Stochastic biophysical modeling of irradiated cells

TL;DR

This paper introduces a stochastic biophysical model of irradiated cells using a Quasi-Markov Chain Monte Carlo approach, capturing complex cellular responses to radiation with potential applications in cancer and radiotherapy research.

Contribution

It presents a novel universal stochastic model incorporating biophysical probabilities and adaptive responses, enhancing simulation of irradiated cell behavior.

Findings

Successfully models cancer transformation and mutations

Simulates radioadaptation and radiotherapy effects

Provides a broad mathematical framework for cell irradiation

Abstract

The paper presents a computational stochastic model of virtual cells irradiation, based on Quasi-Markov Chain Monte Carlo method and using biophysical input. The model is based on a stochastic tree of probabilities for each cell of the entire colony. Biophysics of the cells is described by probabilities and probability distributions provided as the input. The adaptation of nucleation and catastrophe theories, well known in physics, yields sigmoidal relationships for carcinogenic risk as a function of the irradiation. Adaptive response and bystander effect, incorporated into the model, improves its application. The results show that behavior of virtual cells can be successfully modeled, e.g. cancer transformation, creation of mutations, radioadaptation or radiotherapy. The used methodology makes the model universal and practical for simulations of general processes. Potential biophysical curves and relationships are also widely discussed in the paper. However, the presented theoretical model does not describe the real cells and tissues. Also the exposure geometry (e.g., uniform or non-uniform exposure), type of radiation (e.g., X-rays, gamma rays, neutrons, heavy ions, etc.) as well as microdosimetry are not presently addressed. The model is focused mainly on creation of general and maximal wide mathematical description of irradiated hypothetical cells treated as complex physical systems.