Speaker
Description
Cancer affects a countless number of lives across the world each day, and mathematical oncology offers a tool to simulate cancer and potentially improve its treatment. One of the current challenges is the development of resistant populations in response to treatment. In this talk, we present an agent-based model for treatment-resistant prostate cancer, with the aim of gaining insights into the dynamics among different populations of tumor cells in response to varying testosterone levels and different treatment strategies. We present a spatial agent-based model on a discrete lattice, simulating the interactions between testosterone-dependent and testosterone-independent tumor cells. We identify a possible phase transition in the testosterone level in the bloodstream, which could influence which tumor cells dominates the grid. We derive a continuum limit of the discrete model, leading to partial differential equations that describe the tumor’s spatial behavior, allowing us to gain deeper insights into the dynamics of the model. The flexibility of our model allows for its application to other hormonal cancers, and our findings support the promising potential of hormonal manipulation in controlling tumor growth and composition, especially extinction therapy. The mathematical analysis together with simulations provide unique insight into tumor dynamics.
