Modeling and identification of evolutionary dynamics to predict anti-cancer drug response

This thesis deals with the study of cancer treatment models described with evolutionary dynamics. Developing mathematical models which describe the evolution of tumors over time may be potentially useful for oncologists to predict cancer evolution via simulations. This work contributes to the existing literature with the development of a new nonlinear dynamical model, which considers chemotherapy treatment inside a heterogeneous tumor environment. Finally, a preliminary fitting of cancer treatment models is performed with an identification approach by making use of initial experimental data.