Limits...
The genome-scale metabolic model iIN800 of Saccharomyces cerevisiae and its validation: a scaffold to query lipid metabolism.

Nookaew I, Jewett MC, Meechai A, Thammarongtham C, Laoteng K, Cheevadhanarak S, Nielsen J, Bhumiratana S - BMC Syst Biol (2008)

Bottom Line: Predictions of both growth capability and large scale in silico single gene deletions by iIN800 were consistent with experimental data.In addition, 13C-labeling experiments validated the new biomass equations and calculated intracellular fluxes.To demonstrate the applicability of iIN800, we show that the model can be used as a scaffold to reveal the regulatory importance of lipid metabolism precursors and intermediates that would have been missed in previous models from transcriptome datasets.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Chemical Engineering, Faculty of Engineering, King Mongkut's University of Technology Thonburi, Bangkok 10140, Thailand. intawat.in@gmail.com

ABSTRACT

Background: Up to now, there have been three published versions of a yeast genome-scale metabolic model: iFF708, iND750 and iLL672. All three models, however, lack a detailed description of lipid metabolism and thus are unable to be used as integrated scaffolds for gaining insights into lipid metabolism from multilevel omic measurement technologies (e.g. genome-wide mRNA levels). To overcome this limitation, we reconstructed a new version of the Saccharomyces cerevisiae genome-scale model, iIN800 that includes a more rigorous and detailed description of lipid metabolism.

Results: The reconstructed metabolic model comprises 1446 reactions and 1013 metabolites. Beyond incorporating new reactions involved in lipid metabolism, we also present new biomass equations that improve the predictive power of flux balance analysis simulations. Predictions of both growth capability and large scale in silico single gene deletions by iIN800 were consistent with experimental data. In addition, 13C-labeling experiments validated the new biomass equations and calculated intracellular fluxes. To demonstrate the applicability of iIN800, we show that the model can be used as a scaffold to reveal the regulatory importance of lipid metabolism precursors and intermediates that would have been missed in previous models from transcriptome datasets.

Conclusion: Performing integrated analyses using iIN800 as a network scaffold is shown to be a valuable tool for elucidating the behavior of complex metabolic networks, particularly for identifying regulatory targets in lipid metabolism that can be used for industrial applications or for understanding lipid disease states.

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Comparison demonstrating in silico and in vivo growth rates at various cultivation conditions.In silico predictions were performed using FBA with iIN800. Experimental measurements were taken from the literature (see text for references).
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Figure 3: Comparison demonstrating in silico and in vivo growth rates at various cultivation conditions.In silico predictions were performed using FBA with iIN800. Experimental measurements were taken from the literature (see text for references).

Mentions: To validate iIN800, we first investigated the model's ability to simulate aerobic and anaerobic growth in glucose- or ammonium-limited conditions. Several published chemostat datasets were used as experimental references. As shown in Figure 3, the results from the computational growth prediction agreed with experimental measurements. Less than 10% relative error was observed (Figure 3). The details of the simulations and the corresponding reference data are given in Additional file 3. Intracellular fluxes can be easily visualized using the ReMapper software and our model (Additional files 4 and 5).


The genome-scale metabolic model iIN800 of Saccharomyces cerevisiae and its validation: a scaffold to query lipid metabolism.

Nookaew I, Jewett MC, Meechai A, Thammarongtham C, Laoteng K, Cheevadhanarak S, Nielsen J, Bhumiratana S - BMC Syst Biol (2008)

Comparison demonstrating in silico and in vivo growth rates at various cultivation conditions.In silico predictions were performed using FBA with iIN800. Experimental measurements were taken from the literature (see text for references).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2542360&req=5

Figure 3: Comparison demonstrating in silico and in vivo growth rates at various cultivation conditions.In silico predictions were performed using FBA with iIN800. Experimental measurements were taken from the literature (see text for references).
Mentions: To validate iIN800, we first investigated the model's ability to simulate aerobic and anaerobic growth in glucose- or ammonium-limited conditions. Several published chemostat datasets were used as experimental references. As shown in Figure 3, the results from the computational growth prediction agreed with experimental measurements. Less than 10% relative error was observed (Figure 3). The details of the simulations and the corresponding reference data are given in Additional file 3. Intracellular fluxes can be easily visualized using the ReMapper software and our model (Additional files 4 and 5).

Bottom Line: Predictions of both growth capability and large scale in silico single gene deletions by iIN800 were consistent with experimental data.In addition, 13C-labeling experiments validated the new biomass equations and calculated intracellular fluxes.To demonstrate the applicability of iIN800, we show that the model can be used as a scaffold to reveal the regulatory importance of lipid metabolism precursors and intermediates that would have been missed in previous models from transcriptome datasets.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Chemical Engineering, Faculty of Engineering, King Mongkut's University of Technology Thonburi, Bangkok 10140, Thailand. intawat.in@gmail.com

ABSTRACT

Background: Up to now, there have been three published versions of a yeast genome-scale metabolic model: iFF708, iND750 and iLL672. All three models, however, lack a detailed description of lipid metabolism and thus are unable to be used as integrated scaffolds for gaining insights into lipid metabolism from multilevel omic measurement technologies (e.g. genome-wide mRNA levels). To overcome this limitation, we reconstructed a new version of the Saccharomyces cerevisiae genome-scale model, iIN800 that includes a more rigorous and detailed description of lipid metabolism.

Results: The reconstructed metabolic model comprises 1446 reactions and 1013 metabolites. Beyond incorporating new reactions involved in lipid metabolism, we also present new biomass equations that improve the predictive power of flux balance analysis simulations. Predictions of both growth capability and large scale in silico single gene deletions by iIN800 were consistent with experimental data. In addition, 13C-labeling experiments validated the new biomass equations and calculated intracellular fluxes. To demonstrate the applicability of iIN800, we show that the model can be used as a scaffold to reveal the regulatory importance of lipid metabolism precursors and intermediates that would have been missed in previous models from transcriptome datasets.

Conclusion: Performing integrated analyses using iIN800 as a network scaffold is shown to be a valuable tool for elucidating the behavior of complex metabolic networks, particularly for identifying regulatory targets in lipid metabolism that can be used for industrial applications or for understanding lipid disease states.

Show MeSH