title: Predicting outcomes of steady-state ¹³C isotope tracing experiments using Monte Carlo sampling.
abstract: BACKGROUND
  Carbon-13 (13C) analysis is a commonly used method for estimating reaction rates in biochemical networks. The choice of carbon labeling pattern is an important consideration when designing these experiments. We present a novel Monte Carlo algorithm for finding the optimal substrate input label for a particular experimental objective (flux or flux ratio). Unlike previous work, this method does not require assumption of the flux distribution beforehand.
  
  
  RESULTS
  Using a large E. coli isotopomer model, different commercially available substrate labeling patterns were tested computationally for their ability to determine reaction fluxes. The choice of optimal labeled substrate was found to be dependent upon the desired experimental objective. Many commercially available labels are predicted to be outperformed by complex labeling patterns. Based on Monte Carlo Sampling, the dimensionality of experimental data was found to be considerably less than anticipated, suggesting that effectiveness of 13C experiments for determining reaction fluxes across a large-scale metabolic network is less than previously believed.
  
  
  CONCLUSIONS
  While 13C analysis is a useful tool in systems biology, high redundancy in measurements limits the information that can be obtained from each experiment. It is however possible to compute potential limitations before an experiment is run and predict whether, and to what degree, the rate of each reaction can be resolved.
authors.affiliation: Bioinformatics and Systems Biology Program, University of California - San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0419, USA.
authors.forename: Jan
authors.initials: J
authors.lastname: Schellenberger
authors.forename: Daniel C
authors.initials: DC
authors.lastname: Zielinski
authors.forename: Wing
authors.initials: W
authors.lastname: Choi
authors.forename: Sunthosh
authors.initials: S
authors.lastname: Madireddi
authors.forename: Vasiliy
authors.initials: V
authors.lastname: Portnoy
authors.forename: David A
authors.initials: DA
authors.lastname: Scott
authors.forename: Jennifer L
authors.initials: JL
authors.lastname: Reed
authors.forename: Andrei L
authors.initials: AL
authors.lastname: Osterman
authors.forename: Bernhard
authors.initials: B
authors.lastname: Palsson
mesh_terms: D000465:Algorithms; D002247:Carbon Isotopes / Q000378:metabolism*; D004926:Escherichia coli; D007700:Kinetics; D053858:Metabolic Networks and Pathways / Q000502:physiology*; D008954:Models, Biological; D009010:Monte Carlo Method; D049490:Systems Biology / Q000379:methods*
chemical_list: D002247:Carbon Isotopes