Negative autoregulation matches production and demand in synthetic transcriptional networks

We propose a negative feedback architecture that regulates activity of artificial genes, or “genelets”, to meet their output downstream demand, achieving robustness with respect to uncertain open-loop output production rates. In particular, we consider the case where the outputs of two genelets interact to form a single assembled product. We show with analysis and experiments that negative autoregulation matches the production and demand of the outputs: the magnitude of the regulatory signal is proportional to the “error” between the circuit output concentration and its actual demand. This two-device system is experimentally implemented using in vitro transcriptional networks, where reactions are systematically designed by optimizing nucleic acid sequences with publicly available software packages. We build a predictive ordinary differential equation (ODE) model that captures the dynamics of the system, and can be used to numerically assess the scalability of this architecture to larger sets of interconnected genes. Finally, with numerical simulations we contrast our negative autoregulation scheme with a cross-activation architecture, which is less scalable and results in slower response times.

[1]  G. Hong,et al.  Nucleic Acids Research , 2015, Nucleic Acids Research.

[2]  Richard M. Murray,et al.  Feedback architectures to regulate flux of components in artificial gene networks , 2013, 2013 American Control Conference.

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

[4]  Jongmin Kim,et al.  In vitro synthetic transcriptional networks , 2007 .

[5]  Uri Alon,et al.  An Introduction to Systems Biology , 2006 .

[6]  Elisa Franco,et al.  Analysis, design, and in vitro implementation of robust biochemical networks , 2012 .

[7]  Michael Zuker,et al.  Optimal computer folding of large RNA sequences using thermodynamics and auxiliary information , 1981, Nucleic Acids Res..

[8]  Richard M. Murray,et al.  Analysis and design of a synthetic transcriptional network for exact adaptation , 2011, 2011 IEEE Biomedical Circuits and Systems Conference (BioCAS).

[9]  Yi Li,et al.  Synthetic mammalian transgene negative autoregulation , 2013 .

[10]  A. Pühler,et al.  Molecular systems biology , 2007 .

[11]  R. Murray,et al.  Timing molecular motion and production with a synthetic transcriptional clock , 2011, Proceedings of the National Academy of Sciences.

[12]  David K. Karig,et al.  Expression optimization and synthetic gene networks in cell-free systems , 2011, Nucleic acids research.

[13]  Eric Klavins,et al.  Characterization of a biomolecular fuel delivery device under load , 2012, 2012 IEEE 51st IEEE Conference on Decision and Control (CDC).

[14]  E. Winfree,et al.  Construction of an in vitro bistable circuit from synthetic transcriptional switches , 2006, Molecular systems biology.

[15]  Richard M. Murray,et al.  Design and performance of in vitro transcription rate regulatory circuits , 2008, 2008 47th IEEE Conference on Decision and Control.

[16]  E. Winfree,et al.  Synthetic in vitro transcriptional oscillators , 2011, Molecular systems biology.

[17]  R.M. Murray,et al.  Design, modeling and synthesis of an in vitro transcription rate regulatory circuit , 2008, 2008 American Control Conference.