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Whilst this assumption may be valid in the case of microorganisms growing under certain conditions, it is likely invalid in general, and especially for multicellular organisms, where cellular objectives differ greatly both between and within cell types. Moreover, for the purposes of biotechnological applications, it is normally the flux to a specific metabolite or product that is of interest rather than the rate of production of biomass <jats:italic>per se<\/jats:italic>.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>An alternative objective function is presented, that is based upon maximising the correlation between experimentally measured absolute gene expression data and predicted internal reaction fluxes. Using quantitative transcriptomics data acquired from <jats:italic>Saccharomyces cerevisiae<\/jats:italic> cultures under two growth conditions, the method outperforms traditional approaches for predicting experimentally measured exometabolic flux that are reliant upon maximisation of the rate of biomass production.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusion<\/jats:title>\n            <jats:p>Due to its improved prediction of experimentally measured metabolic fluxes, and of its lack of a requirement for knowledge of the biomass composition of the organism under the conditions of interest, the approach is likely to be of rather general utility. The method has been shown to predict fluxes reliably in single cellular systems. Subsequent work will investigate the method\u2019s ability to generate condition- and tissue-specific flux predictions in multicellular organisms.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1186\/1752-0509-6-73","type":"journal-article","created":{"date-parts":[[2012,6,27]],"date-time":"2012-06-27T13:09:33Z","timestamp":1340802573000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":131,"title":["Improving metabolic flux predictions using absolute gene expression data"],"prefix":"10.1186","volume":"6","author":[{"given":"Dave","family":"Lee","sequence":"first","affiliation":[]},{"given":"Kieran","family":"Smallbone","sequence":"additional","affiliation":[]},{"given":"Warwick B","family":"Dunn","sequence":"additional","affiliation":[]},{"given":"Ettore","family":"Murabito","sequence":"additional","affiliation":[]},{"given":"Catherine L","family":"Winder","sequence":"additional","affiliation":[]},{"given":"Douglas B","family":"Kell","sequence":"additional","affiliation":[]},{"given":"Pedro","family":"Mendes","sequence":"additional","affiliation":[]},{"given":"Neil","family":"Swainston","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2012,6,19]]},"reference":[{"key":"943_CR1","doi-asserted-by":"publisher","first-page":"320","DOI":"10.1038\/msb.2009.77","volume":"5","author":"MA Oberhardt","year":"2009","unstructured":"Oberhardt MA, Palsson B\u00d8, Papin JA: Applications of genome-scale metabolic reconstructions. 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