We seek to construct predictive models describing the dynamical behavior of the immune system via systems biological approaches. Using these models, we wish to better understand complex and nonlinear immune behavior in its entirety. Ultimately, we design therapies to predictably modulate such behavior to the direction we desire to help cure infectious, malignant, and autoimmune diseases occurring due to dysregulations of the immune system. To realize this, we need to incorporate sufficient biological realities of the immune system into the models in two aspects: 1) the multiscale nature spanning across genes, cells, tissues, organs, and organisms/populations and 2) the high-throughput nature consisting of numerous molecular and cellular players with intricate interactions among each other. Therefore, we will develop computational frameworks to encompass data from various sources – multi-omics, clinics, or images using methods from machine learning, Bayesian statistics, and data science within traditional mathematical modeling to be deployable to individualized therapies of immune diseases in the era of ‘precision’ medicine.