Surovtsova Irina, Simus Natalia, Hübner Katrin, Sahle Sven, Kummer Ursula
University of Heidelberg, Im Neuenheimer Feld 267, 69120 Heidelberg, Germany.
BMC Syst Biol. 2012 Mar 5;6:14. doi: 10.1186/1752-0509-6-14.
Given the complex mechanisms underlying biochemical processes systems biology researchers tend to build ever increasing computational models. However, dealing with complex systems entails a variety of problems, e.g. difficult intuitive understanding, variety of time scales or non-identifiable parameters. Therefore, methods are needed that, at least semi-automatically, help to elucidate how the complexity of a model can be reduced such that important behavior is maintained and the predictive capacity of the model is increased. The results should be easily accessible and interpretable. In the best case such methods may also provide insight into fundamental biochemical mechanisms.
We have developed a strategy based on the Computational Singular Perturbation (CSP) method which can be used to perform a "biochemically-driven" model reduction of even large and complex kinetic ODE systems. We provide an implementation of the original CSP algorithm in COPASI (a COmplex PAthway SImulator) and applied the strategy to two example models of different degree of complexity - a simple one-enzyme system and a full-scale model of yeast glycolysis.
The results show the usefulness of the method for model simplification purposes as well as for analyzing fundamental biochemical mechanisms. COPASI is freely available at http://www.copasi.org.
鉴于生物化学过程背后的复杂机制，系统生物学研究人员倾向于构建越来越复杂的计算模型。然而，处理复杂系统会带来各种问题，例如难以直观理解、存在多种时间尺度或参数不可识别等。因此，需要一些方法，至少是半自动地，来帮助阐明如何降低模型的复杂性，同时保持重要行为并提高模型的预测能力。结果应易于获取和解释。在最佳情况下，此类方法还可能提供对基本生化机制的洞察。
我们开发了一种基于计算奇异摄动（CSP）方法的策略，该策略可用于对大型复杂动力学常微分方程系统进行“生化驱动”的模型简化。我们在COPASI（复杂途径模拟器）中实现了原始的CSP算法，并将该策略应用于两个不同复杂程度的示例模型——一个简单的单酶系统和一个酵母糖酵解的全尺寸模型。
结果表明该方法对于模型简化以及分析基本生化机制很有用。COPASI可从http://www.copasi.org免费获取。
