This article is part of the supplement: Problems and tools in the systems biology of the neuronal cell

Open Access Open Badges Review

Integration of enzyme kinetic models and isotopomer distribution analysis for studies of in situ cell operation

Vitaly A Selivanov12, Tatiana Sukhomlin3, Josep J Centelles1, Paul WN Lee4 and Marta Cascante12*

Author affiliations

1 Department of Biochemistry and Molecular Biology, Faculty of Chemistry, Marti i Franques, 1, 08028 Barcelona, Spain

2 CERQT-Parc Cientific de Barcelona, Barcelona, Spain

3 Institute of Theoretical and Experimental Biophysics, Pushchino, 142290, Russia

4 Department of Pediatrics, Harbor-UCLA Medical Center, Research and Education Institute, Torrance, CA 90502, USA

For all author emails, please log on.

Citation and License

BMC Neuroscience 2006, 7(Suppl 1):S7  doi:10.1186/1471-2202-7-S1-S7

Published: 30 October 2006


A current trend in neuroscience research is the use of stable isotope tracers in order to address metabolic processes in vivo. The tracers produce a huge number of metabolite forms that differ according to the number and position of labeled isotopes in the carbon skeleton (isotopomers) and such a large variety makes the analysis of isotopomer data highly complex. On the other hand, this multiplicity of forms does provide sufficient information to address cell operation in vivo. By the end of last millennium, a number of tools have been developed for estimation of metabolic flux profile from any possible isotopomer distribution data. However, although well elaborated, these tools were limited to steady state analysis, and the obtained set of fluxes remained disconnected from their biochemical context. In this review we focus on a new numerical analytical approach that integrates kinetic and metabolic flux analysis. The related computational algorithm estimates the dynamic flux based on the time-dependent distribution of all possible isotopomers of metabolic pathway intermediates that are generated from a labeled substrate. The new algorithm connects specific tracer data with enzyme kinetic characteristics, thereby extending the amount of data available for analysis: it uses enzyme kinetic data to estimate the flux profile, and vice versa, for the kinetic analysis it uses in vivo tracer data to reveal the biochemical basis of the estimated metabolic fluxes.