Cyborg Yeast: Feedback Control of Cell Populations

Abstract

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.