| Analysis of regulatory architectures in BST |
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| 作者姓名： | BIAN Fuping LIANG Min ZHAO Xueming |
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| 作者单位： | Department of Mathematics, School of Science, Tianjin University;Liu Hui Center for Applied Mathematics, Nankai University & Tianjin University,Department of Mathematics, School of Science, Tianjin University;Liu Hui Center for Applied Mathematics, Nankai University & Tianjin University,School of Chemical Engineering, Tianjin University, Tianjin 300072, China |
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| 基金项目： | Supported by the National Natural Science Foundation of China (Grant No. 20036010) |
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| 摘 要： | Based on the data envelopment analysis (DEA) theory, the optimal pathway of metabolic reaction networks in biochemical systems is studied. After calculating the mixed-integer linear programming (MILP) model given by Bailey et al. twice, the decision making units (DMU) and the prediction model of DEA are constructed, where the inputs are levels of manipulated parameters (enzyme) and outputs are concentrations of metabolites. When the metabolic networks are reconstructed, the data are obtained by calculating MILP framework twice and the optimal levels of the manipulated parameter at different regular loops are predicted, thus simplifying the calculations of Bailey's.
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| 关 键 词： | metabolic reaction networks MILP the prediction model of DEA efficient DMU |
Analysis of regulatory architectures in BST |
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| BIAN Fuping,LIANG Min,ZHAO Xueming.Analysis of regulatory architectures in BST[J].Progress in Natural Science,2003,13(12):914-919. |
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| Authors: | BIAN Fuping LIANG Min Zhao Xueming |
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| Institution: | 1. Department of Mathematics, School of Science, Tianjin University;Liu Hui Center for Applied Mathematics, Nankai University & Tianjin University
2. School of Chemical Engineering, Tianjin University, Tianjin 300072, China |
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| Abstract: | Based on the data envelopment analysis (DEA) theory, the optimal pathway of metabolic reaction networks in biochemical systems is studied. After calculating the mixed-integer linear programming (MILP) model given by Bailey et al. twice, the decision making units (DMU) and the prediction model of DEA are constructed, where the inputs are levels of manipulated parameters (enzyme) and outputs are concentrations of metabolites. When the metabolic networks are reconstructed, the data are obtained by calculating MILP framework twice and the optimal levels of the manipulated parameter at different regular loops are predicted, thus simplifying the calculations of Bailey's. |
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| Keywords: | metabolic reaction networks MILP the prediction model of DEA efficient DMU |
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