Discovery and Characterization of Novel Feedback Control Mechanisms in Synthetic Gene Networks: From Principled Models to Deep Learning

February 08, 2019, Webb 1100

Enoch Yeung

UCSB, Mechanical Engineering


Biological control is ever present in our world, from the complex nanoscale interactions of microbial consortia to macro-scale social dynamics of ecosystems. Populations of millions of cells within a palm-sized test-tube implement stochastic and massively parallel control strategies, reprogramming their own DNA to manifest diverse controlarchitectures over time. These control systems achieve power efficiency and robustness, at the nano to micro scale. To imbue such properties into novel control systems is the challenge of our time. In this talk I will overview recent experimental and computational advances that accelerate design of novel control systems in the areas of synthetic biology and biomimetic computing. I will highlight two advances in particular: 1) the discovery of a novel biophysical mechanism for implementing feedback control in synthetic gene networks and 2) the integration of deep learning and data-driven methods to generate a computational framework for predicting the operational envelopes of synthetic biocircuits.   In the former, I will discuss how overlooked design properties of synthetic gene networks can be used to enforce local and fast feedback control, resulting in improved persistence (over 72 cell divisions) of memory in the genetic toggle switch.  In the latter, I address the challenge of predicting biological system behavior over a range of conditions, given finite and noisy experimental samples. Specifically, we examine the problem of discovering models for moment dynamics of a biological network, as a function of small molecule control signals and temperature-based perturbations. We introduce deep dynamic mode decomposition and show that the input-Koopman models learned from flow cytometry data predict biocircuit functionality with over 90% accuracy over 1,000 experimental conditions.

Speaker's Bio

Dr. Enoch Yeung has a B.S. in Mathematics from Brigham Young University, magnua cum laude with university honors and a Ph.D. in Control and Dynamical Systems from the California Institute of Technology. He has led many interdisciplinary research projects at the interface of synthetic biology and learning theory including the DARPA Synergistic Discovery & Design Program (currently serving as a performer and PI), DARPA Friend or Foe program (serving as co-PI), DARPA Living Foundries program, the 2018 High Performance Data Analytics Program, the NSF Molecular Programming Project, and the AFOSR Biological Research Initiative. He is an assistant professor in the Department of Mechanical Engineering at the University of California Santa Barbara. Previously, he served as senior research scientist in the Data Science and Analytics Group at Pacific Northwest National Laboratory and lead several internal research efforts in deep learning, network inference, and control of complex systems. His research interests center on learning algorithms for dynamical systems, control theory, synthetic and systems biology. He has served on several advisory panels for DARPA, NIST, the DoD SBME initiative, and the National Defense University. He is the recipient of Kanel Foundation Fellowship, the National Science Foundation Graduate Fellowship, a National Defense Science and Engineering Fellowship, the PNNL Project Team of the Year Award, and the PNNL Outstanding Performance Award.