Evolutionary dynamics under phenotypic uncertainty

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.