Abstract 2484: A novel algorithm to expand mechanistic computational models of biochemical reaction networks to heterogeneous tumor cell communities

Abstract Background: Tumors are complex ecosystems of interacting cell types, and the effects of heterogeneous environments on cell signaling are challenging to study by experiment alone. Mechanistic computational modeling of biochemical reaction networks using ordinary differential equations (ODEs) is a powerful approach for analyzing cell signaling, yet most existing ODE-based models of cell signaling describe a population of homogeneous cells. We introduce Mechanistic Modeling of Cell Communities (M2C2), a novel algorithm to expand a biochemical reaction network model described in one cell type to a community of interacting cell types. Summary: M2C2 represents a cell community as multiple intracellular compartments, each representing a pool of cells with similar properties (“cell types”), interacting through a common extracellular compartment. M2C2 algorithmically expands a one-cell-type model to describe an interacting community of any number of distinct cell types. The expansion assumes all compartments share the same set of possible reactions and rate equations, though the rate constants can be adjusted on a per-cell basis, e.g. for cell-specific mutations or inhibitors. Compartments have independent reactant concentrations, reflecting cell-specific protein expression and interventions. In the context of mass-action kinetics, this leads to different network dynamics in each cell type. Algorithm: The M2C2 function takes two inputs: (1) an integer n representing the desired number of interacting cell types and (2) a single-cell-type model encoded in a vector-matrix form inspired by chemical master equation approaches for stochastic modeling.1 The matrices are arranged into blocks using location to group the molecular species (extracellular, cellular) and reactions (extracellular, interface, cellular). M2C2 expands the original matrix by replicating n copies of cellular and interface blocks while leaving the extracellular block intact. Because this expansion is algorithmic, it works for any integer n;;; without manual correction. M2C2 outputs the expanded model in vector-matrix form, which can be numerically solved using standard methods. Simulated molecular dynamics in each cell type can be analyzed with statistical and visualization methods. Conclusions: Mechanistic Modeling of Cell Communities (M2C2) is a novel approach for mesoscale modeling of cell signaling within multicellular systems such as tumor microenvironments. Cell type proportions and cell-type-specific expression patterns of M2C2-expanded models could be parameterized with multi-omic single-cell data for in silico simulation of personalized tumors and treatments. References: 1. Gillespie DT. Stochastic simulation of chemical kinetics. Annu Rev Phys Chem. 2007;58:35-55. doi: 10.1146/annurev.physchem.58.032806.104637. PMID: 17037977. Citation Format: Amy Gill, Feilim Mac Gabhann. A novel algorithm to expand mechanistic computational models of biochemical reaction networks to heterogeneous tumor cell communities [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 2484.