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研究生： 吳宛珊
Wan-Shian Wu
論文名稱： 生化網路的強健性路徑控制設計
Robust Circuit Control Design of Biochemical Networks
指導教授： 陳博現
Bor-Sen Chen
口試委員:
學位類別： 碩士
Master
系所名稱： 電機資訊學院 - 電機工程學系
Department of Electrical Engineering
論文出版年： 2006
畢業學年度： 94
語文別： 中文
論文頁數： 34
中文關鍵詞： 強健性 、生化網路 、回授控制 、敏感度 、代謝工程
外文關鍵詞： robustness, biochemical network, feedback control, sensitivity, metabolic engineering
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    • 韌性在生化網路的故障安全防護裝置機制上扮演重要角色，一個生化網路必需能在各種環境下都能維持正常功能，也就是對環境干擾及內部(突變)干擾所造成的系統參數變化較不敏感，因此，對於一個韌性不足的生化網路，我們必需強健它，鑑於目前尚未有一個系統性的設計手法，本論文在建立一個有效率的控制設計，用以強健生化網路的韌性。
    在此論文裡，先用S-system模型，做穩態分析，建立生化網路的韌性和敏感度量測，再以回授路徑的概念，建立一個控制設計方法，使生化網路能達到我們想要的韌性和敏感度特性。韌性設計有兩個策略，一是增強系統的穩定使其更能忍受系統參數(動力學參數)變化，另一則是補償參數變化，以消弭它們造成的影響。再者，也加進敏感度條件的考驗，使設計的生化網路擁有夠低的敏感度，可以較不被環境因素干擾所影響。
    運用這個設計方法，將使生化網路擁有足夠的韌性和符合需求的敏感度，得以應付內外干擾所造成的影響，配合現今在生化和基因工程技術上的進步，相信將會在藥物開發和人造生化網路的穩定性上獲得應用。事先運用數學模型作全盤的分析及設計，以縮短試誤和實驗的時間和成本，也是系統生物學裡重要的課題。在此論文裡，將提供強健性設計的數學方法，相關生物技術參考文獻的整理，並舉三羧酸循環的例子加以說明和驗証。

    Background: Robustness plays an important role in the fail-safe mechanism of biochemical networks. A robust biochemical network should be able to cope with environmental changes, not be sensitive to kinetic parameter variations and have a slow rate of degradation of the system function. Therefore, for a biochemical network that lacks robustness to tolerate kinetic parameter variations and environmental changes, it is desirable to have an efficient control design to improve its robustness. Since there does not exist a systematic design method for this purpose, it is highly desirable to develop such a robust circuit control design method.
    Results: In this study, based on the steady state analyses of the synergism and saturation system (S-system) model, a robust control method is proposed via feedback and feedforward biochemical circuits. Two robust biochemical circuit design schemes are developed. One scheme is to improve the system structure stability to tolerate larger kinetic parameter variations, whereas the other is to compensate for the kinetic parameter variations to eliminate their effect. In addition, a multi-objective biochemical circuit control scheme is introduced for both the robust design against kinetic parameter variations and a desired sensitivity design to eliminate the effect of external disturbance simultaneously. Using the proposed systematic control method, a biochemical network can be designed to possess a desired robustness to tolerate kinetic parameter variations and a desired sensitivity to efficiently attenuate the effect of environmental disturbances.
    Conclusions: The proposed control design scheme for a biochemical network will provide a systematic robust circuit design method with a potential applications in synthetic circuit design for biotechnological purpose and drug design purpose. Recent advances in both metabolic and genetic engineering have made the robust biochemical circuit control approach feasible through the design and implementation of synthetic biological networks amenable to mathematical modeling and quantitative analysis. Finally, several computational simulation examples of robust circuit design including the robust design of the TCA cycle are used to illustrate the design procedure and for the performance confirmation of the proposed design method.

    Contents Contents i List of Figures ii 1. Introduction 1 2. Results 4 2.1 Robust Control Design Methods and Results …………………………... 4 2.1.1 Mathematical notations 4 2.1.2 Model of a biochemical network 4 2.1.3 Robust analysis of a biochemical network 6 2.2 Robust Catalytic Design of a Biochemical Network …………………… 9 2.3 Implementation of Biochemical Circuit ………………………………... 11 2.4 Systematic Robust Control Design ……………………………………... 13 2.5 Multipurpose Circuit Control Design of a Biochemical Network ……… 16 3. Computational Simulation 19 4. Discussion 23 5. Conclusions 25 List of Figures Fig. 1. (a) The cascaded biochemical network. The enzymes in red are corresponding to their catalytic reactions. (b) The time responses of (a) in the nominal parameter case. (c) The time responses of (a) under parameter perturbations in Equation(15). (d) The time responses of (a) under parameter perturbations in Equation(30). (e) The designed cascaded biochemical network with (blue line) by the multi-objective design in Example . (f) The time responses of the designed biochemical network in (e) under parameter perturbations in Equation(15). (g) The designed cascaded biochemical network with and (green lines) by the multi-objective design in Example . (h) The time responses of the designed biochemical networks in (g) under parameter perturbations in Equation(15). 31 Fig. 2. Flowchart of the control circuit implementation based on dynamic controller design [40] in Fig. 1(e). 32 Fig. 3. (a) The TCA cycle network redrawn from KEGG database and [2,45]. (b) The time responses of Fig. 2a in the nominal parameter case. 33 Fig. 3. (c) The time responses of the TCA cycle network under the parameter perturbations in Equation(39). (d) The time responses of the designed TCA cycle network with (blue line) under parameter perturbations in Equation(39). 34

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