# Relationship between genealogies and biophysical processes during spatial growth.

> **NIH NIH R01** · BOSTON UNIVERSITY (CHARLES RIVER CAMPUS) · 2022 · $288,750

## Abstract

Project Summary / Abstract
Population dynamics are central to many pressing problems in biomedicine. Whether we look at
epidemics, microbiome, or cancer, we need to understand how populations grow, spread, and
evolve. The outcome of these processes is largely controlled by ecological and genetic diversity
of the population. Moreover, the patterns of diversity are often the only available cues about the
factors that drive population dynamics. Although a lot of effort went into characterizing the
diversity of stationary populations (both well-mixed and spatially structured) the understanding of
evolutionary processes in growing populations is much more limited. Our recent work found that
seemingly innocuous changes in the growth dynamics can fundamentally alter how populations
evolve during spatial expansions. To understand such phenomena, we developed powerful
theoretical tools, which lead to the discovery of hidden universality classes in the standard
reaction-diffusion models of population genetics. Preliminary data strongly supports the
hypothesis that each universality class has a unique structure of genealogies. Moreover, neutral
evolution in some spatially expanding populations seems to produce genealogies identical to
those in rapidly-adapting well-mixed populations, which suggests that some common signatures
of selection need to be revisited. The first aim is to develop this theory further and test it in
numerical simulations. The second aim is to examine how the universal behavior of genealogies
is affected by common biophysical process, which are neglected in standard one-component
reaction-diffusion models. Specifically, we will extend our theory of evolutionary dynamics to
include the influence of mechanical pressure, nutrient diffusion, and movement in response to
environmental gradients. The third aim is focused on establishing a connection between genetic
diversity and growth instabilities that produce typical population morphologies. Taken together,
these lines of research will lay the groundwork to interpret spatially-resolved genetic data and use
it to predict and control the course of evolution. Such capabilities are essential for our fight against
cancer, antibiotic resistance, and epidemics. The mathematical innovations developed in the
course of this work should also be useful across a wide set of applications because reaction-
diffusion models find numerous uses in chemistry, biology, and medicine.

## Key facts

- **NIH application ID:** 10432089
- **Project number:** 5R01GM138530-03
- **Recipient organization:** BOSTON UNIVERSITY (CHARLES RIVER CAMPUS)
- **Principal Investigator:** Kirill Sergeevich Korolev
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $288,750
- **Award type:** 5
- **Project period:** 2020-09-11 → 2025-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10432089

## Citation

> US National Institutes of Health, RePORTER application 10432089, Relationship between genealogies and biophysical processes during spatial growth. (5R01GM138530-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10432089. Licensed CC0.

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