周瑞仁Chou, Jui-Jen臺灣大學:生物產業機電工程學研究所蘇煥評Su, Huan-PingHuan-PingSu2010-05-052018-07-102010-05-052018-07-102008U0001-2508200811451900http://ntur.lib.ntu.edu.tw//handle/246246/180213本研究係應用OKID(Observer / Kalman Filter Identification)於生物系統的建模上，藉由OKID求出的系統矩陣與觀測增益矩陣可以進行系統性地分析、探討擾動系統的穩定性與強健性。OKID屬於狀態空間模型的參數估計方法之ㄧ，本研究藉此描述代謝反應與強健性評估。OKID可有效地鑑別狀態空間模型參數，在少許資料量下即可估計參數、確定適當的模型階數、降低雜訊影響並接受各式資料輸入等特性。由於S-system模型目前被廣泛地應用於代謝網路的建模上，因此藉由輸入各種型式的資料，透過S-system產生輸出資料，利用這些輸出入資料以及OKID的鑑別方法，得出系統參數，並將OKID鑑別出來的模型與S-system比較，OKID鑑別之狀態空間系統矩陣更適於分析生物系統。針對7個生化反應路徑，經由OKID鑑別出來的模型資料與原始資料相比對，至少具有92.1%以上的相似度；並且可依據模型特徵值有效地評估系統強健性。The purpose of this paper is to apply OKID (Observer/Kalman Filter Identification), which is an approach for state space model parameter estimation, to the modeling of bio-systems. With the identified system matrices and observer gain matrix obtained from OKID approach, we can systematically investigate and analyze the stability and robustness of a perturbed system. OKID can effectively and easily identify a state space model, which requires fewer data for estimation, determines appropriate model order, reduces disturbance effect and allows general data inputs. Since S-system has currently been widely used in the modeling of metabolism networks, we adopt S-system to generate output data with various inputs, and use the input-output data sets to identify parameters by OKID approach. Furthermore, compared with S-system model, the model identified by OKID is more suitable for analyzing a bio-system based on the system matrices of the model. Seven numerical examples for biological pathways are given to illustrate and validate the method developed in this study. Results suggest that the built models can fit original data with at least 92.1% similarity and easily evaluate the robustness of the system by eigenvalues of the model.論文口試委員審定書 i謝 ii要 iiibstract ivable of Contents vigures viiables xihapter 1 Introduction 1hapter 2 Literature Review 3hapter 3 Materials and Methods 7.1 S-system Model of Biochemical Networks 7.2 Research Framework 8.3 OKID 10.4 A Novel Robustness Measure 16hapter 4 Results and Discussions 20.1 Example 1: Pathway with two dependent variables, two constant sources, and two feedforward/one feedback 20.2 Example 2: Linear pathway with three dependent variables, one constant source, and one feedback 26.3 Example 3: Cascaded pathway with three dependent variables, one constant source, and two feedback 33.4 Example 4: Branched pathway with three dependent variables, one constant source, and two feedback 42.5 Example 5: Branched pathway with four dependent variables, one constant source, and one feedback 50.6 Example 6: Generic branched pathway with four dependent variables, one constant source, and one feedforward/one feedback 61.7 Example 7: Ethanol production pathway in Saccharomyces cerevisiae with five dependent variables, nine constant sources, and one feedforward/one feedback 70hapter 5 Conclusions 84eferences 85ppendix A Identified Parameters 88ppendix B Similarity Measure 110ppendix C Robustness Analysis (Chen, et al., 2005) 111application/pdf700658 bytesapplication/pdfen-USOKIDERA/DC穩定性強健性S-system模型StabilityRobustnessS-system modelthesishttp://ntur.lib.ntu.edu.tw/bitstream/246246/180213/1/ntu-97-R95631006-1.pdf