Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/6184
標題: 以多樣分析演算法簡化生化網路
Model Reduction of Biochemical Networks by Hybrid Algorithm
作者: 劉晏昌
Liu, Yen-Chang
關鍵字: model reduction;模式簡化;biological system;biochemical network;stoichiometric;singular value decomposition;生物系統;生化網路;化學計量矩陣;奇異值分解
出版社: 電機工程學系所
引用: [1] B. O. Palsson, Systems Biology: Properties of Reconstructed Networks Systems Biology, Cambridge University Press, New York, 2006. [2] J. A. Papin, J. L. Reed and B. O. Palsson, “Hierarchical thinking in network biology: the unbiased modularization of biochemical networks,” Trends in Biochemical Sciences, vol. 29, pp.641-647, 2004. [3] E. O. Voit, Computational Analysis of Biochemical Systems: A Practical Guide for Biochemists and Molecular Biologists, Cambridge University Press, New York, 2000. [4] M. A. Savageau and E. O. Voit, “Recasting Nonlinear Differential Equations as S-Systems: A Canonical Nonlinear Form,” MATHEMATICAL BIOSCIENCES, vol. 87, pp.83-115, 1987. [5] F. S. Wang, C. L. Ko and E. O. Voit, “Kinetic modeling using S-systems and lin-log approaches,” Biochemical Engineering Journal, vol. 33, pp.238-247, 2007. [6] I. Famili and B. O. Palsson, “Systemic metabolic reactions are obtained by singular value decomposition of genome-scale stoichiometric matrices,” Journal of Theoretical Biology, vol. 224, pp.87-96, 2003. [7] M. E. Wall, A. Rechtsteiner and L. M. Rocha, A Practical Approach to Microarray Data Analysis, Kluwer Academic Publishers, Norwell, Massachusetts, 2003. [8] O. Alter, P. O. Brown and D. Botstein, “Singular value decomposition for genome-wide expression data processing and modeling,” Pnas, vol. 97, pp.10101-10106, 2000. [9] N. D. Price, J. L. Reed, J. A. Papin, I. Famili and B. O. Palsson, “Analysis of Metabolic Capabilities Using Singular Value Decomposition of Extreme Pathway,” Biophysical Journal, vol. 84, pp.794-804, 2003. [10] B. S. Chen, Y. C. Wang, W. S. Wu and W. H. Li, “A new measure of the robustness of biochemical networks,” Bioinformatics, vol. 21, pp.2698-2705, 2005. [11] G. Liu, M. T. Swihart and S. Neelamegham, “Sensitivity, principal component and flux analysis applied to signal transduction: the case of epidermal growth factor mediated signaling,” Bioinformatics, vol. 21, pp.1194-1202, 2005. [12] A. P. Burgard, E. V. Nikolaev, C. H. Schilling and C. D. Maranas, “Flux Coupling Analysis of Genome-Scale Metabolic Network Reconstructions,” Genome Research, vol. 14, pp.301-312, 2004. [13] P. J. Sands and E. O. Voit, “Flux-based estimation of parameters in S-systems,” Ecological Modeling, vol. 93, pp.75-88, 1996. [14] H. Schmidt, M. F. Madsen, S. Dano and G. Cedersund, “Complexity reduction of biochemical rate expressions,” Bioinformatics, vol. 24, pp.848-854, 2008. [15] H. Conzelmann, J. Saez-Rodriguez, T. Sauter, E. Bullinger, F. Allgower and E. D. Gilles, “Reduction of mathematical models of signal transduction network: simulation-based approach applied to EGF receptor signaling,” Systems Biology, vol. 1, pp.159-169, 2004. [16] A. N. Gorban, N. K. Kazantzis, I. G. Kevrekidis, H. C. Ottinger and C. Theodoropoulos, Model Reduction and Coarse-Graining Approaches for Multiscale Phenomena, Springer-Verlag GmbH, Berlin, 2006. [17] J. Kim, D. G. Bates, I. Postlethwaite, L. Ma and P. A. Iglesias, “Robustness analysis of biochemical network models,” Systems Biology, vol. 153, pp.96-104, 2006. [18] M. T. Laub and W. F. Loomis, “A Molecular Network That Produces Spontaneous Oscillations in Excitable Cells of Dictyostelium,” Mol. Biol. Cell, vol. 9, pp.3521-3532, 1998. [19] G. Farkas, “Kinetic lumping schemes,” Chemical Engineering Science, vol. 54, pp. 3909-3915, 1999. [20] J. Saez-Rodriguez, A. Kremling, H. Conzelmann, K. Bettenbrock, and E. D. Gilles, “Modular analysis of signal transduction networks,” IEEE Control Systems Magazine, vol. 24, no. 4, pp. 35-52, 2004. [21] A. Kremling, S. Fischer, T. Sauter, K. Bettenbrock, and E. D. Gilles, “Time hierarchies in the escherichia coli carbohydrate uptake and metabolism,” Biosystems, vol. 73, pp. 57-71, 2004. [22] R. Boyer, Concepts in Biochemistry, Second edition, Wiley, USA, 2006.
摘要: 
生化網路是個極巨大且複雜的系統,因此在建立數學模組和分析上相當困難。簡化生化網路的結構並減輕計算的負擔及後續的分析一直是近年科學家研究的重點。本論文利用協和飽和性系統搭配化學計量矩陣法來建構數學架構,再透過奇異值分解的技巧找出反應路徑及相對流量強度。模式簡化整合三種分析方法,分別是反應權重比較、濃度與反應常數靈敏度分析與反應流量分析。藉各別之剩餘誤差來比較原始模組與簡化模組間的關係,模式簡化的結果顯示原始與簡化後的模組其反應相當匹配。希望本研究發展之理論能助於未來大型基因網路研究的簡化。

Biochemical networks are not only complex but also extremely large. The construction and analysis for the mathematical model is thus relatively difficult. In practice, it is usually desirable to further simplify the structure of biological system models for the sake of reducing computation burden or simplification of the task of analysis. Stoichiometric and S-system (S as synergism and saturation) method help us to system modeling. By introducing the technique of singular value decomposition (SVD) it is possible to identify the major flux rate and hence deduce the corresponding signal transduction path. Model reduction by the proposed hybrid method integrates the factors of reactive weight, sensitivity and flux analysis to obtain a model which is closed to the original model in performance but with simpler structure. Satisfactory simulation results for a variety of biochemical models show effectiveness of the proposed approach.
URI: http://hdl.handle.net/11455/6184
其他識別: U0005-0508201110300200
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