Automatic Control of Gene Expression in Mammalian Cells.

Automatic control of gene expression in living cells is paramount importance to characterize both endogenous gene regulatory networks and synthetic circuits. In addition, such a technology can be used to maintain the expression of synthetic circuit components in an optimal range in order to ensure reliable performance. Here we present a microfluidics-based method to automatically control gene expression from the tetracycline-inducible promoter in mammalian cells in real time. Our approach is based on the negative-feedback control engineering paradigm. We validated our method in a monoclonal population of cells constitutively expressing a fluorescent reporter protein (d2EYFP) downstream of a minimal CMV promoter with seven tet-responsive operator motifs (CMV-TET). These cells also constitutively express the tetracycline transactivator protein (tTA). In cells grown in standard growth medium, tTA is able to bind the CMV-TET promoter, causing d2EYFP to be maximally expressed. Upon addition of tetracycline to the culture medium, tTA detaches from the CMV-TET promoter, thus preventing d2EYFP expression. We tested two different model-independent control algorithms (relay and proportional-integral (PI)) to force a monoclonal population of cells to express an intermediate level of d2EYFP equal to 50% of its maximum expression level for up to 3500 min. The control input is either tetracycline-rich or standard growth medium. We demonstrated that both the relay and PI controllers can regulate gene expression at the desired level, despite oscillations (dampened in the case of the PI controller) around the chosen set point.

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

[2]  Diego di Bernardo,et al.  Construction and Modelling of an Inducible Positive Feedback Loop Stably Integrated in a Mammalian Cell-Line , 2011, PLoS Comput. Biol..

[3]  I︠a︡. Z. T︠S︡ypkin Relay Control Systems , 1985 .

[4]  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.

[5]  X. Darzacq,et al.  In vivo dynamics of RNA polymerase II transcription , 2007, Nature Structural &Molecular Biology.

[6]  L. Tsimring,et al.  Vacuum-assisted cell loading enables shear-free mammalian microfluidic culture. , 2012, Lab on a chip.

[7]  D. di Bernardo,et al.  miRNAs confer phenotypic robustness to gene networks by suppressing biological noise , 2013, Nature Communications.

[8]  J. Stelling,et al.  A tunable synthetic mammalian oscillator , 2009, Nature.

[9]  Kazuyuki Aihara,et al.  Hybrid optimal scheduling for intermittent androgen suppression of prostate cancer. , 2010, Chaos.

[10]  M. Gossen,et al.  Inducible gene expression systems for higher eukaryotic cells. , 1994, Current opinion in biotechnology.

[11]  R. W. Jones,et al.  Respiratory responses to CO2 inhalation; a theoretical study of a nonlinear biological regulator. , 1954, Journal of applied physiology.

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

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

[14]  M. Hoagland,et al.  Feedback Systems An Introduction for Scientists and Engineers SECOND EDITION , 2015 .

[15]  M. Gossen,et al.  Tight control of gene expression in mammalian cells by tetracycline-responsive promoters. , 1992, Proceedings of the National Academy of Sciences of the United States of America.