Application of OKID on Modeling and Robustness Evaluation of Bio-systems

• Main Authors: Huan-Ping Su, 蘇煥評
• Other Authors: Jui-Jen Chou
• Format: Others
• Language: en_US
• Published: 2008
• Online Access: http://ndltd.ncl.edu.tw/handle/69784360847209514808

碩士 === 國立臺灣大學 === 生物產業機電工程學研究所 === 96 === 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.