Predicting outcomes of steady-state ¹³C isotope tracing experiments using Monte Carlo sampling.

TitlePredicting outcomes of steady-state ¹³C isotope tracing experiments using Monte Carlo sampling.
Publication TypeJournal Article
Year of Publication2012
AuthorsSchellenberger J, Zielinski DC, Choi W, Madireddi S, Portnoy V, Scott DA, Reed JL, Osterman AL, Palsson B
JournalBMC Syst Biol
PubMed Date2012-2-1
KeywordsAlgorithms, Carbon Isotopes, Escherichia coli, Kinetics, Metabolic Networks and Pathways, Models, Biological, Monte Carlo Method, Systems Biology

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.

Alternate JournalBMC Syst Biol
PubMed ID22289253



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