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The Strathprints institutional repository is a digital archive of University of Strathclyde research outputs.

Strathprints serves world leading Open Access research by the University of Strathclyde, including research by the Strathclyde Institute of Pharmacy and Biomedical Sciences (SIPBS), where research centres such as the Industrial Biotechnology Innovation Centre (IBioIC), the Cancer Research UK Formulation Unit, SeaBioTech and the Centre for Biophotonics are based.

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Model reduction of cell signal transduction networks via hybrid inference method

Jia, J.F. and Liu, T.Y. and Yue, H. and Wang, H. (2008) Model reduction of cell signal transduction networks via hybrid inference method. Journal of the Graduate School of the Chinese Academy of Sciences, 25 (3). pp. 355-366.

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Abstract

The mathematical model of cell signal transduction networks is highly nonlinear and complex, which involves a large number of variables and kinetics parameters. How to effectively develop the reduced-order model is a major problem for analyzing complex systems. In this work, a model reduction strategy via hybrid inference method is proposed for complex signal transducion networks. This approach synthesizes metabolic control analysis, sensitivity analysis, principal component analysis, and flux analysis to reduce the dimensions of the model and to decrease the number of the biological reactions. Using NF-κB signaling pathway as an example, the detailed model consists of 24 ordinary differential equations and 64 parameters. According to the model reduction strategy, the reduced-order model is composed of 17 ordinary differential equations, one algebraic equation, and 52 parameters. The simulation results demonstrate that the reduced-order model quantitatively predicts the dynamic characteristics of the system output, which are much the same as that of the detailed model. Therefore, the model reduction strategy provides guidance for the analysis and design of complex cell networks. It is more effective and more straightforward to estimate the unknown parameters by means of the reduced-order model.