VARIANCE-REDUCED SIMULATION OF MULTISCALE TUMOR GROWTH MODELING
Authored by Giovanni Samaey, Annelies Lejon, Bert Mortier
Date Published: 2017
DOI: 10.1137/15m1043224
Sponsors:
Flanders Research Foundation
Agency for Innovation by Science and Technology in Flanders (IWT)
Platforms:
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Model Documentation:
Other Narrative
Mathematical description
Model Code URLs:
Model code not found
Abstract
We are interested in the mean-field evolution of a growing tumor as it
emerges from a stochastic agent-based multiscale model. To this end, we
introduce a hybrid PDE/Monte Carlo variance reduction technique. The
variance reduction on the cell densities is achieved by combining a
simulation of the stochastic agent-based model on the microscopic scale
with a deterministic solution of a simplified (coarse) PDE on the
macroscopic scale as a control variable. We show that this technique is
able to significantly reduce the variance with only the (limited)
additional computational cost associated with the deterministic solution
of the coarse PDE. We illustrate the performance with numerical
experiments in different biological scenarios.
Tags
Agent-based model
proliferation
cancer
progression
invasion
Multiscale modeling
Tumor growth
apoptosis
In-vitro
Mathematical-model
Cells
Variance reduction
Induced angiogenesis