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

Jan Schellenberger, Daniel C. Zielinski, Wing Choi, Sunthosh Madireddi, Vasiliy Portnoy, David A. Scott, Jennifer L. Reed, Andrei L. Osterman, Bernhard T. Palsson*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

23 Citations (Scopus)

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.

Original languageEnglish
Article number9
JournalBMC Systems Biology
Volume6
DOIs
Publication statusPublished - 30 Jan 2012

Bibliographical note

Funding Information:
This work was supported by National Institutes of Health grants GM068837, GM057089, and GM062791.

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