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Following the Dynamics of Structural Variants in Experimentally Evolved Populations
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Evolutionary dynamics under phenotypic uncertainty.

Vaibhav Mohanty1,2,3, Anna Sappington2,3,4, Eugene I Shakhnovich1

  • 1Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA 02138.

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Summary
This summary is machine-generated.

Probabilistic Phenotype Genetics (ProP Gen) theory introduces a new framework for evolutionary dynamics, revealing how phenotypic uncertainty challenges classical population genetics. This new model explains phenomena like phenotypic buoying and bacterial persister cell dynamics.

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Area of Science:

  • Evolutionary Biology
  • Population Genetics
  • Mathematical Biology

Background:

  • Classical population genetics models, based on stochastic differential equations (SDEs), have been used for decades.
  • These traditional models overlook the pervasive influence of phenotype heterogeneity and noise in biological systems.
  • Phenotypic uncertainty is a critical factor in microbial evolution, cancer progression, and other complex biological dynamics.

Purpose of the Study:

  • To develop a novel mathematical framework, Probabilistic Phenotype Genetics (ProP Gen) theory, for understanding evolutionary dynamics under phenotypic uncertainty.
  • To investigate how phenotypic uncertainty impacts fundamental principles of population genetics and evolutionary processes.
  • To provide a more accurate theoretical and computational approach for modeling biological systems with significant phenotypic variation.

Main Methods:

  • Development of a new class of SDEs to incorporate phenotypic uncertainty into evolutionary models.
  • Analytical derivation and numerical verification of complex phase diagrams for genotype-phenotype coexistence.
  • Creation of a novel discrete-time simulation algorithm, Probabilistic Serial Dilution (ProSeD), designed for systems with phenotypic noise and overlapping generations.

Main Results:

  • Phenotypic uncertainty fundamentally alters classical population genetics tenets, such as the invariance of evolutionary dynamics to fitness shifts.
  • Discovery that 'phenotypic bridges' can accelerate fitness valley crossing, even at low mutation rates.
  • Identification and explanation of 'phenotypic buoying,' where low-fitness phenotypes persist due to high-fitness carriers, leading to complex coexistence phase diagrams.
  • Demonstration that ProP Gen theory accurately models bacterial 'persister' cell dynamics and offers insights into cancer evolution strategies.

Conclusions:

  • ProP Gen theory provides a more realistic framework for evolutionary dynamics by incorporating phenotypic uncertainty.
  • The theory explains previously paradoxical phenomena and offers new predictions for evolutionary trajectories in diverse biological systems.
  • ProP Gen theory and the ProSeD algorithm are essential tools for studying evolution in systems characterized by noise and heterogeneity, including cancer and microbial communities.