title: 13C labeling experiments at metabolic nonstationary conditions: an exploratory study.
abstract: BACKGROUND
  Stimulus Response Experiments to unravel the regulatory properties of metabolic networks are becoming more and more popular. However, their ability to determine enzyme kinetic parameters has proven to be limited with the presently available data. In metabolic flux analysis, the use of 13C labeled substrates together with isotopomer modeling solved the problem of underdetermined networks and increased the accuracy of flux estimations significantly.
  
  
  RESULTS
  In this contribution, the idea of increasing the information content of the dynamic experiment by adding 13C labeling is analyzed. For this purpose a small example network is studied by simulation and statistical methods. Different scenarios regarding available measurements are analyzed and compared to a non-labeled reference experiment. Sensitivity analysis revealed a specific influence of the kinetic parameters on the labeling measurements. Statistical methods based on parameter sensitivities and different measurement models are applied to assess the information gain of the labeled stimulus response experiment.
  
  
  CONCLUSIONS
  It was found that the use of a (specifically) labeled substrate will significantly increase the parameter estimation accuracy. An overall information gain of about a factor of six is observed for the example network. The information gain is achieved from the specific influence of the kinetic parameters towards the labeling measurements. This also leads to a significant decrease in correlation of the kinetic parameters compared to an experiment without 13C-labeled substrate.
authors: Bioprocess Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany. awahl@mpi-magdeburg.mpg.de
authors: Sebastian Aljoscha
authors: SA
authors: Wahl
authors: Katharina
authors: K
authors: Nöh
authors: Wolfgang
authors: W
authors: Wiechert
mesh_terms: D002247:Carbon Isotopes / Q000493:pharmacokinetics; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D004798:Enzymes / Q000378:metabolism; D007553:Isotope Labeling / Q000379:methods* / Q000706:statistics & numerical data; D007700:Kinetics; D053858:Metabolic Networks and Pathways / Q000502:physiology*; D008954:Models, Biological; D015203:Reproducibility of Results; D012107:Research Design / Q000706:statistics & numerical data; D012680:Sensitivity and Specificity
chemical_list: D002247:Carbon Isotopes; D004798:Enzymes