Enoch Yeung

Bio

Enoch Yeung received a B.S. in Mathematics from Brigham Young University, Magna Cum Laude with University Honors, and a Ph.D in Control and Dynamical Systems from the California Institute of Technology. His research interests are centered in system identification and machine learning, design and analysis of distributed control systems, and model reduction for stochastic and nonlinear systems, with applications to biological and cyber-physical systems.. His most recent work has focused on reverse-engineering genetic context effects in synthetic biocircuits, developing algorithms for network verification of synthetic biological networks, developing synthetic biosensors in living prokaryotes for performing temporal logic, and paper-based synthetic biosensing solutions for mobile devices. His latest work was showcased at the 2016 DARPA Wait, What? Technology Forum.

He is the recipient of an National Defense Science and Engineering Graduate Fellowship, National Science Foundation Graduate Fellowship, Kanel Foundation Fellowhsip, an ACC Best Presentation Session Award, and a Charles Lee Powell Foundation Fellowship. His research also has been supported by several research programs such as the DARPA Living Foundries program, the NSF Molecular Programming Project, and a AFOSR Biological Research Initiative.

Abstract

Over the past two decades, exponential advances in biotechnology and bioengineering have enabled us to explore different computing paradigms for biological systems. How do biological systems make decisions, grapple environmental complexity and uncertainty, and ultimately survive across non-stationary environments? In this talk, I will discuss several research thrusts that attempt to unravel some of the principles of nonlinear and stochastic computing in natural and engineered biological systems. First, I will introduce the concept of ‘biocircuit engineering’, or design of gene regulatory networks for specific computing tasks. I will then discuss how recent research in biocircuit verification methods have produced unexpected insight into a neglected form of gene control in synthetic gene networks, enforced by biophysical constraints. We demonstrate how these additional layer of computation can be used to greatly improve performance of a classical biocircuit, the toggle switch memory module. Our results reinforce the perspective that biological cells respond dynamically to their environmental context, to perform both local and global gene regulation. Moreover, our findings underscore a lack of modularity in biological computing that breaks away from traditional Von-Neumann architectures. Rather, we argue that cellular biological systems perform efficient computation using stochastic (and distributed) layered computational architectures (with feedback). These observations motivate ongoing efforts to develop a framework for engineering distributed layered computing architectures capable of processing and responding to data streams generated by stochastic and nonlinear systems.