Mustafa Khammash is the Professor of Control Theory and Systems Biology at the Department of Biosystems Science and Engineering (D-BSSE) at the Swiss Federal Institute of Technology in Zurich (ETHZ). From 2006 till 2011 he served as the Director of the Center for Control, Dynamical systems, and Computations (CCDC) at the University of California at Santa Barbara (UCSB) where he held a Professor appointment in the Mechanical Engineering department since 2001. Before joining UCSB he was on the faculty of the Electrical Engineering department at Iowa State University, a position he held since completing his Ph.D. at Rice University in 1990. Dr. Khammash currently works in the areas of control theory, systems biology, and synthetic biology. His research strives to understand the role of dynamics, feedback, and randomness in biology, and to develop the tools needed to aid in this understanding. Khammash is a Fellow of the IEEE, IFAC, and the Japan Society for the Promotion of Science. He is the recipient of the National Science Foundation Young Investigator Award, the Iowa State University Foundation Early Achievement in Research and Scholarship Award, the ISU College of Engineering Young Faculty Research Award, and the Ralph Budd Best Engineering PhD Thesis Award.
A hallmark of living cells is their inherent stochasticity. Stochastic molecular noise in individual cells manifests as cell-to-cell variability within a population of genetically identical cells. While experimental tools have enabled the measurement and quantification of variability of populations consisting of millions of cells, new modeling and analysis tools have lead to a substantial improvement in our understanding of the stochastic nature of living cell populations and its biological role. More recently, these developments came together to pave the way for the real-time control of living cells.
In this presentation, we describe novel analytical and experimental work that demonstrates how a computer can be interfaced with living cells and used to control their behavior. We discuss how computer controlled light pulses, in combination with a genetically encoded light-responsive module and a flow cytometer can be configured to achieve precise and robust set-point regulation of gene expression in the noisy environment of the cell. We then address the theoretical, computational, and practical issues concerning the feedback control of single cells as well as cell populations. Aside from its potential applications in biotechnology and therapeutics, this approach opens up exciting opportunities for the development of new control theoretic methods aimed at confronting the unique challenges of manipulating the dynamic behavior of living cells.
