Name
Abstract | ||
|---|---|---|
The current trend in dynamical modelling of biochemical systems is to construct more and more mechanistically detailed and thus complex models. The complexity is reflected in the number of dynamic state variables and parameters, as well as in the complexity of the kinetic rate expressions. However, a greater level of complexity, or level of detail, does not necessarily imply better models, or a better understanding of the underlying processes. Data often does not contain enough information to discriminate between different model hypotheses, and such overparameterization makes it hard to establish the validity of the various parts of the model. Consequently, there is an increasing demand for model reduction methods.We present a new reduction method that reduces complex rational rate expressions, such as those often used to describe enzymatic reactions. The method is a novel term-based identifiability analysis, which is easy to use and allows for user-specified reductions of individual rate expressions in complete models. The method is one of the first methods to meet the classical engineering objective of improved parameter identifiability without losing the systems biology demand of preserved biochemical interpretation.The method has been implemented in the Systems Biology Toolbox 2 for MATLAB, which is freely available from http://www.sbtoolbox2.org. The Supplementary Material contains scripts that show how to use it by applying the method to the example models, discussed in this article. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1093/bioinformatics/btn035 | Bioinformatics |
Keywords | Field | DocType |
better model,complex rational rate expression,kinetic rate expression,new reduction method,model reduction method,biochemical rate expression,different model hypothesis,complete model,individual rate expression,example model,complex model,complexity reduction,system biology,level of detail,kinetics | MATLAB,Expression (mathematics),Level of detail,Computer science,Identifiability,Toolbox,Systems biology,Theoretical computer science,Reduction (complexity),State variable,Bioinformatics | Journal |
Volume | Issue | ISSN |
24 | 6 | 1367-4811 |
Citations | PageRank | References |
7 | 0.65 | 2 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Henning Schmidt | 1 | 142 | 16.73 |
| Mads F Madsen | 2 | 7 | 0.65 |
| Sune Danø | 3 | 7 | 0.65 |
| Wolfram Burgard | 4 | 58 | 6.17 |