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Automatic network coupling analysis for dynamical systems based on detailed kinetic models.

Dirk Lebiedz·Julia Kammerer·Ulrich Brandt-Pollmann
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Abstract

We introduce a numerical complexity reduction method for the automatic identification and analysis of dynamic network decompositions in (bio)chemical kinetics based on error-controlled computation of a minimal model dimension represented by the number of (locally) active dynamical modes. Our algorithm exploits a generalized sensitivity analysis along state trajectories and subsequent singular value decomposition of sensitivity matrices for the identification of these dominant dynamical modes. It allows for a dynamic coupling analysis of (bio)chemical species in kinetic models that can be exploited for the piecewise computation of a minimal model on small time intervals and offers valuable functional insight into highly nonlinear reaction mechanisms and network dynamics. We present results for the identification of network decompositions in a simple oscillatory chemical reaction, time scale separation based model reduction in a Michaelis-Menten enzyme system and network decomposition of a detailed model for the oscillatory peroxidase-oxidase enzyme system.

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AlgorithmsAnimalsArtificial IntelligenceCell Physiological PhenomenaComputer SimulationGene Expression RegulationHumansKineticsModels, BiologicalMultienzyme ComplexesSignal TransductionJournal ArticleResearch Support, Non-U.S. Gov't

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Dirk Lebiedz, Julia Kammerer, Ulrich Brandt-Pollmann. 2005-10-12. Automatic network coupling analysis for dynamical systems based on detailed kinetic models.. https://doi.org/10.1103/physreve.72.041911

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