# Biophysical foundations of evolutionary dynamics

> **NIH NIH R35** · HARVARD UNIVERSITY · 2021 · $732,555

## Abstract

The overarching goal of this research is to develop predictive multiscale biophysical models of adaptive
evolutionary dynamics. The new concept of Biophysical Fitness Landscape (BFL) is a map of protein/nucleic
acid molecular properties to fitness. We demonstrated the conceptual validity of BFL by discovering a simple
and accurate quantitative relationship between fitness of E. coli and molecular properties of important core
metabolic enzymes. 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 biophysical analysis, automated
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.
As a key model 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 metabolomics 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. 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 BFL. Comprehensive molecular mapping of possible escape
routes will provide an opportunity to rationally design new class of compounds – “evolution drugs” - that
comprehensively block pathogen’s resistance. In a related effort we will explore the biophysical underpinnings
of codon adaptation to discern their effects on mRNA and cotranslational protein folding. 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:** 10086190
- **Project number:** 1R35GM139571-01
- **Recipient organization:** HARVARD UNIVERSITY
- **Principal Investigator:** EUGENE I SHAKHNOVICH
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $732,555
- **Award type:** 1
- **Project period:** 2021-06-01 → 2026-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10086190

## Citation

> US National Institutes of Health, RePORTER application 10086190, Biophysical foundations of evolutionary dynamics (1R35GM139571-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10086190. Licensed CC0.

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