Abstract
BackgroundThe dynamics of biochemical networks can be modelled by systems of ordinary differential equations. However, these networks are typically large and contain many parameters. Therefore model reduction procedures, such as lumping, sensitivity analysis and time-scale separation, are used to simplify models. Although there are many different model reduction procedures, the evaluation of reduced models is difficult and depends on the parameter values of the full model. There is a lack of a criteria for evaluating reduced models when the model parameters are uncertain.ResultsWe developed a method to compare reduced models and select the model that results in similar dynamics and uncertainty as the original model. We simulated different parameter sets from the assumed parameter distributions. Then, we compared all reduced models for all parameter sets using cluster analysis. The clusters revealed which of the reduced models that were similar to the original model in dynamics and variability. This allowed us to select the smallest reduced model that best approximated the full model. Through examples we showed that when parameter uncertainty was large, the model should be reduced further and when parameter uncertainty was small, models should not be reduced much.ConclusionsA method to compare different models under parameter uncertainty is developed. It can be applied to any model reduction method. We also showed that the amount of parameter uncertainty influences the choice of reduced models.
Highlights
The dynamics of biochemical networks can be modelled by systems of ordinary differential equations
The compound concentrations can be modelled by systems of ordinary differential equations (ODEs) and such dynamical models of biochemical networks may give biological insight that could not be obtained by modelling the compounds individually
We developed a new method to evaluate model reductions under parameter uncertainty based on the symmetric error measure in (4)
Summary
The dynamics of biochemical networks can be modelled by systems of ordinary differential equations. These networks are typically large and contain many parameters. There are many different model reduction procedures, the evaluation of reduced models is difficult and depends on the parameter values of the full model. Modelling of biochemical networks Biochemical networks consist of chemical reactions between compounds, such as enzymes and metabolites Through these reactions, the various compounds are consumed and produced. The compound concentrations can be modelled by systems of ordinary differential equations (ODEs) and such dynamical models of biochemical networks may give biological insight that could not be obtained by modelling the compounds individually.
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