title: Refined elasticity sampling for Monte Carlo-based identification of stabilizing network patterns.
abstract: BACKGROUND
  Structural kinetic modelling (SKM) is a framework to analyse whether a metabolic steady state remains stable under perturbation, without requiring detailed knowledge about individual rate equations. It provides a representation of the system's Jacobian matrix that depends solely on the network structure, steady state measurements, and the elasticities at the steady state. For a measured steady state, stability criteria can be derived by generating a large number of SKMs with randomly sampled elasticities and evaluating the resulting Jacobian matrices. The elasticity space can be analysed statistically in order to detect network positions that contribute significantly to the perturbation response. Here, we extend this approach by examining the kinetic feasibility of the elasticity combinations created during Monte Carlo sampling.
  
  
  RESULTS
  Using a set of small example systems, we show that the majority of sampled SKMs would yield negative kinetic parameters if they were translated back into kinetic models. To overcome this problem, a simple criterion is formulated that mitigates such infeasible models. After evaluating the small example pathways, the methodology was used to study two steady states of the neuronal TCA cycle and the intrinsic mechanisms responsible for their stability or instability. The findings of the statistical elasticity analysis confirm that several elasticities are jointly coordinated to control stability and that the main source for potential instabilities are mutations in the enzyme alpha-ketoglutarate dehydrogenase.
authors.affiliation: Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany, Bioinformatics Group, University of Potsdam and Max-Planck Institute for Molecular Plant Physiology, Potsdam, Germany and Computational Systems Biology Group, School of Engineering and Science, Jacobs University Bremen, Bremen, Germany Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany, Bioinformatics Group, University of Potsdam and Max-Planck Institute for Molecular Plant Physiology, Potsdam, Germany and Computational Systems Biology Group, School of Engineering and Science, Jacobs University Bremen, Bremen, Germany.
authors.forename: Dorothee
authors.initials: D
authors.lastname: Childs
authors.affiliation: Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany, Bioinformatics Group, University of Potsdam and Max-Planck Institute for Molecular Plant Physiology, Potsdam, Germany and Computational Systems Biology Group, School of Engineering and Science, Jacobs University Bremen, Bremen, Germany.
authors.forename: Sergio
authors.initials: S
authors.lastname: Grimbs
authors.affiliation: Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany, Bioinformatics Group, University of Potsdam and Max-Planck Institute for Molecular Plant Physiology, Potsdam, Germany and Computational Systems Biology Group, School of Engineering and Science, Jacobs University Bremen, Bremen, Germany.
authors.forename: Joachim
authors.initials: J
authors.lastname: Selbig
mesh_terms: D002952:Citric Acid Cycle; D007700:Kinetics; D053858:Metabolic Networks and Pathways; D008954:Models, Biological; D009010:Monte Carlo Method