Agent-based Modeling of Multi-compartment Tumor Organoid Utilizing the PhysiCell Software Framework

Elmar Bucher

Indiana University

"Agent-based Modeling of Multi-compartment Tumor Organoid Utilizing the PhysiCell Software Framework"

Tumor cell line organoid cultures are widely used in wet lab cancer research. Different from monolayer cell cultures, organoids preserve many phenotypic features demonstrated by cancer cells in vivo. In contrast to tissue microarrays, organoids offer a simplified, controllable environment. Recently, two-compartment matrigel/collagen1 organoids were developed [Lee2022], enabling scientists to mimic DCIS (ductal carcinoma in situ), IDS (invasive ductal carcinoma), and PDAC (pancreatic ductal adenocarcinoma) in extracellular matrix environments, as well as healthy mammary epithelial and fallopian tube epithelial extracellular matrix systems. Utilizing the C++ based PhysiCell software framework [Ghaffarizade2018], we implemented an agent-based model of these two-compartment organoids. Our model was calibrated on the available data from a study from Crawford et al., who used these two-compartment organoids to explore the effect of collagen1 density conditions on cancer cell proliferation and invasion ability. Based on the results, the authors suggested that the cancer cell’s proliferation and invasiveness are being linked to cell-extracellular matrix friction [Crawford2022]. In our research work, we determine whether we can recapitulate these wet lab experiment findings. Having a mathematical model which can capture the emergent phenomena, will extend our understanding of the two-compartment organoid wet lab model. Furthermore, the mathematical model makes it possible to quickly process a variety of experimental parameter settings in silico. The results will help to determine and plan the most interesting experimental parameters to explore in the wet lab. [Lee2022] https://doi.org/10.1016/j.mattod.2022.07.006 [Crawford2022] https://doi.org/10.1101/2022.11.15.516548 [Ghaffarizade2018] https://doi.org/10.1371/journal.pcbi.1005991
Additional authors: Ashleigh Crawford, Johns Hopkins University Isha Bhorkar, Johns Hopkins University David Schell, Johns Hopkins University Denis Wirtz, Johns Hopkins University Paul Mackin, Indiana University

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