Biochemical networks are not only complex but also extremely large. The dynamic biological model of great complexity resulting in a large number of parameters is a main difficulty for optimization and control processes. In practice, it is highly desirable to further simplify the structure of biological models for the sake of reducing computational cost or simplification for the task of system analysis. This paper considers the S-system model used for describing the response of biochemical networks. By introducing the technique of singular value decomposition (SVD), we are able to identify the major state variables and parameters and eliminate unimportant metabolites and the corresponding signal transduction pathways. The model reduction by multiobjective analysis integrates the criteria of reactive weight, sensitivity, and flux analyses to obtain a reduced model in a systematic way. The resultant model is closed to the original model in performance but with a simpler structure. Representative numerical examples are illustrated to prove feasibility of the proposed method.
Liu, Y.-C., Lin, C.-L., & Chuang, C.-H. (2013). An Approach for Model Reduction of Biochemical Networks. Computational Biology Journal, 2013, 1–14. https://doi.org/10.1155/2013/263973