Real-time feedback control of gene expression

Gene expression is fundamental for the functioning of cellular processes and is tightly regulated. Inducible promoters allow one to perturb gene expression by changing the expression level of a protein from its physiological level. This is a common tool to decipher the functioning of biological processes: the expression level of a gene is changed and one observes how the perturbed cell behaves differently from an unperturbed cell. A shortcoming of inducible promoters is the difficulty to apply precise and time-varying perturbations. This is due to two reasons: (i) cell-to-cell variability and noise in the gene expression process which limit the precision of the applied perturbations. (ii) the difficulty to quantitatively predict the behavior of cellular systems on a long time-horizon, which would be required to apply time-varying perturbations. But precise time-varying perturbations are particularly informative about the dynamics of a biological system. Here I present a feedback control platform, that can control the expression of a gene in yeast cells with quantitative accuracy over long time periods. The platform integrates fluorescence microscopy to monitor gene expression, microfluidics to act on the cellular environment and software implementing real-time image analysis and feedback control. This closed loop control setup is able to drive the expression of a yeast gene in a population of cells or in a single cell for both time-constant and time-varying target profiles. I use the high osmolarity glycerol (HOG) pathway, a stress response pathway of Saccharomyces cerevisiae, to activate gene expression and I show that the platform can be modified to use other gene induction systems. In addition to the gene expression control platform, I present a feedback control system able to control the activity of the HOG pathway. Understanding cellular dynamics in a quantitative way necessitates the ability of applying precise perturbations. The gene expression control platform that I present here is a major step towards this goal, since it allows to precisely perturb the expression level of a protein.

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