Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/8338
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dc.contributor陳博現zh_TW
dc.contributor李柏坤zh_TW
dc.contributor.advisor林俊良zh_TW
dc.contributor.advisorChun-Liang Linen_US
dc.contributor.author劉元威zh_TW
dc.contributor.authorLiu, Yuan-Weien_US
dc.contributor.other中興大學zh_TW
dc.date2009zh_TW
dc.date.accessioned2014-06-06T06:41:24Z-
dc.date.available2014-06-06T06:41:24Z-
dc.identifierU0005-2407200813152200zh_TW
dc.identifier.citation[1] 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. [2] O. Wolkenhauer, B. K. Ghosh and K. H. Cho, Control and coordination in biochemical networks, Control Systems Magazine, IEEE, Vol. 24, pp. 30-34, 2004. [3] J. Saez-Rodriguez, A. Kremling, H. Conzelmann, K. Bettenbrock and E. D. Gilles, Modular analysis of signal transduction networks, Control Systems Magazine, IEEE, Vol. 24, pp. 35-52, 2004. [4] N. V. Torres and E. O. Voit, Pathway analysis and optimization in metabolic engineering, Cambridge University Press, 2002. [5] J. D. Murray, Mathematical Biology, Springer-Verlag, New York, 1993. [6] M. S. Calder and D. Siegel, Properties of the Michaelis-Menten mechanism in phase space, Journal of Mathematical Analysis and Applications, Vol. 339, pp. 1044-1064, 2008. [7] H. Conzelmann, J. Saez-Rodriguez, T. Sauter, E. Bullinger, F. Allgower and E. D. Gilles, Reduction of mathematical models of signal transduction networks: simulation-based approach applied to EGF receptor signalling, Systems Biology, Vol. 1, pp. 159-169, 2004. [8] H. Conzelmann, J. Saez-Rodriguez, T. Sauter, B. N. Kholodenko and E. D. Gilles, A domain-oriented approach to the reduction of combinatorial complexity in signal transduction networks, BMC Bioinformatics, Vol. 7, No. 34, 2006. [9] M. A. Savageau, Biochemical systems analysis: a study of function and design in molecular biology, The Quarterly Review of Biology, Vol. 52, pp. 292-293, 1977. [10] 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. [11] E. O. Voit, Computational analysis of biochemical systems, Cambridge University Press, Cambridge, UK, 2000. [12] E. O. Voit, Canonical nonlinear modeling: S-systems approach to understanding complexity, Van Nostrand Reinhold, New York, 1991. [13] K. Zhou, Essentials of robust control, Prentice Hall, New Jersey, 1998. [14] C. T. Chen, Linear system theory and design, Oxford University Press, New York, 1998. [15] K. A. Hoo and J. C. Kantor, Global linearization and control of a mixed culture bioreactor with competition and external inhibition, Mathematical Biosciences, Vol. 82, pp. 43-62, 1986. [16] K. A. Hoo and J. C. Kantor, Linear feedback equivalence and control of an unstable biological reactor, Chemical Engineering Communications, Vol. 46, pp. 385-399, 1986. [17] B. Noble and J. W. Daniel, Applied linear algebra, Pearson Education Taiwan, Taiwan, 2003. [18] R. W. Brockett, Nonlinear systems and differential geometry, Proceedings of the IEEE, Vol. 64, pp. 61-72, 1976. [19] A. Ervadi-Radhakrishnan and E. O. Voit, Controllability of non-linear biochemical systems, Mathematical Biosciences, Vol. 196, pp. 99-123, 2005. [20] C. L. Lin, Mathematics of modern control theory, Kaun Tang international publications, Taipei, 2007. [21] M. A. Savageau, Design principles for elementary gene circuits: elements, methods and examples, Chaos, Vol. 11 pp. 142-159, 2001. [22] M. A. Savageau, Alternative designs for a genetic switch: analysis of switching times using the piece-wise power-law representation, Mathematical Biosciences, Vol. 180, pp. 237-253, 2002. [23] M. A. Savageau, Biochemical systems analysis, Journal of Theoretical Biology, Vol. 25, pp. 365-369, 1969. [24] E. O. Voit, M. A. Savageau, Accuracy of alternative representations for integrated biochemical systems, Biochemistry, Vol. 26, pp. 6869-6880, 1987. [25] A. Sorribas and M. A. Savageau, A comparison of variant theories of intact biochemical systems. 1. enzyme-enzyme interactions and biochemical systems Theory. Mathematical biosciences, Vol. 94, pp. 161-193, 1989. [26] M. A. Savageau, Parameter sensitivity as a criterion for evaluating and comparing the performance of biochemical systems, Nature, Vol. 229, pp.542-544, 1971 [27] R. W. Brockett, Feedback invariants for nonlinear systems, Proceedings of the IFAC World Congress, Helsinki, pp. 1115-1120, 1978. [28] A. Isidori, Nonlinear control systems: an introduction, Springer, New York, 1995. [29] Q. Lu, Y. Sun and S. Mei, Nonlinear control systems and power system Dynamics, Kluwer Academic Publishers, 2001. [30] H. K. Khalil, Nonlinear systems, Pearson Education, New Jersey, 2002. [31] A. Sorribas and M. Cascante, Structure identifiability in metabolic pathways: parameter estimation in models based on the power-law formalism, Biochemical Journal, Vol. 298, pp. 303-311, 1994.en_US
dc.identifier.urihttp://hdl.handle.net/11455/8338-
dc.description.abstract生物系統中的訊息傳導網路有著高度的複雜性。如何有系統地將訊息傳導網路數學模型化,並更進一步發展出一套適當且有效率的控制策略,是對控制學門的研究學者們非常有吸引力的。在本論文的初步研究中,提出了訊息傳導網路數學模型化的方法,並且也提出了一個控制器設計構想;其中在數學模型化的部份,本論文提出一個新的串接分析數學模型。本論文提供了廣泛對於訊息傳導網路系統的動態、穩態、穩定性、靈敏度和控制器的設計模擬驗證。希望本研究發展的理論對未來訊息傳導網路的研究發展上有正面的助益。zh_TW
dc.description.abstractSignal transduction networks of biological systems are highly complex. How to mathematically describe a signal transduction network by systematic approaches so as to further develop an appropriate and effective control strategy is attractive to control engineers. In this thesis, a mathematical model and a controller design idea of signal transduction networks are presented. For constructing mathematical model, a new cascaded analysis model is proposed. Dynamic analysis, steady-state analysis, stability analysis, sensitivity analysis and controller design are simulated and fully verified. It is expected that this research could be a basis for constructing mathematical models and designing controllers for signal transduction networks in biological systems.en_US
dc.description.tableofcontentsContents 誌謝 (i) 中文摘要 (ii) Abstract (iii) Contents (iv) List of Figures (vi) List of Tables (ix) Chapter 1 Introduction (1) Chapter 2 Mathematical Model for Signal Transduction Networks (4) 2.1 S-systems (4) 2.2 Parameter Estimation (5) 2.3 Signal Transduction Networks Model (7) 2.4 Cascaded Analysis Model (10) Chapter 3 Stability and Sensitivity Analysis (13) 3.1 Threshold parameter values of S-systems (13) 3.2 Stability of Linearized Model (15) 3.3 Sensitivity Analysis (19) Chapter 4 Control Design (24) 4.1 Control Design Using Feedback Linearization (24) Chapter 5 Numerical Simulations (30) 5.1 Parameter estimation (30) 5.2 Cascaded analysis model (32) 5.3 Stability of Taylor’s linerized model (33) 5.4 Sensitivity analysis (35) 5.5 Control design (37) 5.6 Control Design for Cascaded Analysis Model (41) Chapter 6 Discussion (47) Chapter 7 Conclusions (49) Reference (51)zh_TW
dc.language.isoen_USzh_TW
dc.publisher電機工程學系所zh_TW
dc.relation.urihttp://www.airitilibrary.com/Publication/alDetailedMesh1?DocID=U0005-2407200813152200en_US
dc.subjectsignal transduction networksen_US
dc.subject訊息傳導網路zh_TW
dc.subjectbiochemical networksen_US
dc.subjectsystems biologyen_US
dc.subjectfeedback linearizationen_US
dc.subjectLyapunov stabilityen_US
dc.subject生化網路zh_TW
dc.subject系統生物zh_TW
dc.subject回授線性化zh_TW
dc.subject李亞普諾夫穩定性zh_TW
dc.title訊息傳導網路分析與控制設計zh_TW
dc.titleAnalsis and Control Design for Signal Transduction Networksen_US
dc.typeThesis and Dissertationzh_TW
item.fulltextno fulltext-
item.languageiso639-1en_US-
item.openairetypeThesis and Dissertation-
item.cerifentitytypePublications-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.grantfulltextnone-
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