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Parameterization of Lattice-Based Tumor Models from Data.

Nick Jagiella 1, 2 
1 BANG - Nonlinear Analysis for Biology and Geophysical flows
LJLL - Laboratoire Jacques-Louis Lions, Inria Paris-Rocquencourt
Abstract : In order to establish a predictive model for in-vivo tumor growth and therapy, a multiscale model has to be set-up and calibrated individually in a stepwise process to a targeted cell type and di erent environments (in-vitro and in-vivo). As a proof of principle we will present the process chain of model construction and parametrization from di erent data sources for the avascular growth of the EMT6/Ro and the SK-MES-1 cell line. In a rst step, a multiscale and individual-based model has been built up and validated with EMT6/Ro mouse mammary carcinoma multi-cellular cell spheroid data from literature. For this cell line it predicted the growth kinetics to be controlled by a combination of spatial restrains and nutrient limitation. ATP was found to be the critical resource which the cells try to keep constant over a wide range of oxygen and glucose medium concentrations by switching between aerobic and anaerobic metabolism. Only if both, oxygen and glucose are very limiting saturation was observed which the model could explain by cell-cell-adhesion-driven migration. In a second step, the model was adapted to the SK-MES-1 cell line. The growth kinetics was calibrated quantitatively in comparison with growth curves and qualitatively by image analysis of spheroid cryosections stained for apoptosis and proliferation. Beside ATP, lactate was identi ed to control the size of the necrotic core. For the transition to the in-vivo situation, we propose a model extension introducing a blood vessel network and angiogenesis. In order to parametrize the functional vessel properties and to validate angiogenesis rules, we study the parameter inference from contrast enhanced perfusion images. As a benchmark, we rst solve the direct problem of contrast agent perfusion along a network of either permeable or non-permeable vessels. Then by voxel-wisely solving the inverse problem and direct comparison between recovered and original parameter maps we study its predictive e ciency for di erent cases.
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Submitted on : Tuesday, January 22, 2013 - 7:15:56 PM
Last modification on : Friday, February 4, 2022 - 3:20:54 AM
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  • HAL Id : tel-00779981, version 1


Nick Jagiella. Parameterization of Lattice-Based Tumor Models from Data.. Cancer. Université Pierre et Marie Curie - Paris VI, 2012. English. ⟨NNT : ⟩. ⟨tel-00779981⟩



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