Towards Integrative Mechanistic Models of Mammalian Cell Responses to Anti-Cancer Drug Combinations

A key missing capability in current cancer research is the ability to predict how a particular single cancer cell will respond to 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 anti-cancer drugs will improve drug combination response predictions. However, building such single-cell models of complex, large-scale, and incompletely understood systems remains an extremely challenging task. To address this issue, we defined a pipeline to convert structured lists of species, parameters, and reaction types into an SBML (Systems Biology Markup Language) model file and created a model based on one of the largest pan-cancer signaling models in the literature. The previous model (774 species, 141 genes, 8 ligands, 2400 reactions) developed by our lab already incorporated major pathways of receptor tyrosine kinase signaling, proliferation, cell cycle, apoptosis, DNA damage, transcription, and translation. The new model format is available online and generated using open-source tools to accommodate community use and contribution. One key aspect in our effort is our model’s compatibility with high performance computing. We can simulate thousands to millions of single cell trajectories, enabling us to study virtual cell population responses to drugs and drug combinations. Another aspect of our model is its ability to accommodate addition of new reactions or pathways. As a proof of concept, we created an even larger model with the addition of Interferon-γ (IFNγ) signaling pathway, resulting in a model with 950 species, 150 genes, 9 ligands, 2500 reactions. All in all, our new model format enables easy modification of large mechanistic models and simulation of thousands of single cell responses to drugs and drug combinations.