Abstract
BackgroundGiven 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.ResultsWe 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.ConclusionThe 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.
Highlights
Given the complex mechanisms underlying biochemical processes systems biology researchers tend to build ever increasing computational models
Aiming at a comprehensive understanding of the dynamic behavior of such systems has led to the development of an ever increasing number of computational models which are in the majority of cases formulated on the basis of ordinary differential equations (ODEs) [1]
2.1 Computational Singular Perturbation (CSP) in COPASI Consider a system consisting of K biochemical reactions, the dynamics of which is determined by a system of N ordinary differential equations: d y (t) = g(y(t)) =
Summary
2.1 CSP in COPASI Consider a system consisting of K biochemical reactions, the dynamics of which is determined by a system of N ordinary differential equations:. The Radical Pointer from the CSP data shows that the complex C dominates the fast mode The contributions of both reactions to the slow and fast modes are comparable (see Figure 2, which displays the evolution in time of Radical Pointer and Participation Indices). The new chemical equation for vPDC is: BPG PEP + 2 ADP → ACA + 2 A TP After these four simplification steps the full model (original values in parentheses) has been reduced eventually to 17 (22) species and 19 (24) reactions with a total of 43 (59) parameters (the reduced reaction network is depicted on the Figure 8). This discrepancy is of (only) quantitative nature and it does not occur if the full model is reduced by just three reactions (instead of five) as presented in Additional file 3
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