A hybrid Ornstein-Uhlenbeck-Branching framework unifies microbial and pediatric tumor evolution

Area of Science:

• Evolutionary Biology
• Mathematical Modeling
• Cancer Research

Background:

• Pediatric tumors can relapse even with low mutation burdens, indicating complex evolutionary dynamics.
• Understanding these dynamics requires models that account for both random chance (stochastic variability) and counteracting forces (stabilizing constraints).

Purpose of the Study:

• To develop and validate a novel computational framework, the Ornstein-Uhlenbeck-Branching model, for simulating lineage diversification in evolving populations.
• To apply this model to understand mutation frequency dynamics in bacterial evolution experiments and infer potential applications in cancer research.

Main Methods:

• Developed a hybrid Ornstein-Uhlenbeck-Branching framework combining stochastic trait dynamics with birth-death processes.
• Parameterized the model using data from the *Escherichia coli* long-term evolution experiment (LTEE).
• Conducted forward simulations for predictive modeling and in silico therapy simulations.

Main Results:

• The model accurately reproduced lineage-specific mutation dynamics and branching patterns observed in LTEE.
• Identified distinct evolutionary regimes: high plasticity (priA) and collapse-prone (recG).
• Simulated therapy outcomes, including population dynamics, clonal pruning, and extinction/persistence patterns.

Conclusions:

• The balance between stabilizing forces and stochastic variability quantitatively predicts evolutionary plasticity and persistence.
• The framework offers a foundation for evolution-aware, patient-specific cancer modeling using longitudinal clinical data.