Analyzing the Trade-offs Between Model Complexity and Parameter Identifiability in Math Modeling of Tumor Dynamics
Lafayette College
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Description
Mathematicians, biologists, and medical practitioners alike can benefit from modelling complex biological systems---such as describing tumor growth dynamics---using mathematical models. In fact, being able to accurately model these systems can inform clinical treatment decisions. However, models that capture the complex reality of the physical system can be difficult to uniquely parameterize given the sparsity of available data; some model simplification is often necessary. Therefore, we ask: how much complexity does a model need in order to accurately describe the given data, yet still maintain parameter identifiability?
This thesis examines the trade-offs between model complexity and parameter identifiability of such models. It investigates a number of different tumor dynamics models, perform model calibration, and develops a procedure for identifiability analysis on these models.
Title
Analyzing the Trade-offs Between Model Complexity and Parameter Identifiability in Math Modeling of Tumor Dynamics
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.