Picture of athlete cycling

Open Access research with a real impact on health...

The Strathprints institutional repository is a digital archive of University of Strathclyde's Open Access research outputs. Strathprints provides access to thousands of Open Access research papers by Strathclyde researchers, including by researchers from the Physical Activity for Health Group based within the School of Psychological Sciences & Health. Research here seeks to better understand how and why physical activity improves health, gain a better understanding of the amount, intensity, and type of physical activity needed for health benefits, and evaluate the effect of interventions to promote physical activity.

Explore open research content by Physical Activity for Health...

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.

Full text not available in this repository. Request a copy from the Strathclyde author

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.