Speaker
Description
Metastasis to the liver remains a leading cause of mortality in colorectal cancer patients, due largely to the difficulty of treating established metastatic lesions. Spatial transcriptomic (ST) imaging provides highly detailed, spatially resolved data on the cellular composition and interactions within these lesions, offering new insights into how metastases are established and evolve in the liver. Upon arrival, tumour cells interact with hepatic cells and actively remodel the surrounding tissue to form a supportive metastatic niche \cite{li_understanding_2025}. These early processes, which can be resolved through spatial analysis of ST images, determine whether lesions can successfully form and expand. Here, we develop an agent-based multiscale model using a PhysiCell framework to investigate colorectal metastases in the liver \cite{ghaffarizadeh_physicell:_2018, ghaffarizadeh_biofvm:_2016}. To bridge experimental data and model development, we employ a spatial analysis pipeline using tools such as Muspan \cite{bull2024muspan} to quantify cellular neighbourhoods, interactions, and tissue architecture from the ST images. This approach enables the construction of a data-driven, spatially informed agent-based model that captures the mechanistic processes underlying metastatic colonisation. By validating the model against experimental observations, we establish a framework to predict disease progression and potentially improve future therapeutic strategies.
Bibliography
@article{li_understanding_2025,
title = {Understanding pre-metastatic niche formation: implications for colorectal cancer liver metastasis},
volume = {23},
issn = {1479-5876},
shorttitle = {Understanding pre-metastatic niche formation},
url = {https://translational-medicine.biomedcentral.com/articles/10.1186/s12967-025-06328-2},
doi = {10.1186/s12967-025-06328-2},
language = {en},
number = {1},
urldate = {2026-03-19},
journal = {Journal of Translational Medicine},
author = {Li, Yaqin and Wang, Hong and Mao, Dengxuan and Che, Xiaoyu and Chen, Yan and Liu, Yuping},
month = mar,
year = {2025},
pages = {340},
}
@article{ghaffarizadeh_physicell:_2018,
title = {{PhysiCell}: {An} open source physics-based cell simulator for 3-{D} multicellular systems},
volume = {14},
issn = {1553-7358},
shorttitle = {{PhysiCell}},
url = {https://dx.plos.org/10.1371/journal.pcbi.1005991},
doi = {10.1371/journal.pcbi.1005991},
language = {en},
number = {2},
urldate = {2026-03-19},
journal = {PLOS Computational Biology},
author = {Ghaffarizadeh, Ahmadreza and Heiland, Randy and Friedman, Samuel H. and Mumenthaler, Shannon M. and Macklin, Paul},
editor = {Poisot, Timothée},
month = feb,
year = {2018},
pages = {e1005991},
}
@article{ghaffarizadeh_biofvm:_2016,
title = {{BioFVM}: an efficient, parallelized diffusive transport solver for 3-{D} biological simulations},
volume = {32},
copyright = {http://creativecommons.org/licenses/by-nc/4.0/},
issn = {1367-4811, 1367-4803},
shorttitle = {{BioFVM}},
url = {https://academic.oup.com/bioinformatics/article/32/8/1256/1744374},
doi = {10.1093/bioinformatics/btv730},
abstract = {Abstract
Motivation: Computational models of multicellular systems require solving systems of PDEs for release, uptake, decay and diffusion of multiple substrates in 3D, particularly when incorporating the impact of drugs, growth substrates and signaling factors on cell receptors and subcellular systems biology.
Results: We introduce BioFVM, a diffusive transport solver tailored to biological problems. BioFVM can simulate release and uptake of many substrates by cell and bulk sources, diffusion and decay in large 3D domains. It has been parallelized with OpenMP, allowing efficient simulations on desktop workstations or single supercomputer nodes. The code is stable even for large time steps, with linear computational cost scalings. Solutions are first-order accurate in time and second-order accurate in space. The code can be run by itself or as part of a larger simulator.
Availability and implementation: BioFVM is written in C ++ with parallelization in OpenMP. It is maintained and available for download at http://BioFVM.MathCancer.org and http://BioFVM.sf.net under the Apache License (v2.0).
Contact: paul.macklin@usc.edu.
Supplementary information: Supplementary data are available at Bioinformatics online.},
language = {en},
number = {8},
urldate = {2026-03-19},
journal = {Bioinformatics},
author = {Ghaffarizadeh, Ahmadreza and Friedman, Samuel H. and Macklin, Paul},
month = apr,
year = {2016},
pages = {1256--1258},
}
@article{bull2024muspan,
title={MuSpAn: a toolbox for multiscale spatial analysis},
author={Bull, Joshua A and Moore, Joshua W and Mulholland, Eoghan J and Leedham, Simon J and Byrne, Helen M},
journal={bioRxiv},
pages={2024--12},
year={2024},
publisher={Cold Spring Harbor Laboratory}
}
