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Mathematically, MFA models are traditionally formulated using separate state variables for reaction fluxes and isotopomer abundances. Analysis of isotope labeling experiments using this set of variables results in a non-convex optimization problem that suffers from both implementation complexity and convergence problems.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>This article addresses the mathematical and computational formulation of <jats:sup>13<\/jats:sup>C MFA models using a new set of variables referred to as <jats:italic>fluxomers<\/jats:italic>. These composite variables combine both fluxes and isotopomer abundances, which results in a simply-posed formulation and an improved error model that is insensitive to isotopomer measurement normalization. A powerful fluxomer iterative algorithm (FIA) is developed and applied to solve the MFA optimization problem. For moderate-sized networks, the algorithm is shown to outperform the commonly used 13CFLUX cumomer-based algorithm and the more recently introduced OpenFLUX software that relies upon an elementary metabolite unit (EMU) network decomposition, both in terms of convergence time and output variability.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusions<\/jats:title>\n            <jats:p>Substantial improvements in convergence time and statistical quality of results can be achieved by applying fluxomer variables and the FIA algorithm to compute best-fit solutions to MFA models. We expect that the fluxomer formulation will provide a more suitable basis for future algorithms that analyze very large scale networks and design optimal isotope labeling experiments.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1186\/1752-0509-5-129","type":"journal-article","created":{"date-parts":[[2011,8,16]],"date-time":"2011-08-16T18:34:57Z","timestamp":1313519697000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":40,"title":["Fluxomers: a new approach for 13C metabolic flux analysis"],"prefix":"10.1186","volume":"5","author":[{"given":"Orr","family":"Srour","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jamey D","family":"Young","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yonina C","family":"Eldar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2011,8,16]]},"reference":[{"key":"743_CR1","volume-title":"Molecular Biology of the Cell, Fourth Edition Garland","author":"B Alberts","year":"2008","unstructured":"Alberts B, Johnson A, Lewis J, Raff M, Roberts K, Walter P: Molecular Biology of the Cell, Fourth Edition Garland. 2008, http:\/\/www.amazon.com\/exec\/obidos\/redirect?tag=citeulike07-20&path=ASIN\/0815332181"},{"issue":"2","key":"743_CR2","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1093\/bib\/bbl009","volume":"7","author":"R Steuer","year":"2006","unstructured":"Steuer R: Review: On the analysis and interpretation of correlations in metabolomic data. 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