Assessing the Flexibility of Deterministic Tumor-Immune ODE Models for Fitting Stochastic Synthetic Data
Lafayette College
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Description
This thesis uses math modeling to investigate the fundamentals of immunotherapy — a tumor treatment that works directly with the immune system to kill tumor cells. Specifically, we study the interaction between immune and tumor cells. Because real-world clinical data is hard to access, we implement PhysiCell, a stochastic ABM, to generate synthetic tumor data and use the data as a representation of truth. Meanwhile, we implement two deterministic ODE models that describe the effect of the immune system on tumor growth but with different levels of complexity. Our goal is to determine the level of complexity needed for the deterministic ODE model to replicate the results generated by a stochastic ABM. We use Bayesian inference to fit the model trajectories produced by the ODE models to the synthetic ABM data. Following that, we use AIC and the posterior distribution curves from Bayesian inference as criteria to compare the two ODE models. We seek to determine whether models that are simple enough for clinical use can still be flexible enough to mimic clinical data.
Title
Assessing the Flexibility of Deterministic Tumor-Immune ODE Models for Fitting Stochastic Synthetic Data
Digital collection of student honors theses, beginning in academic year 2021-2022.
Past theses written by Lafayette students through academic year 2020-2021 are kept in Special Collections and College Archives. Information about the honors theses in Special Collections is available in the Library Catalog.