Reconstruction and validation of Saccharomyces cerevisiae iND750, a fully compartmentalized genome-scale metabolic model.

By Natalie C Duarte, Markus J. Herrgård, Bernhard O. Palsson. Genome Research, 2004

699 citations recorded by the source corpus.

A fully compartmentalized genome-scale metabolic model of Saccharomyces cerevisiae that accounts for 750 genes and their associated transcripts, proteins, and reactions has been reconstructed and validated. All of the 1149 reactions included in this in silico model are both elementally and charge balanced and have been assigned to one of eight cellular locations (extracellular space, cytosol, mitochondrion, peroxisome, nucleus, endoplasmic reticulum, Golgi apparatus, or vacuole). When in silico predictions of 4154 growth phenotypes were compared to two published large-scale gene deletion studies, an 83% agreement was found between iND750's predictions and the experimental studies. Analysis of the failure modes showed that false predictions were primarily caused by iND750's limited inclusion of cellular processes outside of metabolism. This study systematically identified inconsistencies in our knowledge of yeast metabolism that require specific further experimental investigation.

Subfields: Biomedical Engineering, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/e68872993b11df6d36dcb3dcd9336f26a2adba9b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

In Molecular Biology

Citations in the corpus

Graph theory and networks in Biology. (2006, background): “cerevisiae in [131] to computationally predict the impact of gene deletions on the organism.”
Genome-scale models of microbial cells: evaluating the consequences of constraints (2004, background): “Knockout studies have been performed in E.coli 49,51,76,89–91 , S.cerevisiae 47,92,93 , H.pylori 43 , H.influenzae 45 , and M. extorquens 77 with an accuracy rate of around 60–90%.”
The Edinburgh human metabolic network reconstruction and its functional analysis (2007, background): “Even for simple microorganisms such as Escherichia coli and Saccharomyces cerevisiae , the high-quality metabolic networks reconstruction often take years to ﬁnish (Reed et al , 2003; Duarte et al , 2004).”
BiGG: a Biochemical Genetic and Genomic knowledgebase of large scale metabolic reconstructions (2010, background): “…genome-scale reconstructions of six organisms (see Additional File 1): Homo sapiens Recon 1 [33], Escherichia coli i JR 904 [34] and i AF1260 [35], Saccharomyces cerevisiae i ND750 [36], Staphylococcus aureus i SB619 [37], Methanosarcina barkeri i AF692 [38] and Helicobacter pylori i IT341 [39].”
A consensus yeast metabolic network reconstruction obtained from a community approach to systems biology (2008, neutral): “net/inchi" metaid="M_172_inchi"> InChI=1/C10H16N5O13P3/c11-8-5-9(13-2-12-8)15(3-14-5)10-7(17)6 (16)4(26-10)1-25-30(21,22)28-31(23,24)27-29(18,19)20/h2-4,6-7,10,16-17H,1H2, (H,21,22)(H,23,24)(H2,11,12,13)(H2,18,19,20)/t4-,6-,7-,10-/m1/s1/f/h18-19,21 ,23H,11H2 </in:inchi> <rdf:RDF xmlns:rdf="http://ww”
Modelling and analysis of gene regulatory networks (2008, methodology): “The method has been successfully used to model large metabolic networks covering the near-complete metabolism of several specie”
An optimization framework for identifying reaction activation/inhibition or elimination candidates for overproduction in microbial systems. (2006, background): “Metabolism is described by adopting genome-scale metabolic models widely available for many organisms (Schilling et al., 2002; Van Dien and Lidstrom, 2002; Forster et al., 2003; Reed et al., 2003; Duarte et al., 2004).”
Genome-scale models of bacterial metabolism: reconstruction and applications (2008, background): “Interpretation of inconsistent cases by expert examination led to several annotation and model reﬁnements, some of which were supported by the results of targeted experiments (Covert et al ., 2004; Duarte et al ., 2004; Joyce et al ., 2006; Reed et al ., 2006b).”
Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0 (2007, positive): “In spite of their limitations, the predictive power of genome-scale models of metabolic networks has been demonstrated in diverse situations through careful experimentation (see for example refs”
Flux balance analysis of biological systems: applications and challenges (2009, methodology): “Constraint-based analyses of reconstructed metabolic networks have proved to be quite effective in various applications such as metabolic engineering [7–9], prediction of outcomes of gene deletions [10], drug-target identification, as reported in [11] and in the elucidation of cellular regulatory ne”