# Examining the conditionality and time dynamics of evolutionary constraint in yeast

> **NIH NIH F32** · STANFORD UNIVERSITY · 2024 · $73,828

## Abstract

Project Summary
The idea of evolutionary constraint is central to understanding of how populations have and will adapt(ed) in
response to heterogeneous selective pressures. Constraints therefore determine how organisms with relevance
to human health, like pathogens and cancers, adapt to different drugs, hosts, or environments. Constraints can
be formed by strict trade-offs, when mutations adaptive in one environment have antagonistically pleiotropic
costs in alternate environments, or by more dynamic processes, when the ability to select for hypothetically
possible costless generalism is limited by mutational accessibility, speed of selection, or ecological opportunity
for selection. These overlapping processes can make evolutionary constraint difficult to measure and these
different processes difficult to disentangle. Despite the centrality of constraint to evolutionary theory, these
difficulties mean that the processes forming constraints at different levels remain poorly understood—
leaving open questions about how they affect evolution.
In this proposal, I leverage high throughput microbial experimental evolution lineage tracking methods
that provide unprecedented power to measure the distributions of fitness effects for mutations in
multiple environments. These methods allow us to generate quantitative insights into the dynamics of
evolutionary constraint. Aim 1 will ask whether constraints in laboratory experimental evolution resemble
natural variation by measuring the joint distribution of fitness effects (JDFE) for natural isolates of S. cerevisiae
in two nutrient environments mimicking natural conditions (synthetic wine must and synthetic beer wort) and then
experimentally evolving a barcoded laboratory strain in these and an alternating media environment to determine
whether the laboratory evolved JDFE resembles the naturally evolved JDFE. Aim 2 will explore how
evolutionary constraints change over time by leveraging a rebarcoding system to experimentally evolve S.
cerevisiae in the same conditions as Aim 1 for 5 evolutionary `steps' (~400 generations) and measure the JDFE
at each `step'. Most laboratory experimental evolution examines shorter time scales, but we know from long-term
experiments and natural observations that evolutionary dynamics change over time as populations near fitness
peaks and/or change their environment. Thus, Aim 2 will allow us to quantitatively measure how constraints
change over time.
Through this work, I will build a powerful system for conducting high-throughput, long term evolution in multiple
environments. The proposed research will significantly expand my training in the areas of quantitative genetics,
population genetics, and molecular biology while encouraging increasing independence. It will therefore
compliment my PhD training in evolutionary ecology to allow me to build an integrative research program
that links genetic, evolutionary, and ecological processes to understand how constraints determine ...

## Key facts

- **NIH application ID:** 10995985
- **Project number:** 1F32GM155990-01
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Elisa Visher
- **Activity code:** F32 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $73,828
- **Award type:** 1
- **Project period:** 2024-09-01 → 2026-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10995985

## Citation

> US National Institutes of Health, RePORTER application 10995985, Examining the conditionality and time dynamics of evolutionary constraint in yeast (1F32GM155990-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10995985. Licensed CC0.

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