@article{BulikGrimbsHuthmacheretal.2009,
  author    = {Bulik, Sascha and Grimbs, Sergio and Huthmacher, Carola and Selbig, Joachim and Holzhutter, Hermann G.},
  title     = {Kinetic hybrid models composed of mechanistic and simplified enzymatic rate laws : a promising method for speeding up the kinetic modelling of complex metabolic networks},
  issn      = {1742-464X},
  doi       = {10.1111/j.1742-4658.2008.06784.x},
  year      = {2009},
  abstract  = {Kinetic modelling of complex metabolic networks - a central goal of computational systems biology - is currently hampered by the lack of reliable rate equations for the majority of the underlying biochemical reactions and membrane transporters. On the basis of biochemically substantiated evidence that metabolic control is exerted by a narrow set of key regulatory enzymes, we propose here a hybrid modelling approach in which only the central regulatory enzymes are described by detailed mechanistic rate equations, and the majority of enzymes are approximated by simplified (nonmechanistic) rate equations (e.g. mass action, LinLog, Michaelis-Menten and power law) capturing only a few basic kinetic features and hence containing only a small number of parameters to be experimentally determined. To check the reliability of this approach, we have applied it to two different metabolic networks, the energy and redox metabolism of red blood cells, and the purine metabolism of hepatocytes, using in both cases available comprehensive mechanistic models as reference standards. Identification of the central regulatory enzymes was performed by employing only information on network topology and the metabolic data for a single reference state of the network [Grimbs S, Selbig J, Bulik S, Holzhutter HG \& Steuer R (2007) Mol Syst Biol3, 146, doi:10.1038/msb4100186]. Calculations of stationary and temporary states under various physiological challenges demonstrate the good performance of the hybrid models. We propose the hybrid modelling approach as a means to speed up the development of reliable kinetic models for complex metabolic networks.},
  language  = {en}
}