SEMINAR

Systems biology seeks to build quantitative predictive models of biological system behavior for the predictable manipulation of a given system. Biological systems operate across multiple spatiotemporal scales: from molecules to the organismal level, and from milliseconds to days, and even to decades. A good example of such multiscale behavior is the immune system. A myriad of molecular and cellular players comprises the immune system. They operate throughout the body and interact with each other across space and time. Therefore, predictably affecting the immune system for the desired outcomes (i.e., therapies) requires a better understanding of how the system operates as a whole, which is far beyond merely cataloging constituent molecular and cellular components and their interactions among immediate regulatory neighbors. For this endeavor, I seek to explore immune system behavior in its entirety via mathematical and computational modeling by establishing “scalable” modeling of the immune system to describe the multiscale and complex nature of the immune system in silico, eventually allowing predictive dissection and modulation of the complex immune behavior for treating immune-related diseases. In this seminar, I will highlight some challenges in such scalable immune system modeling and my previous works to overcome those (at least partially) and showcase how such efforts helped elucidate the counterintuitive mechanism of maintaining immune homeostasis. I will also lay out research plans incorporating data-driven (e.g., omics) approaches into multiscale computational modeling to realize in silico immune systems with an example of COVID-19 disease progression dynamics.