Motivation:
The current trend in dynamical modeling of biochemicalsystems is to construct more and more mechanistically detailedand thus complex models. The complexity is reflected in thenumber of dynamic state variables and parameters, as well asin the complexity of the kinetic rate expressions. However,a greater level of complexity, or level of detail, does notnecessarily imply better models, or a better understanding ofthe underlying processes. Data does often not contain enoughinformation to discriminate between different model hypotheses,and such overparameterization makes it hard to establish thevalidity of the various parts of the model. Consequently thereis an increasing demand for model reduction methods.
Results:
We present a new reduction method that reduces complexrational rate expressions, such as those often used to describeenzymatic reactions. The method is a novel term-based identifiabilityanalysis, which is easy to use and allows for user-specifiedreductions of individual rate expressions in complete models.The method is one of the first methods to meet the classicalengineering objective of improved parameter identifiabilitywithout losing the systems biology demand of preserved biochemicalinterpretation.