Bayesian calibration of a stochastic, multiscale agent-based model for predicting in vitro tumor growth

Hybrid multiscale agent-based models (ABMs) are unique in their ability to simulate individual cell interactions and microenvironmental dynamics. Unfortunately, the high computational cost of modeling individual cells, the inherent stochasticity of cell dynamics, and numerous model parameters are fu...

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Bibliographic Details
Main Authors:	Faghihi, D. (Author), Lima, E.A.B.F (Author), Philley, R. (Author), Phillips, C.M (Author), Virostko, J. (Author), Yang, J. (Author), Yankeelov, T.E (Author)
Format:	Article
Language:	English
Published:	Public Library of Science 2021
Subjects:	accuracy agent based model analytical error Article Bayes theorem Bayes Theorem biological model biology breast carcinoma cell line Breast Neoplasms breast tumor calibration cancer cell cell compartmentalization cell death Cell Death Cell Line, Tumor cell population cell proliferation Cell Proliferation cell survival Cell Survival Computational Biology computer simulation Computer Simulation concentration (parameter) controlled study female Female glucose Glucose growth factor human human cell Humans in vitro study Intercellular Signaling Peptides and Proteins Likelihood Functions Markov chain metabolism microscopy Models, Biological pathology physiology reaction diffusion model sensitivity analysis signal peptide simulation spatiotemporal analysis Spatio-Temporal Analysis statistical model stochastic model Stochastic Processes system analysis Systems Analysis tumor cell line tumor growth tumor microenvironment Tumor Microenvironment
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Bayesian calibration of a stochastic, multiscale agent-based model for predicting in vitro tumor growth

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