# Genomics of rapid adaptation in the lab and in the wild

> **NIH NIH R35** · STANFORD UNIVERSITY · 2022 · $725,021

## Abstract

Project Summary
Adaptation is the foundational concept in biology. My lab aims to build an empirically and theoretically rich theory
of adaptation. We focus specifically on the inference of adaptation from genomic data and on the study of rapid
evolution in real time. The latter include (i) experimental evolution in yeast (adaptation by de novo mutation in
well-mixed clonal system), (ii) adaptation on seasonal and ecological time-scales in Drosophila (adaptation from
standing variation in a obligately sexual organism), and (iii) tumor initiation, growth, and evolution in experimental
mouse model of lung cancer (engineered de novo and spontaneous alterations in a spatially constrained
context). These projects utilize a diversity of systems, high throughput and well-powered experimental
modalities, and sophisticated and varied analytic frameworks, but they all focus on the overarching need to
understand the dynamics of rapid adaptation. Ultimately, we aim to construct a theory of evolutionary adaptation
that can naturally account for the patterns of evolution established over long periods of time, which are evident
in genomic data, as well as the short-term dynamics of adaptation directly observable in short-term experimental
studies of evolution.

## Key facts

- **NIH application ID:** 10413041
- **Project number:** 5R35GM118165-07
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Dmitri Petrov
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $725,021
- **Award type:** 5
- **Project period:** 2016-06-01 → 2026-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10413041

## Citation

> US National Institutes of Health, RePORTER application 10413041, Genomics of rapid adaptation in the lab and in the wild (5R35GM118165-07). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10413041. Licensed CC0.

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