Towards Integrative Mechanistic Models of Mammalian Cell Responses to Extracellular Perturbations: Growth Factors, Hormones, and Cytokines

A key missing capability in current cancer research is the ability to predict how a particular single cancer cell will respond to microenvironmental cues or a drug cocktail. Yet, it is not even possible to perform this task well for normal healthy cells. This work builds on the hypothesis that first principles, mechanistic models of how cells respond to different perturbagens will ultimately improve drug combination response predictions. However, building such single-cell models of complex, large-scale, and incompletely understood systems remains an extremely challenging task. Here, we defined an open-source pipeline for scalable, single-cell mechanistic modeling that converts simple, annotated input files (structured lists of species, parameters, reaction types) into an SBML model file. Using this pipeline, (i) we re-created one of the largest pan-cancer signaling models in the literature (774 species, 141 genes, 2400 reactions), (ii) enlarged the model to include Interferon-γ (IFNγ) signaling pathway (950 species, 150 genes, 2500 reactions), and (iii) re-parametrized the model to test and prioritize candidate mechanisms. Specifically, we used the enlarged model to test alternative mechanistic hypotheses for the experimental observations that IFNγ inhibits epidermal growth factor (EGF)-induced cell proliferation. Our single-cell-simulation-based analysis suggested, and experiments support that these observations are better explained by IFNγ-induced SOCS1 expression sequestering activated EGF receptors, thereby downregulating AKT activity, as opposed to direct IFNγ-induced upregulation of p21 expression. Finally, our new modeling format is available online and compatible with high performance (Kubernetes) computing platforms, enabling us to study virtual cell population responses. Overall, our new model enables easy modification of large mechanistic models and simulation of thousands of single-cell responses to multiple ligands and drug combinations.