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 availabl

SUBMITTER: Schellenberger J 

PROVIDER: S-EPMC3323462 | biostudies-literature | 2012 Jan

REPOSITORIES: biostudies-literature