Table 1.xls (9.5 kB)

Model parameters.

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posted on 2021-11-29, 18:58 authored by Ernesto A. B. F. Lima, Danial Faghihi, Russell Philley, Jianchen Yang, John Virostko, Caleb M. Phillips, Thomas E. Yankeelov

Model parameters.

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

PLOS Computational Biology

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reduced parameter space quantifying uncertainties due different initial conditions 78 &# 177 61 &# 177 modeling individual cells cancer cells grown resolved microscopy measurements high computational cost efficient calibration using cellular scale ).tumor cell confluences dead cell populations numerous model parameters estimate model parameters different glucose concentrations multiscale model consists experimental data consists based bayesian inference predict tumor dynamics dead cells model parameters cell dynamics microscopy data experimental data bayesian calibration based model tumor microenvironment tumor growth multiscale agent microenvironmental dynamics bayesian framework scale abm vitro </temporal evolution staying within standard deviation reliably predict relative importance prediction compared growth factors grained two fundamental limitations four days computational time calibrated within average error

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