The immune system, central to human health, is one of the most complex human biological systems. The immune system is governed by nonlinear networks of tens of cell types, and the inter-cellular complexity is exacerbated further by the even more complex biological and biochemical networks within each cell (including signaling, metabolism, and gene regulation) that define single-cell dynamics and decision-making. Such multiscale complexity makes it incredibly challenging to understand the complete etiology and pathology of immunological diseases, or even characterize and diagnose the health status of a person’s immune system. In this presentation, I will focus on our efforts to develop a digital twin - a comprehensive computer model - of the immune system. I will discuss its uses to better understand the dynamics of the immune system across different scales of biological organization, including CD4+ T cell differentiation. To exemplify our methods in the context of drug development, I will also share our very recent results of new antiviral combinatorial treatments for influenza identified from a comprehensive model of host-pathogen interactions between influenza and airway epithelial cells.