Automatic network coupling analysis for dynamical systems based on detailed kinetic models.
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.
Lebiedz
Dirk
D
Interdisciplinary Center for Scientific Computing, Im Neuenheimer Feld 368, D-69120 Heidelberg, Germany. lebiedz@iwr.uni-heidelberg.de
Kammerer
Julia
J
Brandt-Pollmann
Ulrich
U
D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D002468:Cell Physiological Phenomena; D003198:Computer Simulation; D005786:Gene Expression Regulation / Q000502:physiology*; D006801:Humans; D007700:Kinetics; D008954:Models, Biological; D009097:Multienzyme Complexes / Q000378:metabolism*; D015398:Signal Transduction / Q000502:physiology*
D009097:Multienzyme Complexes