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Logo of bmcsysbioBioMed Centralsearchsubmit a manuscriptregisterthis articleBMC Systems Biology
 
BMC Syst Biol. 2012; 6: 16.
Published online Mar 12, 2012. doi:  10.1186/1752-0509-6-16
PMCID: PMC3361480
Dynamic regulatory on/off minimization for biological systems under internal temporal perturbations
Sabrina Kleessencorresponding author1 and Zoran Nikoloski1,2
1Max-Planck Institute of Molecular Plant Physiology, 14476 Potsdam, Germany
2Institute of Biochemistry and Biology, University of Potsdam, 14476 Potsdam, Germany
corresponding authorCorresponding author.
Sabrina Kleessen: kleessen/at/mpimp-golm.mpg.de; Zoran Nikoloski: nikoloski/at/mpimp-golm.mpg.de
Received July 15, 2011; Accepted March 12, 2012.
Abstract
Background
Flux balance analysis (FBA) together with its extension, dynamic FBA, have proven instrumental for analyzing the robustness and dynamics of metabolic networks by employing only the stoichiometry of the included reactions coupled with adequately chosen objective function. In addition, under the assumption of minimization of metabolic adjustment, dynamic FBA has recently been employed to analyze the transition between metabolic states.
Results
Here, we propose a suite of novel methods for analyzing the dynamics of (internally perturbed) metabolic networks and for quantifying their robustness with limited knowledge of kinetic parameters. Following the biochemically meaningful premise that metabolite concentrations exhibit smooth temporal changes, the proposed methods rely on minimizing the significant fluctuations of metabolic profiles to predict the time-resolved metabolic state, characterized by both fluxes and concentrations. By conducting a comparative analysis with a kinetic model of the Calvin-Benson cycle and a model of plant carbohydrate metabolism, we demonstrate that the principle of regulatory on/off minimization coupled with dynamic FBA can accurately predict the changes in metabolic states.
Conclusions
Our methods outperform the existing dynamic FBA-based modeling alternatives, and could help in revealing the mechanisms for maintaining robustness of dynamic processes in metabolic networks over time.
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