
attractant. These data revealed that chemotactic performance depends nonlinearly on swimming phenotype,
which in turn depends nonlinearly on protein abundances. These nonlinearities have important consequences:
because the average of a nonlinear function is different from the nonlinear function of the average, the population
could outcompete the performance of its mean phenotype in some conditions. This result illustrates a basic and
ubiquitous mechanism by which phenotypic diversity can modulate function in cell biology, even in the absence
of any interactions among cells.
In this next funding cycle, we plan to examine the consequence of this mechanism for signal transduction by
combining our microfluidics and computational framework with single-cell FRET technology developed by long-
term collaborator Dr. Thomas Shimizu. This new combined platform enables high-throughput single-cell
measurements of signaling dynamics in microfluidics chambers. Using this approach, we will examine how
temporal variations in individual cells, due to spontaneous fluctuations in the pathway (Aim 1) and to cell cycle
regulation (Aim 2), affect their ability to process signals. These aims will also quantify the contribution of these
processes to the standing variation in an isogenic population. Finally, in Aim 3 we will examine how phenotypic
diversity shapes the population’s capability to process signals. Taken together, the proposed aims will go beyond
the population-average characterization of this signaling network to reveal how diverse individual cells process
signals while growing and fluctuating, and how this diversity shapes the population’s signal transduction
capabilities beyond those of its mean phenotype.

## Key facts

- **NIH application ID:** 9901543
- **Project number:** 5R01GM106189-07
- **Recipient organization:** YALE UNIVERSITY
- **Principal Investigator:** Thierry Emonet
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $343,050
- **Award type:** 5
- **Project period:** 2013-04-01 → 2023-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9901543

## Citation

> US National Institutes of Health, RePORTER application 9901543, The contribution of phenotypic diversity and temporal variability to population signal transduction (5R01GM106189-07). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9901543. Licensed CC0.

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