In Vivo Real-Time Control of Gene Expression: A Comparative Analysis of Feedback Control Strategies in Yeast.

Real-time automatic regulation of gene expression is a key technology for synthetic biology enabling, for example, synthetic circuit's components to operate in an optimal range. Computer-guided control of gene expression from a variety of inducible promoters has been only recently successfully demonstrated. Here we compared, in silico and in vivo, three different control algorithms: the Proportional-Integral (PI) and Model Predictive Control (MPC) controllers, which have already been used to control gene expression, and the Zero Average Dynamics (ZAD), a control technique used to regulate electrical power systems. We chose as an experimental testbed the most commonly used inducible promoter in yeast: the galactose-responsive GAL1 promoter. We set two control tasks: either force cells to express a desired constant fluorescence level of a reporter protein downstream of the GAL1 promoter (set-point) or a time-varying fluorescence (tracking). Using a microfluidics-based experimental platform, in which either glucose or galactose can be provided to the cells, we demonstrated that both the MPC and ZAD control strategies can successfully regulate gene expression from the GAL1 promoter in living cells for thousands of minutes. The MPC controller can track fast reference signals better than ZAD but with a higher actuation effort due to the large number of input switches it requires. Conversely, the PI controller's performance is comparable to that achieved by the MPC and the ZAD controllers only for the set-point regulation.

[1]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[2]  Weiping Li,et al.  Applied Nonlinear Control , 1991 .

[3]  Tor Arne Johansen,et al.  Nonlinear Predictive Control Using Local Models -Applied to a Batch Process , 1994 .

[4]  Jay H. Lee,et al.  Model predictive control: past, present and future , 1999 .

[5]  Robert Grino,et al.  QUASI-SLIDING CONTROL BASED ON PULSE WIDTH MODULATION, ZERO AVERAGED DYNAMICS AND THE L2 NORM , 2000 .

[6]  P. Swain,et al.  Intrinsic and extrinsic contributions to stochasticity in gene expression , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[7]  Goldberg,et al.  Genetic algorithms , 1993, Robust Control Systems with Genetic Algorithms.

[8]  P. Swain,et al.  Stochastic Gene Expression in a Single Cell , 2002, Science.

[9]  Francesc Guinjoan,et al.  A fixed-frequency quasi-sliding control algorithm: application to power inverters design by means of FPGA implementation , 2003 .

[10]  Richard M. Murray,et al.  Feedback Systems An Introduction for Scientists and Engineers , 2007 .

[11]  M. Bennett,et al.  Metabolic gene regulation in a dynamically changing environment , 2008, Nature.

[12]  L. Tsimring,et al.  A synchronized quorum of genetic clocks , 2009, Nature.

[13]  J Hasty,et al.  Microfluidics for synthetic biology: from design to execution. , 2011, Methods in enzymology.

[14]  Mario di Bernardo,et al.  Analysis, design and implementation of a novel scheme for in-vivo control of synthetic gene regulatory networks , 2011, Autom..

[15]  Jared E. Toettcher,et al.  Light-based feedback for controlling intracellular signaling dynamics , 2011, Nature Methods.

[16]  D. Pincus,et al.  In silico feedback for in vivo regulation of a gene expression circuit , 2011, Nature Biotechnology.

[17]  David Botstein,et al.  A Test of the Coordinated Expression Hypothesis for the Origin and Maintenance of the GAL Cluster in Yeast , 2011, PloS one.

[18]  F. Fages,et al.  Long-term model predictive control of gene expression at the population and single-cell levels , 2012, Proceedings of the National Academy of Sciences.

[19]  E. Feng,et al.  Modelling and optimal control for a fed-batch fermentation process , 2013 .

[20]  G Fiore,et al.  An experimental approach to identify dynamical models of transcriptional regulation in living cells. , 2013, Chaos.

[21]  Jeffrey J. Tabor,et al.  Characterizing bacterial gene circuit dynamics with optically programmed gene expression signals , 2014, Nature Methods.

[22]  Mario di Bernardo,et al.  In-Vivo Real-Time Control of Protein Expression from Endogenous and Synthetic Gene Networks , 2014, PLoS Comput. Biol..

[23]  Megan N. McClean,et al.  Real-time optogenetic control of intracellular protein concentration in microbial cell cultures. , 2014, Integrative biology : quantitative biosciences from nano to macro.

[24]  Fabiola Angulo,et al.  Performance of a Zero Average Dynamics‐controlled buck converter using different pulse‐width modulation schemes , 2015, Int. J. Circuit Theory Appl..