Deterministic and stochastic analysis of an oncolytic infection model

Mathematical models of biological processes have had a history of high predictive power of both experimental data and preliminary drug tests. Models that aim to predict the dynamics of oncolytic virus infections have previously focused solely on 3 populations, i.e., the cancer cells, virus-inflicted cancer cells, and the virus itself. Considering that most tumor populations have a substantial proportion of healthy cells, an oncolytic model that rightly adjusts for this increased fraction would provide insight into dual cell dynamics. Our findings elucidated that certain parameter variations with the tumor proliferation, infection, virus replication, and infected cell dissolution rates eventually allowed for the negation of cancer cells while simultaneously keeping the healthy cells alive. A stochastic interpretation of the model was further utilized to more authentically characterize the probabilistic events during an infection. In addition to preventing the oscillatory behavior observed with the deterministic model, it also confirmed the initial denouement of an extinct cancer population and thriving non-cancerous cells.