Using Dynamic Active Subspaces to Construct Surrogate Models for Calibrating Tumor Growth Models to Data

While the use of complex ODE models describing tumor growth and tumor-immune interactions may be desirable for accurately representing the biological mechanisms that govern tumor growth, the high-dimensionality of the parameter space can make it difficult to uniquely identify optimal parameter estimates during model calibration. Construction of a surrogate model that can accurately approximate the quantity of interest with fewer parameters may enable us to fit this alternate model to the data in place of the original ODE system. In this investigation, we consider how active subspaces may be used to construct time-dependent surrogate models with reduced parameter dimension, and analyze the trade-offs in parameter identifiability, model fit error, and computational run-time to provide a roadmap for ensuring an identifiable parameter set during model calibration.

Title	Using Dynamic Active Subspaces to Construct Surrogate Models for Calibrating Tumor Growth Models to Data
Creator	Lewis, Allison L.
Creator	Vu, P. N.
Publisher	PUMP Journal of Undergraduate Research
Academic Department	Mathematics
Division	Natural Sciences
Organization	Lafayette College
Date Issued	2023
Date Available	2023-01-26
Type	Article
Language	English
Keyword	dynamic active subspaces
	activity scores
	surrogate models
	model calibration
	mathematical oncology
Bibliographic Citation	Vu, P. N. and A. L. Lewis (2023) "Using Dynamic Active Subspaces to Construct Surrogate Models for Calibrating Tumor Growth Models to Data." PUMP Journal of Undergraduate Research 6: 1-28.
Standard Identifier	Handle 10385/b8515p98x
Permalink	http://hdl.handle.net/10385/b8515p98x
Rights Statement	In Copyright
Rights Holders	Vu, P. N.
Rights Holders	Lewis, Allison L.