Mathematical modeling of tumor development and treatments

Theoretical Biology Seminar

Meeting Details

For more information about this meeting, contact Kristin Berrigan, Jessica Conway, Carina Curto, Wenrui Hao, Leonid Berlyand, Timothy Reluga, Vladimir Itskov.

Speaker: Leili Shahriyari, UMass Amherst

Abstract: Colon cancer is the third leading cause of cancer-related deaths in the United States in both men and women. A major clinical challenge is to obtain an effective treatment strategy for each patient or at least identify a subset of patients who could benefit from a particular treatment. Since each colon cancer has its own unique features, it is very important to obtain personalized cancer treatments and find a way to tailor treatment strategies for each patient based on each individual’s characteristics, including race, gender, genetic factors, immune response variations. In this talk, we propose a unique approach to develop a data-driven QSP model to suggest effective treatment for each patient based on gene expression data from the primary tumor samples. Since signatures of main characteristics of tumors, such as immune response variations, can be found in gene expression profiling of primary tumors, we use gene expression data as input. We develop an innovative framework to systematically employ a combination of data science, mathematical, and statistical methods to obtain personalized colon cancer treatment.


Room Reservation Information

Room Number: 106 McAllister

Date: 11/05/2019

Time: 1:30pm - 2:30pm