Bioinformatics Advance Access published online on May 12, 2009
Bioinformatics, doi:10.1093/bioinformatics/btp310
Robust synthetic biology design: stochastic game theory approach
1 Lab. of Control and Systems Biology, Department of Electrical Engineering, National Tsing Hua University, Hsinchu, 30013, Taiwan, R.O.C.
2 Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, 30068, Taiwan, R.O.C.
*To whom correspondence should be addressed. Dr. Bor-Sen Chen, E-mail: bschen{at}ee.nthu.edu.tw
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Abstract |
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Motivation: Synthetic biology is to engineer artificial biological systems to investigate natural biological phenomena and for a variety of applications. However, the development of synthetic gene networks is still difficult and most newly created gene networks are non-functioning due to uncertain initial conditions and disturbances of extra-cellular environments on the host cell. At present, how to de-sign a robust synthetic gene network to work properly under these uncertain factors is the most important topic of synthetic biology.
Results: A robust regulation design is proposed for a stochastic synthetic gene network to achieve the prescribed steady states under these uncertain factors from the minimax regulation perspective. This minimax regulation design problem can be transformed to an equivalent stochastic game problem. Since it is not easy to solve the robust regulation design problem of synthetic gene networks by nonlinear stochastic game method directly, the Takagi-Sugeno (T-S) fuzzy model is proposed to approximate the nonlinear synthetic gene network via the linear matrix inequality (LMI) technique through the Robust Control Toolbox in Matlab. Finally, an in silico example is given to illustrate the design procedure and to confirm the efficiency and efficacy of the proposed robust gene design method.
Contact: bschen{at}ee.nthu.edu.tw, hsiaoching.lee{at}gmail.com
Supplementary information: {{http://www.ee.nthu.edu.tw/bschen/SyntheticBioDesign_supplement.pdf}}
Associate Editor: Prof. Alfonso Valencia
Received on November 12, 2008; revised on April 14, 2009; accepted on May 6, 2009
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