Advances in building patient-specific agent-based models in cancer

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• Paul Macklin (Indiana University Bloomington)
• Thursday 18 September 2025, 14:15-15:00
• Seminar Room 1, Newton Institute.

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OOEW07 - Mathematical Foundations of Oncological Digital Twins

Tumors are a mixture of malignant cancer cells, stromal cells, and immune cells that interact as dynamically evolving ecosystems. Over the last ten years, there has been substantial progress in building patient-tailored simulation models of cancer: a key component of digital twins. In the context of cancer immunology, single-cell effects and pairwise cell-cell interactions are paramount. Agent-based models (ABMs), which simulate individual cells as software agents that interact in simulated tissue environments, are well-suited to simulating these cancer-immune systems, but they generally require substantial hand-coding (in C++, Python, etc.). Beyond creating a substantial barrier to entry for domain experts and multidisciplinary teams, this has also hampered model reuse, extensibility, maintenance, and reproducibility. In this talk, we present a new conceptual framework—a cell behavior hypothesis grammar—that uses natural language statements (cell rules) to create mathematical and simulation models in real time without writing code. This enables systematic integration of biological knowledge and multi-omics data to generate in silico models with user-friendly online tools, enabling virtual “thought experiments” that interactively test and expand our understanding of multicellular systems and generate new testable hypotheses. We demonstrate its use in developing both de novo mechanistic models and those informed (and initialized) by multi-omics data, with focused examples on models of hypoxic breast cancer, tumor-immune interactions, and virtual combination immunotherapy trials in pancreatic ductal carcinoma (PDAC). We also show how rapid model creation (via the grammar) can be combined with high-performance computing (HPC) to accelerate discovery in cancer biology and development of reusable components for digital twins. We close with a discussion of how language-focused modeling will open new doors to combine human and machine intelligence in mathematical oncology.  These results—recently published in Cell—are the joint work of a broad coalition including Elana Fertig (University of Maryland); Laura Heiser, Young Hwan Chang, Lisa Coussens, and Joe Gray (Oregon Health and Sciences University), Genevieve Stein-O’Brien (Johns Hopkins), and others with my lab at Indiana University. 

This talk is part of the Isaac Newton Institute Seminar Series series.

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