Please use this identifier to cite or link to this item: https://doi.org/10.1073/pnas.1206810109
Title: Long-term model predictive control of gene expression at the population and single-cell levels
Authors: Uhlendorf, J.
Miermont, A.
Delaveau, T.
Charvin, G.
Fages, F.
Bottani, S.
Batta, G.
Hersen, P. 
Keywords: Computational biology
High osmolarity glycerol pathway
Model based control
Quantitative systems biology
Issue Date: 28-Aug-2012
Citation: Uhlendorf, J., Miermont, A., Delaveau, T., Charvin, G., Fages, F., Bottani, S., Batta, G., Hersen, P. (2012-08-28). Long-term model predictive control of gene expression at the population and single-cell levels. Proceedings of the National Academy of Sciences of the United States of America 109 (35) : 14271-14276. ScholarBank@NUS Repository. https://doi.org/10.1073/pnas.1206810109
Abstract: Gene expression plays a central role in the orchestration of cellular processes. The use of inducible promoters to change the expression level of a gene from its physiological level has significantly contributed to the understanding of the functioning of regulatory networks. However, from a quantitative point of view, their use is limited to short-term, population-scale studies to average out cell-to-cell variability and gene expression noise and limit the nonpredictable effects of internal feedback loops that may antagonize the inducer action. Here,we showthat, by implementing an external feedback loop, one can tightly control the expression of a gene over many cell generations with quantitative accuracy. To reach this goal, we developed a platform for real-time, closed-loop control of gene expression in yeast that integrates microscopy for monitoring gene expression at the cell level, microfluidics to manipulate the cells' environment, and original software for automated imaging, quantification, and model predictive control. By using an endogenous osmostress responsive promoter and playing with the osmolarity of the cells environment, we show that long-term control can, indeed, be achieved for both time-constant and time-varying target profiles at the population and even the single-cell levels. Importantly, we provide evidence that real-time control can dynamically limit the effects of gene expression stochasticity. We anticipate that our method will be useful to quantitatively probe the dynamic properties of cellular processes and drive complex, synthetically engineered networks.
Source Title: Proceedings of the National Academy of Sciences of the United States of America
URI: http://scholarbank.nus.edu.sg/handle/10635/128529
ISSN: 00278424
DOI: 10.1073/pnas.1206810109
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