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Open Access Highly Accessed Research article

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

Jan Schellenberger1, Daniel C Zielinski2, Wing Choi2, Sunthosh Madireddi2, Vasiliy Portnoy2, David A Scott3, Jennifer L Reed4, Andrei L Osterman3 and Bernhard ∅ Palsson2*

Author affiliations

1 Bioinformatics and Systems Biology Program, University of California - San Diego, 9500 Gilman Drive, La Jolla, CA, 92093-0419 USA

2 Department of Bioengineering, University of California - San Diego, 9500 Gilman Drive, La Jolla, CA, 92093-0412 USA

3 Bioinformatics and Systems Biology Program, Sanford-Burnham Institute, 10901 North Torrey Pines Road, La Jolla, CA, 92037 USA

4 Department of Chemical and Biological Engineering, University of Wisconsin Madison, 1415 Engineering Drive, Madison, WI, 53706-1607 USA

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Citation and License

BMC Systems Biology 2012, 6:9  doi:10.1186/1752-0509-6-9

Published: 30 January 2012

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.