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Constrained Diffusion as a Paradigm for Evolution.

Daniel Lazarev1,2,3,4,5, Anna Sappington2,6,7, Grant Chau8

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This summary is machine-generated.

We developed DiffEvol, a computational framework modeling evolution as constrained diffusion. This method reveals evolutionary forces and predicts pathogen emergence by analyzing genomic data, including SARS-CoV-2 vaccine impacts.

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Area of Science:

  • Computational Biology
  • Evolutionary Dynamics
  • Genomics

Background:

  • Understanding evolutionary forces is crucial in computational biology.
  • Evolution is influenced by biological, physical, and environmental factors impacting viability.
  • Genomic data holds key insights into evolutionary processes.

Purpose of the Study:

  • To introduce DiffEvol, a novel framework for modeling evolution as constrained diffusion.
  • To estimate evolutionary constraints and viable genotype manifolds from genomic data.
  • To analyze pathogen evolution, including SARS-CoV-2, and improve forecasting.

Main Methods:

  • DiffEvol framework models evolution as constrained diffusion over discrete genotype space.
  • Inverts diffusion dynamics using genomic sequence data to recover constraint functions.
  • Applies the framework to SARS-CoV-2 genomic data from 2020-2024.

Main Results:

  • Reconstructed constraint functions mirroring known viral fitness trends, including a vaccine-induced phase transition.
  • Identified a clear representation of evolutionary features and trends through constraint subspace.
  • Demonstrated the ability to link sequence mutations to an evolving fitness landscape.

Conclusions:

  • DiffEvol provides a mathematical language for evolutionary dynamics under constraints.
  • Framework enhances forecasting of emergent strains and reverse time analyses of pathogen evolution.
  • Applicable to diverse evolutionary phenomena like vaccine resistance and protein evolution.