# Predictive biophysical models of evolution

> **NIH NIH R01** · HARVARD UNIVERSITY · 2020 · $569,400

## Abstract

Project Summary/Abstract
The overarching goal of the proposed research is to develop predictive multiscale biophysical
models of adaptive evolutionary dynamics. In earlier work we demonstrated for several cases of
biomedical importance that fitness effect of genetic variation can be accurately predicted from a
unique combination of molecular traits of the mutated protein. This finding transforms the
concept of fitness landscape from an artful metaphor into a quantitative tractable tool to predict
the genotype-phenotype relationship (GPR). Here we take these findings as a foundation to
further extend our understanding of interplay between biophysical and population factors that
determine the dynamics and outcome of adaptive evolution. We will apply microfluidics and
automatic robotics setup along with protein engineering and genomic editing tools to explore
evolutionary dynamics in laboratory experiments under conditions that allow tight control on all
scales – from molecules to populations. To that end, we carry out a set of evolution
experiments with adapting populations of E. coli escaping from antibiotic stress and structural
instability of the essential protein Dihydrofolate Reductase. We characterize on all scales –
genotyping, molecular traits, systems proteomics and population - multiple evolutionary paths to
resistance and adaption of emerging bacterial strains and determine at which level of
description (genotype, biophysical properties, systems responses) evolution becomes
reproducible – and by implication predictable. In parallel we model the evolutionary dynamics
using multiscale models where cytoplasm of model cells is presented in a biophysically realistic
manner, and fitness of model organisms is predicted from its molecular traits using
experimentally derived GPR. Molecular traits of mutant forms are predicted using state of the art
computational tools of molecular biophysics allowing reproducing and predicting complete
evolutionary trajectories of adapting populations of model cells. A tight integration between
theory and experiment will provide an opportunity to develop predictive evolutionary models of
ever increasing accuracy and realism. Progress along these lines will transform our approaches
to study evolutionary dynamics from descriptive into predictive and quantitative, which will be
instrumental to the development of novel approaches to fight antibiotic resistance and,
potentially, viral escape from stressors such as drugs and immune response.

## Key facts

- **NIH application ID:** 9822975
- **Project number:** 5R01GM068670-16
- **Recipient organization:** HARVARD UNIVERSITY
- **Principal Investigator:** EUGENE I SHAKHNOVICH
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $569,400
- **Award type:** 5
- **Project period:** 2004-04-01 → 2021-11-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9822975

## Citation

> US National Institutes of Health, RePORTER application 9822975, Predictive biophysical models of evolution (5R01GM068670-16). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9822975. Licensed CC0.

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