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Virtual Joint Seminar: Open Source Multicellular Systems Modeling for Cancer (and COVID-19)
May 4 at 2:00 PM - 3:30 PM
Virtual Joint Seminar of Penn PSOC, Bioengineering, PICS
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Cancer and other critical human health problems are inherently multiscale: molecular-scale processes such as receptor trafficking and metabolism drive cell-scale processes such as cycling and motility. Biophysical processes like diffusion and tissue mechanics both constrain and drive single-cell behavior. Moreover, cells communicate and coordinate through chemical and mechanical processes. All these processes are dynamically linked to drive emergent multicellular systems behaviors in healthy and diseased tissues.
Computational models can act as “virtual laboratories” for multicellular systems biology. The ideal such laboratory should include cell and tissue biomechanics, biotransport of multiple chemical substrates including signaling factors, and many interacting cells. This talk presents PhysiCell (http://dx.doi.org/10.1371/journal.pcbi.1005991), an open source agent-based platform for 3-D multicellular systems biology. With PhysiCell, desktop workstations can routinely simulate systems of ten or more cell-secreted chemical signals and tissue substrates, along with 10^5 to 10^6 individual cells that grow, divide, die, secrete chemical signals, move, exchange mechanical forces, and remodel their tissue microenvironment. High-performance computing (HPC) resources can run tens of thousands of copies of these models to perform vast 3-D computational experiments, and artificial intelligence (AI) techniques can help us to accelerate these investigations and analyze the simulation results.
We will explore PhysiCell models of two cancer problems: (1) We will investigate how tumor-parenchyma biomechanical cross-talk can lead to tumor dormancy in liver micrometastases, and how changes in tissue mechanics (e.g., after an illness, injury, or aging) can lead to tumor “reawakening”. (2) We will explore the critical role of stochastic tumor-immune interactions in cancer immunosurveillance. We will show joint work with Argonne National Lab to explore large hypothesis and design spaces on supercomputers, as well as very recent advances in using machine learning to accelerate these explorations while also improving model interpretation.
We will also present work by a growing coalition of mathematicians, virologists, immunologists, pharmacologists, and others to rapidly prototype and iteratively improve a comprehensive multiscale SARS-CoV-2 tissue simulator. The first prototype model was built and shared internationally as open source code and interactive, cloud-hosted models in under 12 hours, and the work was disseminated as a community-written preprint within a week. The second prototype was updated to include ACE2 receptor-driven virus endocytosis and a more modular design; the third prototype is well underway. This coalition-based approach is developing submodel components in parallel and coordination, allowing us to rapidly advance towards a feature complete framework to drive many independent investigations that help us attack COVID-19. This project shows the potential for similar community-driven advances in cancer. Moreover, the novel mix of cancer biologists, immunologists, microbiologists, and others is fueling creative advances and new technical capabilities in multiscale tissue modeling. We anticipate that this progress will drive advances in cancer immunology, inflammation, and virus-driven carcinogenesis for years to come.
Paul Macklin, Ph.D.
Associate Professor, Indiana University
Paul Macklin is a mathematician, Associate Professor, and Director of Undergraduate studies in the recently-established Department of Intelligent Systems Engineering at Indiana University. He works with biologists, modelers, and clinicians to develop and validate sophisticated 3D computer models of cancer, SARS-CoV-2, and other multicellular systems, using the open source PhysiCell platform developed by his lab. He also works with the National Cancer Institute and the Department of Energy to co-lead a national initiative to create digital twins for the future of personalized predictive cancer medicine.