Browsing by Author "Lee, Hsiao-Ching"

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Robust model matching design methodology for a stochastic synthetic gene network
(2011-03-01)
Chen, Bor-Sen
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Chang, Chia-Hung
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Wang, Yu-Chao
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Wu, Chih-Hung
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Lee, Hsiao-Ching
Synthetic biology has shown its potential and promising applications in the last decade. However, many synthetic gene networks cannot work properly and maintain their desired behaviors due to intrinsic parameter variations and extrinsic disturbances. In this study, the intrinsic parameter uncertainties and external disturbances are modeled in a non-linear stochastic gene network to mimic the real environment in the host cell. Then a non-linear stochastic robust matching design methodology is introduced to withstand the intrinsic parameter fluctuations and to attenuate the extrinsic disturbances in order to achieve a desired reference matching purpose. To avoid solving the Hamilton-Jacobi inequality (HJI) in the non-linear stochastic robust matching design, global linearization technique is used to simplify the design procedure by solving a set of linear matrix inequalities (LMIs). As a result, the proposed matching design methodology of the robust synthetic gene network can be efficiently designed with the help of LMI toolbox in Matlab. Finally, two in silico design examples of the robust synthetic gene network are given to illustrate the design procedure and to confirm the robust model matching performance to achieve the desired behavior in spite of stochastic parameter fluctuations and environmental disturbances in the host cell. (C) 2010 Elsevier Inc. All rights reserved.
Robust synthetic biology design: stochastic game theory approach
(2009-07-15)
Chen, Bor-Sen
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Chang, Chia-Hung
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Lee, Hsiao-Ching
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生物科技學系
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Department of Biological Science and Technology
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 design 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 non-linear stochastic game method directly, the Takagi-Sugeno (T-S) fuzzy model is proposed to approximate the non-linear 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.
Robust synthetic gene network design via library-based search method
(2011-10-01)
Wu, Chih-Hung
;
Lee, Hsiao-Ching
;
Chen, Bor-Sen
;
生物科技學系
;
Department of Biological Science and Technology
Motivation: Synthetic biology aims to develop the artificial gene networks with desirable behaviors using systematic method. These networks with desired behaviors could be constructed using diverse biological parts, which may limit the development to complex synthetic gene networks. Fortunately, some well-characterized promoter libraries for engineering gene networks are widely available. Thus, a synthetic gene network can be constructed by selecting adequate promoters from promoter libraries to achieve the desired behaviors. However, the present promoter libraries cannot be directly applied to engineer a synthetic gene network. In order to efficiently select adequate promoters from promoter libraries for a synthetic gene network, promoter libraries are needed to be redefined based on the dynamic gene regulation. Results: Based on four design specifications, a library-based search method is proposed to efficiently select the most adequate promoter set from the redefined promoter libraries by a genetic algorithm (GA) to achieve optimal reference tracking design. As the number and size of promoter libraries increase, the proposed method can play an important role in the systematic design of synthetic biology.