A systematic design method for robust synthetic biology to satisfy design specifications

BackgroundSynthetic biology is foreseen to have important applications in biotechnology and medicine, and is expected to contribute significantly to a better understanding of the functioning of complex biological systems. However, the development of synthetic gene networks is still difficult and most newly created gene networks are non-functioning due to intrinsic parameter uncertainties, external disturbances and functional variations of intra- and extra-cellular environments. The design method for a robust synthetic gene network that works properly in a host cell under these intrinsic parameter uncertainties and external disturbances is the most important topic in synthetic biology.ResultsIn this study, we propose a stochastic model that includes parameter fluctuations and external disturbances to mimic the dynamic behaviors of a synthetic gene network in the host cell. Then, based on this stochastic model, four design specifications are introduced to guarantee that a synthetic gene network can achieve its desired steady state behavior in spite of parameter fluctuations, external disturbances and functional variations in the host cell. We propose a systematic method to select a set of appropriate design parameters for a synthetic gene network that will satisfy these design specifications so that the intrinsic parameter fluctuations can be tolerated, the external disturbances can be efficiently filtered, and most importantly, the desired steady states can be achieved. Thus the synthetic gene network can work properly in a host cell under intrinsic parameter uncertainties, external disturbances and functional variations. Finally, a design procedure for the robust synthetic gene network is developed and a design example is given in silico to confirm the performance of the proposed method.ConclusionBased on four design specifications, a systematic design procedure is developed for designers to engineer a robust synthetic biology network that can achieve its desired steady state behavior under parameter fluctuations, external disturbances and functional variations in the host cell. Therefore, the proposed systematic design method has good potential for the robust synthetic gene network design.

[1]  Nancy A. Jenkins,et al.  Recombineering: a powerful new tool for mouse functional genomics , 2001, Nature Reviews Genetics.

[2]  J. Collins,et al.  Programmable cells: interfacing natural and engineered gene networks. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[3]  Calin Belta,et al.  Robustness analysis and tuning of synthetic gene networks , 2007, Bioinform..

[4]  Pablo A. Parrilo,et al.  Efficient classification of complete parameter regions based on semidefinite programming , 2007, BMC Bioinformatics.

[5]  M. Oh,et al.  Directed Evolution of Metabolically Engineered Escherichiacoli for Carotenoid Production , 2000, Biotechnology progress.

[6]  Guanrong Chen,et al.  Linear Stochastic Control Systems , 1995 .

[7]  A. Arkin,et al.  Stochastic kinetic analysis of developmental pathway bifurcation in phage lambda-infected Escherichia coli cells. , 1998, Genetics.

[8]  J. Collins,et al.  Construction of a genetic toggle switch in Escherichia coli , 2000, Nature.

[9]  A. Arkin,et al.  It's a noisy business! Genetic regulation at the nanomolar scale. , 1999, Trends in genetics : TIG.

[10]  M. Elowitz,et al.  A synthetic oscillatory network of transcriptional regulators , 2000, Nature.

[11]  Hidde de Jong,et al.  Modeling and Simulation of Genetic Regulatory Systems: A Literature Review , 2002, J. Comput. Biol..

[12]  H. Kitano Systems Biology: A Brief Overview , 2002, Science.

[13]  R. Weiss,et al.  Ultrasensitivity and noise propagation in a synthetic transcriptional cascade. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[14]  D. Endy Foundations for engineering biology , 2005, Nature.

[15]  Pablo A. Iglesias,et al.  Quantifying robustness of biochemical network models , 2002, BMC Bioinformatics.

[16]  M. Savageau,et al.  Parameter Sensitivity as a Criterion for Evaluating and Comparing the Performance of Biochemical Systems , 1971, Nature.

[17]  R. Parker,et al.  Mechanisms and control of mRNA decapping in Saccharomyces cerevisiae. , 2000, Annual review of biochemistry.

[18]  E. Voit Design principles and operating principles: the yin and yang of optimal functioning. , 2003, Mathematical biosciences.

[19]  Weihai Zhang,et al.  State Feedback HINFINITY Control for a Class of Nonlinear Stochastic Systems , 2006, SIAM J. Control. Optim..

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

[21]  W. Zhang,et al.  State Feedback H_∞Control for a Class of Nonlinear Stochastic Systems , 2009 .

[22]  J. Liao,et al.  Design of artificial cell-cell communication using gene and metabolic networks. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[23]  Chen Bs,et al.  Stochastic H-2/H-infinity control with state-dependent noise , 2004 .

[24]  Giancarlo Ferrari-Trecate,et al.  The Switching Threshold Reconstruction Problem for Piecewise-Affine Models of Genetic Regulatory Networks , 2008, IEEE Transactions on Automatic Control.

[25]  Bor-Sen Chen,et al.  Robust H∞ filtering for nonlinear stochastic systems , 2005 .

[26]  Bor-Sen Chen,et al.  Robust Engineered Circuit Design Principles for Stochastic Biochemical Networks With Parameter Uncertainties and Disturbances , 2008, IEEE Transactions on Biomedical Circuits and Systems.

[27]  Kevin Truong,et al.  Identification and characterization of subfamily-specific signatures in a large protein superfamily by a hidden Markov model approach , 2002, BMC Bioinformatics.

[28]  Bor-Sen Chen,et al.  Robust synthetic biology design: stochastic game theory approach , 2009, Bioinform..

[29]  Roy Parker,et al.  Analyzing mRNA decay in Saccharomyces cerevisiae. , 2002, Methods in enzymology.

[30]  K. F. Tipton,et al.  Biochemical systems analysis: A study of function and design in molecular biology , 1978 .

[31]  Raymond A Zilinskas,et al.  The promise and perils of synthetic biology. , 2006, New Atlantis.

[32]  E D Sontag,et al.  Some new directions in control theory inspired by systems biology. , 2004, Systems biology.

[33]  Stephen P. Boyd,et al.  Linear Matrix Inequalities in Systems and Control Theory , 1994 .

[34]  Vipul Periwal,et al.  System Modeling in Cellular Biology: From Concepts to Nuts and Bolts , 2006 .

[35]  J. Chin Programming and engineering biological networks. , 2006, Current opinion in structural biology.

[36]  D. Court,et al.  Genetic engineering using homologous recombination. , 2002, Annual review of genetics.

[37]  Michael A. Savageau,et al.  Design principles for elementary gene circuits: Elements, methods, and examples. , 2001, Chaos.

[38]  J. Doyle,et al.  Robust perfect adaptation in bacterial chemotaxis through integral feedback control. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[39]  Hiroaki Kitano,et al.  Biological robustness , 2008, Nature Reviews Genetics.

[40]  E. Andrianantoandro,et al.  Synthetic biology: new engineering rules for an emerging discipline , 2006, Molecular systems biology.

[41]  Jeff Hasty,et al.  Engineered gene circuits , 2002, Nature.

[42]  Eberhard O. Voit,et al.  Computational Analysis of Biochemical Systems: A Practical Guide for Biochemists and Molecular Biologists , 2000 .

[43]  Balaji S. Srinivasan,et al.  The evolution of genetic regulatory systems in bacteria , 2004, Nature Reviews Genetics.

[44]  Bor-Sen Chen,et al.  Stochastic H2/H∞ control with state-dependent noise , 2004, IEEE Trans. Autom. Control..

[45]  Mark Goulian,et al.  Robust control in bacterial regulatory circuits. , 2004, Current opinion in microbiology.

[46]  Bor-Sen Chen,et al.  On the attenuation and amplification of molecular noise in genetic regulatory networks , 2006, BMC Bioinformatics.

[47]  L. Serrano,et al.  Engineering stability in gene networks by autoregulation , 2000, Nature.

[48]  Bor-Sen Chen,et al.  Robust $H_{\infty}$-Stabilization Design in Gene Networks Under Stochastic Molecular Noises: Fuzzy-Interpolation Approach , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[49]  W. R. Farmer,et al.  Improving lycopene production in Escherichia coli by engineering metabolic control , 2000, Nature Biotechnology.

[50]  E. Yaz Linear Matrix Inequalities In System And Control Theory , 1998, Proceedings of the IEEE.

[51]  E. Feron,et al.  History of linear matrix inequalities in control theory , 1994, Proceedings of 1994 American Control Conference - ACC '94.

[52]  Bor-Sen Chen,et al.  Robust Hinfinity filtering for nonlinear stochastic systems , 2005, IEEE Trans. Signal Process..

[53]  J. Keizer Biochemical Oscillations and Cellular Rhythms: The molecular bases of periodic and chaotic behaviour, by Albert Goldbeter , 1998 .