Research Project Title

A Model of Immune System Response to Tumor Growth

Presenter Information

Lydia Pane, Gonzaga UniversityFollow

Session Type

Traditional Paper Presentation

Research Project Abstract

Nonlinear dynamical models of immune-tumor interaction give insight into which immune defenses are most effective in tumor lysis. We analyze three coupled nonlinear differential equations that incorporate CD8+ T-cells and NK cells to understand the destructive dynamics and possible immunotherapy applications. The accuracy of the T-cell competition term is analyzed through data fitting, and it is concluded that a rational form of the competition term is necessary. We then successfully reproduce the full immune system and the lack NK cell simulations of the mathematical solution, but find a discrepancy in the simulation in the absence ot T-cells. Last, we make two simplifying assumptions in which tumor cell population is significantly larger than T-cell population and vice versa. The analysis of large tumor cell population show that with no competition term of T-cells/tumor cells the model acts the same as when no T-cells are present. When the T-cell population significantly exceeds tumor cells the model shows tumor growth control for certain challenges of tumor.

Session Number

RS3

Location

Weyerhaeuser 205

Abstract Number

RS3-e

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Apr 28th, 9:15 AM Apr 28th, 10:45 AM

A Model of Immune System Response to Tumor Growth

Weyerhaeuser 205

Nonlinear dynamical models of immune-tumor interaction give insight into which immune defenses are most effective in tumor lysis. We analyze three coupled nonlinear differential equations that incorporate CD8+ T-cells and NK cells to understand the destructive dynamics and possible immunotherapy applications. The accuracy of the T-cell competition term is analyzed through data fitting, and it is concluded that a rational form of the competition term is necessary. We then successfully reproduce the full immune system and the lack NK cell simulations of the mathematical solution, but find a discrepancy in the simulation in the absence ot T-cells. Last, we make two simplifying assumptions in which tumor cell population is significantly larger than T-cell population and vice versa. The analysis of large tumor cell population show that with no competition term of T-cells/tumor cells the model acts the same as when no T-cells are present. When the T-cell population significantly exceeds tumor cells the model shows tumor growth control for certain challenges of tumor.