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The integration of available genomic and metabolomic data through the generation of genome scale metabolic models has enabled the development of computational models that predict the behaviour of organisms under specific conditions and present a route to metabolic engineering.
Generation and curation of plant genome-scale metabolic models has proven far more challenging, not the least of which is our incomplete knowledge of compartmentation and organelle transporters in plants.
Since the first large-scale reconstruction of the Saccharomyces cerevisiae metabolic network 15 years ago the development of yeast metabolic models has progressed rapidly, resulting in no less than nine different yeast genome-scale metabolic models.
The use of transcriptional profiles in combination with metabolic models has previously been used to identify signature pathways in yeast [4] and in human tissues [5].
Constraint-based analysis of genome-scale metabolic models has become a key methodology to gain insights into functions, capabilities, and properties of cellular metabolism.
Using synthetic data generated from metabolic models has been adopted widely in literature as a way of testing algorithms in a controlled context [ 20].
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Nevertheless, based on the EBPR transformations and general bacterial metabolism, metabolic models have been derived to describe the energetic and substrate requirements.
In contrast to the extensive interest devoted towards bacterial and eukaryotic metabolism reconstruction, efforts to construct archaeal metabolic models have been noticeably limited [ 11, 12].
Recently, human metabolic models have been used to study cancer metabolism (9) and predict potential drug targets and biomarkers (10).
These metabolic models have been extensively used to elucidate the organizational principles of yeast metabolism and drive yeast strain engineering strategies for targeted overproductions.
Furthermore, its genome has been very well characterized and genome scale metabolic models have been reconstructed (Goffeau 2000; Nookaew et al. 2008).
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