In this talk, we refer to the achievement of an intended and predicted response in a biological system as controlling biology. Efforts towards controlling biology have evolved on different scales from controlling gene expression at the single-cell level to controlling large complex networks such as glucose regulation via an artificial pancreas. However, most approaches are not immune to the inherent properties of nature such as stochasticity and unmodeled dynamics. What allows nature to evolve and life to exist is what makes it challenging to control. New technology in synthetic biology and bioelectronics can give us unprecedent spatiotemporal control over nature. Through adaptive external “sense and respond” learning algorithms, we can gain improved control over cellular response. In this talk, will discuss work done towards developing NN-based predictors and feedback controllers in order to direct cellular response with no model a priori and no offline training. We also introduce efforts towards data-driven state-transition models of complex biological processes in order to identify and drive systems towards desired reachable states via our ML-based controllers.
Marcella M. Gomez is an assistant professor at UC Santa Cruz in the department of Applied Mathematics. She received her PhD from Caltech in 2015 and a B.S. from UC Berkeley in 2009; both degrees in Mechanical Engineering. Her research interests are in synthetic and systems biology. She is also a proud Chicana, first generation Mexican-American from Riverside, CA.