# A quantitative examination of cellular mechanisms that modulate the impacts of genetic variation - Renewal - 1

> **NIH GM R35** · ARIZONA STATE UNIVERSITY-TEMPE CAMPUS · 2026 · $416,578

## Abstract

PROJECT SUMMARY
Predicting the phenotypic impacts of a mutation is a major goal in biology and medicine. But the paths linking
genotype to phenotype are difficult to navigate. For one, some phenotypes impact others, so the impacts of
mutation can stretch out across networks of related traits. One of my lab’s goals is to investigate the
relationships between basic features of cells (e.g., misfolded protein abundance, levels of protein-folding
chaperones, and cell growth rate) so we can predict some phenotypes from others. But doing so is not enough.
Predicting phenotype is more challenging than this because the impacts of mutation, and the networks of
related traits through which they spread, can change across contexts. By re-measuring the relationships
between traits in many different genetic backgrounds and environments, my lab endeavors to make headway
on one of the major goals of modern biology: predicting the phenotypic impacts of mutation in diverse contexts.
To achieve this goal, my lab conducts high-throughput experiments in the model eukaryote, budding yeast, that
simultaneously quantify the phenotypic impacts of many mutations across many environments. We interpret
these big datasets using diverse mathematical models and machine learning approaches. In some projects, we
quantify the correlations between phenotypes to infer the network through which a mutation’s influence travels
and how that network changes across contexts. For example, I recently measured the correlations between
yeast single-cell morphological traits, how they change across the cell cycle, and how this predicts which traits
are jointly influenced by mutation. In the next five years, my lab plans to apply a similar strategy to study how
the impacts of mutation travel through a regulatory network. In other projects, we deconvolute high-
dimensional data into an abstract genotype-phenotype map that uses shared mutant behavior across
contexts to improve fitness predictions. In the next five y

## Key facts

- **NIH application ID:** 11330587
- **Project number:** 5R35GM133674-07
- **Recipient organization:** ARIZONA STATE UNIVERSITY-TEMPE CAMPUS
- **Principal Investigator:** Kerry A Geiler-Samerotte
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** GM
- **Fiscal year:** 2026
- **Award amount:** $416,578
- **Award type:** 5
- **Project period:** 2019-09-01T00:00:00 → 2030-04-30T00:00:00

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11330587

## Citation

> US National Institutes of Health, RePORTER application 11330587, A quantitative examination of cellular mechanisms that modulate the impacts of genetic variation - Renewal - 1 (5R35GM133674-07). Retrieved via AI Analytics 2026-06-25 from https://api.ai-analytics.org/grant/nih/11330587. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*