A multiscale mechanistic model of human dendritic cells for in-silico investigation of immune responses and novel therapeutics discovery

Abstract

Abstract Given the critical role of dendritic cells (DCs) in shaping immunity, they present an attractive avenue as a therapeutic target to program the immune system and reverse immune disease disorders. DC-mediated immune response grows into a complex continuum of molecular and cellular networks where each scale decides to be later translated into a seamless phenotype. Computational models open novel frontiers in research by integrating large-scale interaction to interrogate the influence of complex biological behavior across scales. The ability of modeling to digest large amounts of biological networks will likely pave the way to understanding any complex system in more approachable ways. We developed a logical and predictive model of DC function that integrates the heterogeneity of DCs population, APC function, and cell-cell interaction, spanning molecular to population levels. We provided three sample use cases to apply the model in the context of studying cell dynamics and disease environments. First, we characterized the DC molecular response to Sars-Cov-2 and influenza by comparing single to co-infection conditions. The second example presents simulations to predict the crosstalk between DCs and T cells in a cancer microenvironment. Finally, for the third example, we used the Kyoto Encyclopedia of Genes and Genomes enrichment analysis against the model’s components to identify 45 diseases and 24 molecular pathways that the DC model can address. This study presents a resource to decode the complex dynamics underlying DC-derived APC communication and provides a platform for researchers to perform in-silico experiments on human DC for vaccine design, drug discovery, and immunotherapies. The work was supported by an NIH grant R35GM119770 to Tomáš Helikar