Mathematical Oncology 2
Graduate course, University of South Florida (USF) and Moffitt Cancer Center, Department of Integrated Mathemtical Oncology (IMO), 2019
I was the course director and taught together with other faculty members of the IMO every Spring from 2019 to 2021.
Course Description This is a deep-focus course on the data-driven development of mathematical models of tissue homeostasis, cancer development, and treatment response to answer specific open questions in cancer biological and clinical oncology.
Course Purpose The IMO2 Integrated Mathematical Oncology course is an intense course that focuses on the data-driven development of mathematical models for clinical and biological insights. Topics include modeling for tissue homeostasis, oncogenesis, metastasis formation, radiation therapy, chemotherapy, immunotherapy, and cancer screening. Students are expected to have completed the IMO1 course before enrolling. Individual lectures will provide recent primary research articles, and students are expected to participate in analyzing these papers as part of their studies.
Course Objectives The primary objective of this course is to provide an understanding of how to develop and test data-driven mathematical models of biological questions relevant to cancer. Students will understand how to select the appropriate modeling approach, how to fit mathematical models to data, how to analyze dynamics, and how to make testable predictions. Students will supplement the lecture information and primary research paper reading by implementing appropriate model systems from the primary assigned textbooks.
Student Learning Outcomes In written mid-term and oral end-term exams, students will demonstrate the ability to build mathematical and computational models purposely for specific research questions utilizing specific biological/clinical knowledge and data.
Required Texts and/or Readings and Course Materials
Introduction to Mathematical Oncology. Yang Kuang, John D. Nagy, Steffen E. Eikenberry, 2016 by Chapman and Hall/CRC, ISBN 978-1584889908
Dynamics of Cancer: Mathematical Foundations of Oncology. Dominik Wodarz, Natalia Komarova, 2014 World Scientific Publishing, ISBN 978-9814566360
Some coding experience in Matlab, Python, Java, Julia, Mathematica
Supplementary (Optional) Texts and Materials
Mathematical Biology I: An Introduction, J. D. Murray, 2007 (3rd edition), Springer. ISBN 978-0387952239
Mathematical Biology II: Spatial Models and Biomedical Applications, 2011 (3rd edition), Springer. ISBN 978-0387952284
Evolutionary Dynamics: Exploring the Equations of Life, M. A. Nowak, 2006, Belknap Press. ISBN 978-0674023383
Evolutionary Game Theory, Natural Selection, and Darwinian Dynamics, T. S. Vincent & J. S. Brown, 2005, Cambridge University Press. ISBN 978-0521841702
Matrix Population Models: Construction, Analysis, and Interpretation, H. Caswell, 2006, Sinauer Associates. ISBN 978-0878931217