# Methods for quantifying selection in evolving populations

> **NIH NIH R35** · UNIVERSITY OF CALIFORNIA RIVERSIDE · 2021 · $371,826

## Abstract

PROJECT SUMMARY
Understanding selection in complex evolving populations is a common theme across the biomedical sciences.
Examples include the characterization of driver mutations that lead to cancer, pathogen evolution to escape
human immune responses, and the growth of antibiotic-resistant bacteria. Recent experimental advances have
substantially increased the availability of temporal genetic data, which could be exploited to detect selection with
greater accuracy and precision. However, inferring selection from temporal genetic data remains technically
challenging. The central goal of my research is to develop and apply efficient computational and statistical
methods to quantitatively describe evolutionary dynamics, including the role of selection in evolution. Drawing
on novel approaches derived from statistical physics, we will develop robust, scalable, and interpretable methods
to infer the fitness effects of mutations from temporal genetic data, accounting for features such as genetic
linkage, epistasis, and time-varying selection. These methods will be integrated into a software package in order
to make them more widely accessible to the community. We will focus on two specific applications: 1)
investigating the evolution of human immunodeficiency virus (HIV)-1 to evade adaptive immune responses, a
prototypical example of rapid and complex evolution, and 2) interpreting massively parallel assays of protein
function. Our research program will create new tools for understanding complex evolving populations and apply
them to elucidate host-pathogen coevolutionary dynamics and to improve widely used high-throughput
experimental techniques.

## Key facts

- **NIH application ID:** 10200848
- **Project number:** 5R35GM138233-02
- **Recipient organization:** UNIVERSITY OF CALIFORNIA RIVERSIDE
- **Principal Investigator:** John P Barton
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $371,826
- **Award type:** 5
- **Project period:** 2020-07-01 → 2025-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10200848

## Citation

> US National Institutes of Health, RePORTER application 10200848, Methods for quantifying selection in evolving populations (5R35GM138233-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10200848. Licensed CC0.

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