Strategies for Developing Mathematical Models of Cancer
0Presented by:
Emma Turian, Northeastern Illinois University, Chicago IL USA
https://qubeshub.org/community/groups/simiode/expo
Abstract: The dynamic of cancer, including its response to therapy, is a topic that students majoring in fields in the Biological Sciences are generally interested in. Students with a mathematical or computational academic background enjoy mathematical concepts being introduced using a practical approach. These interests made teaching an interdisciplinary mathematical modeling of cancer course possible. We introduce a technique for building a system of ordinary differential equations modeled up from data, diagrams, and assumptions. Our model is about tumor evolution under therapy, and we illustrate how this system of ordinary differential equations is developed starting with data representing malignant tumor growth. Initial methodologies employed to estimate parameter values consist of dynamic parameter estimation, and model fitting techniques including different versions of sum of squares minimization using Solver. These strategies are then compared with machine learning’s outcomes. Initial tumor growth models are then modified according to diagrams and assumptions to reflect the inclusion of therapy for tumor reduction. Along the way a variety of methods, R software codes, and free resources are mentioned, and some are employed to illustrate outcomes.