B.S.E. 1985 Princeton University
C.P.G.S. 1986 Cambridge University
Ph.D. 1991 Caltech
Associate Director Army Institute for Collaborative Biotechnologies
Duncan and Suzanne Mellichamp Chair in Process Control Department of Chemical Engineering at the University of California at Santa Barbara, appointments in the Electrical Engineering Department, and the Biomolecular Science and Engineering Program
Faculty Purdue University, University of Delaware
Visiting Positions DuPont, Weyerhaeuser, and Stuttgart University
The NSF National Young Investigator
ONR Young Investigator
Humboldt Research Fellowship
AIChE Computing in Chemical Engineering Award
The Purdue Potter Award
The ASEE Ray Fahien Award
Editor-in-Chief The IEEE Transactions on Control Systems Technology
Associate Editor Journal of Process Control, the SIAM Journal on Applied Dynamical Systems, and Royal Society’s Interface
The Computing in Chemical Engineering Award The American Institute of Chemical Engineers, 2005

Natural control systems are paragons of optimality. Over millennia, these architectures have been honed to achieve robust regulation of a myriad of processes at the levels of genes, proteins, cells, and entire systems. One of the more interesting aspects of these circuits, and one of the challenges for control research, is unraveling the multi-scale, hierarchical control that achieves robust performance in the face of stochastic perturbations. These perturbations arise from both intrinsic sources (e.g., inherent variability in the transcription machinery), and extrinsic sources (e.g., environmental fluctuations). Robustness in key performance variables to particular perturbations is shown to be achieved at the expense of strong sensitivity to other perturbations.

In this talk, several biological examples will be used to highlight robustly regulated behavior, including: circadian timekeeping in neuronal cells; the unfolded protein response and its connection to Alzheimer’s and diabetes; and programmed cell death (apoptosis). A key insight from these examples is that control at the cellular network level guides many properties in a manner that is distinct from control at the intracellular level.

A variety of tools from systems theory are employed in this research, including the structured singular value, sensitivity measures (with extensions to limit cycle behavior and stochastic systems), and discrete stochastic simulations. Those tools complement the high throughput biological assays that are used to interrogate the natural control circuits.