graphical approach to model reduction for nonlinear biochemical networks图形化的模型降阶方法对非线性生化网络.pdfVIP

Graphical Approach to Model Reduction for Nonlinear Biochemical Networks David O. Holland, Nicholas C. Krainak, Jeffrey J. Saucerman* Department of Biomedical Engineering, Robert M. Berne Cardiovascular Research Center, University of Virginia, Charlottesville, Virginia, United States of America Abstract Model reduction is a central challenge to the development and analysis of multiscale physiology models. Advances in model reduction are needed not only for computational feasibility but also for obtaining conceptual insights from complex systems. Here, we introduce an intuitive graphical approach to model reduction based on phase plane analysis. Timescale separation is identified by the degree of hysteresis observed in phase-loops, which guides a ‘‘concentration-clamp’’ procedure for estimating explicit algebraic relationships between species equilibrating on fast timescales. The primary advantages of this approach over Jacobian-based timescale decomposition are that: 1) it incorporates nonlinear system dynamics, and 2) it can be easily visualized, even directly from experimental data. We tested this graphical model reduction approach using a 25-variable model of cardiac b1-adrenergic signaling, obtaining 6- and 4-variable reduced models that retain good predictive capabilities even in response to new perturbations. These 6 signaling species appear to be optimal ‘‘kinetic biomarkers’’ of the overall b1-adrenergic pathway. The 6-variable reduced model is well suited for integration into multiscale models of heart function, and more generally, this graphical model reduction approach is readily applicable to a variety of other complex biological systems. Citation: Holland DO, Krainak NC, Saucerman JJ (2011) Graphical Approach to Model Reduction for Nonlinear Biochemical Networks. PLoS ONE 6(8): e23795. doi: