Mathematical Models in Immuno-Oncology

Abstract

Precision medicine employs intricate statistical methods to evaluate relationships between patient profiles and fundamental clinically driven outcomes. Most notably in immunotherapy, precision medicine technologies have enabled revolutionary advances in oncology, particularly in immunotherapy. Mathematical modeling of the dynamic interactions between the patient’s disease and the pharmacological system presents an alternate approach to therapy personalization. In addition, mathematical models may help us better comprehend tumor evolution and the dynamics of cancer, the immune system, and the short- and long-term applications of chemotherapy and immunotherapy. Compared to economics, engineering, and physics, the application of mathematical models has been reasonably limited. In cancer, it is increasingly acknowledged that mathematical modeling is crucial for generating experimental and clinically testable hypotheses and directing study design. A mathematical model reduces biological complexity to a manageable representation that is complicated enough to retain the general qualities of the natural system and sufficiently simple to provide insight that can be utilized to make predictions. Mathematical models can aid in comprehending complicated biological systems by offering approximations and abstractions that can be used to construct testable hypotheses about how biological components evolve and interact to produce a given outcome. While attempting to validate a model, a mathematician typically identifies gaps in biological understanding and calls attention to crucial components that experimental biologists may overlook. This paves the way for future discoveries and raises our understanding of the condition, hence increasing the possibility that successful therapies will be developed.