Stochastic Biophysics of Cellular Radiosensitivity: From Molecular Noise and Repair Kinetics to Evolutionary Demographics

Radiation-induced DNA double-strand breaks (DSBs) are ultimately responsible for cellular mortality, mutagenesis, and severe evolutionary bottlenecks. Classical models, such as the Linear-Quadratic (LQ) framework, can predict population survival patterns, but they do not capture the inherent single-cell stochasticity that drives critical events like tumor recurrence or the development of radioresistant cells.

To address this gap, researchers created a mathematically accurate stochastic differential equation (SDE) framework to model continuous DSB induction and repair as a Feller square-root process. By obtaining exact closed-form expressions for the foci moments, a highly efficient Maximum Likelihood Estimation (MLE) pipeline was developed.

This pipeline can bypass the need for computationally intensive Monte Carlo simulations while still extracting deterministic repair velocities and intrinsic molecular noise from empirical single-cell $\gamma$-H2AX data. This kinetic model, when combined with a cumulative damage hazard through the Feynman-Kac formalism, accurately reconstructs the macroscopic LQ survival pattern from microscopic first principles.

Sensitivity analysis reveals a fundamental evolutionary duality: while initial damage accumulates additively, the ultimate cellular fate is determined by a nonlinear survival response. This response is influenced by the balance between the damage hazard rate and the strength of intrinsic molecular noise. Remarkably, the researchers found that molecular noise actually increases population survival, acting as a non-genetic bet-hedging mechanism that buffers the population by favoring cells with brief periods of low damage loads.

This exact stochastic framework successfully connects microscopic biophysics with macroscopic demographics, providing profound mechanistic understanding of the evolutionary origins of radioresistance.