And generous funding to support our work from the NCI, NCI Systems Biology, lLustgarten Foundation, the NSF, the JKTG Foundation, and others. Thank you for following along! 19/19

Thanks to contributions from all of our collaborators at JHU, IU, OHSU and beyond--
Dr. Ines Godet, Dr. Laura Heiser, and the Jaffee lab just to name a few 18/19

This has been a true dream team science project led by
@mathcancer.bsky.social @fertiglab.bsky.social and Dr. Genevieve Stein-O'Brien 17/19

Another nice benefit of our approach is that we can combine the model grammar with graphical interfaces to easily write and test new models without writing code. This opens the world of simulation modeling to new communities. (Preprint by @rheiland soon!) 16/19

Altogether, this approach shows a powerful new strategy to predict emergent cell behaviors and test the impact of hypotheses of the impact of cell-cell interactions defined either from literature or genomics that are broadly applicable across biological systems. 15/19

In this work, we found that applying combination immunotherapy to our model improved clearance of virtual tumors. 14/19

Example 5 extends our model to simulate combination immunotherapy strategies in pancreatic cancer based on these genomics-derived rules of immune cell interactions in immunotherapy response to provide a platform for virtual clinical trials. 13/19

Genomics data can also be used to define novel cell-cell interactions of tumor-immune interactions during immunotherapy response and propose candidate therapeutic targets to overcome immunosuppressive signaling (e.g., www.cell.com/cancer-cell/... 12/19

This example demonstrates the power of the simulation to model emergent cell behaviors, as a population of protective M2-like macrophages emerges as the simulation progresses and protects the remaining tumor from destruction. 11/19

With the immune cells in place, example 4 uses extensive literature-derived rules of tumor-immune interactions to simulate T cell activation in response to tumor contact. 10/19

Example 3 extends this tumor model to incorporate the regulation of tumor growth in immune cells 9/19

This model allowed us to forecast PDAC microenvironment dynamics from RNA and imaging information. We saw isolated tumor cells transition predominantly to the mesenchymal phenotype and then formulate nests of epithelial tumor cells invade during carcinogenesis. 8/19

Example 2 models human pancreatic tumor progression to invasion by initializing mathematical agents from spatial transcriptomics coordinates of epithelial cells and fibroblasts the hypothesis grammar to encode our genomics-inferred rules of their cell-cell interactions. 7/19

Recently, integrating single-cell data from human pancreatic cancer tumors and organoid co-culture demonstrated a trade-off between tumor cell proliferation and fibroblast-associated EMT in epithelial cells. 6/19  doi.org/10.1101/2022...

Example 1 simulates unchecked cell growth in cancer, which exhausts oxygen in non-vascularized tumors. Modeling this resource consumption problem has provided a foundation for mathematical modeling of tumors, and this serves as the base example of tumor cell behavior. 5/19

To allow for human-readable encoding of equations, we define a new concept of hypothesis grammar and demonstrate its use in 5 real-world biological examples. 4/19

Here, we extend PhysiCell to model cellular systems that translates human-readable text into mathematical equations of cell behavior for broad accessibility of these complex mathematical models. 3/19

Agent-based modeling is particularly well-suited to studying communities of interacting cells, as it is intuitive to map a single cell to a single agent and is readily empowered through the PhysiCell software ecosystem. 2/19 journals.plos.org/ploscompbiol...