Mathematical oncology: using mathematics to understand cancer progression and treatment
Abstract
Abstract
Over the last several decades, much has been learned about cancer through experimental and clinical research. However, the more we learn about cancer, the more it is recognized that cancer is a multi-faceted disease that depends on a large number of nonlinear, multiscale processes. In this talk, I will introduce the field of mathematical oncology – the subfield of mathematical biology that uses mathematics to elucidate how tumor behavior results from these complex multiscale interactions. Although questions that arise in this field are motivated by the biology, a broad range of mathematical techniques are needed to best address these questions. I will discuss how different mathematical approaches (continuous vs. discrete, deterministic vs. stochastic) can be utilized to answer the types of questions that arise in trying to understand cancer dynamics. Examples to be explored will include a deterministic and continuous model of tumor response to cancer-killing viruses, a stochastic hybrid discrete/continuous model of drug resistance, and a stochastic hybrid discrete/continuous model of tumor response to vascular-targeting drugs.
Citation:
Gevertz, J. (2015, November 14). Mathematical oncology: using mathematics to understand cancer progression and treatment [Conference presentation]. Joint Meeting of the Mathematical Association of America New Jersey Section and the New Jersey Association of Mathematics Teacher Educators, Union, NJ, United States.
Description
Department of Mathematics and Statistics
Rights
File not available for download due to copyright restrictions
URI
http://sections.maa.org/newjersey/PastMeeting/Fall2015/MAANJ_Fall_2015_Meeting_Program.pdfhttp://sections.maa.org/newjersey/Main/index.html
http://dr.tcnj.edu/handle/2900/4283