Bioinformatics Advance Access originally published online on July 15, 2004
Bioinformatics 2004 20(18):3387-3397; doi:10.1093/bioinformatics/bth412
Bioinformatics vol. 20 issue 18 © Oxford University Press 2004; all rights reserved.
An optimized algorithm for flux estimation from isotopomer distribution in glucose metabolites
1 Departamento de Bioquímica i Biologia Molecular, Facultat de Química and CERQT at Parc Científic de Barcelona, Barcelona, Catalunya, Spain, 2 Departamento de Bioquímica, Instituto de Investigaciones Biomédicas, Alberto Sols UAM/CSIC, Facultad de Medicina, Arzobispo Morcillo 4, 28029 Madrid, Spain and 3 Department of Pediatrics, Harbor-UCLA Medical Center, Research and Education Institute, Torrance, CA 90502, USA
Received on April 26, 2004; revised on June 21, 2004; accepted on July 7, 2004
Advance Access Publication July 15, 2004
Motivation: Analysis of the conversion of 13C glucose within the metabolic network allows the evaluation of the biochemical fluxes in interconnecting metabolic pathways. Such analyses require solving hundreds of equations with respect to individual isotopomer concentrations, and this assumes applying special software even for constructing the equations. The algorithm, proposed by others could be improved.
Method: A C-code linked to the program written in Mathematica (Wolfram Research Inc.), constructs and solves differential equations for all isotopomer concentrations, using the general enzyme characteristics (Km, equilibrium constant, etc.). This code uses innovative algorithm of determination for the isotopomers–products, thus essentially decreasing the computation time. Feasible metabolic fluxes are provided by the parameters of enzyme kinetics found from the data fitting.
Results: The software effectively evaluates metabolic fluxes based on the measured isotopomer distribution, as was illustrated by the analysis of glycolysis and pentose phosphate cycle. The mechanism of transketolase and transaldolase catalysis was shown to induce a specific kind of isotopomer re-distribution, which, despite the significance of its effect, usually is not taken into account.
Availability: The software could be freely downloaded from the site: http://bq.ub.es/bioqint/label_distribution/
Contact: martacascante{at}ub.edu
* To whom correspondence should be addressed.
CiteULike 
Connotea 
Del.icio.us What's this?
This article has been cited by other articles:
|
|
 |
|
  V. A. Selivanov, S. Marin, P. W. N. Lee, and M. Cascante Software for dynamic analysis of tracer-based metabolomic data: estimation of metabolic fluxes and their statistical analysis Bioinformatics, November 15, 2006; 22(22): 2806 - 2812. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
A. Rantanen, T. Mielikainen, J. Rousu, H. Maaheimo, and E. Ukkonen Planning optimal measurements of isotopomer distributions for estimation of metabolic fluxes Bioinformatics, May 15, 2006; 22(10): 1198 - 1206. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
V. A. Selivanov, L. E. Meshalkina, O. N. Solovjeva, P. W. Kuchel, A. Ramos-Montoya, G. A. Kochetov, P. W. N. Lee, and M. Cascante Rapid simulation and analysis of isotopomer distributions using constraints based on enzyme mechanisms: an example from HT29 cancer cells Bioinformatics, September 1, 2005; 21(17): 3558 - 3564. [Abstract] [Full Text] [PDF]
|
|