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Context-specific metabolic networks are consistent with experiments.

Becker SA, Palsson BO - PLoS Comput. Biol. (2008)

Bottom Line: Furthermore, the algorithm provides a quantitative inconsistency score indicating how consistent a set of gene expression data is with a particular metabolic objective.We show that this algorithm produces results consistent with biological experiments and intuition for adaptive evolution of bacteria, rational design of metabolic engineering strains, and human skeletal muscle cells.This work represents progress towards producing constraint-based models of metabolism that are specific to the conditions where the expression profiling data is available.

View Article: PubMed Central - PubMed

Affiliation: Department of Bioengineering, University of California San Diego, La Jolla, California, United States of America.

ABSTRACT
Reconstructions of cellular metabolism are publicly available for a variety of different microorganisms and some mammalian genomes. To date, these reconstructions are "genome-scale" and strive to include all reactions implied by the genome annotation, as well as those with direct experimental evidence. Clearly, many of the reactions in a genome-scale reconstruction will not be active under particular conditions or in a particular cell type. Methods to tailor these comprehensive genome-scale reconstructions into context-specific networks will aid predictive in silico modeling for a particular situation. We present a method called Gene Inactivity Moderated by Metabolism and Expression (GIMME) to achieve this goal. The GIMME algorithm uses quantitative gene expression data and one or more presupposed metabolic objectives to produce the context-specific reconstruction that is most consistent with the available data. Furthermore, the algorithm provides a quantitative inconsistency score indicating how consistent a set of gene expression data is with a particular metabolic objective. We show that this algorithm produces results consistent with biological experiments and intuition for adaptive evolution of bacteria, rational design of metabolic engineering strains, and human skeletal muscle cells. This work represents progress towards producing constraint-based models of metabolism that are specific to the conditions where the expression profiling data is available.

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The mapping of Affymetrix gene chip data to reactions.Reactions in the white area have no usable gene chip data on either platform. Reactions in grey have usable data only on the 133+ 2.0 platform. Reactions in black have usable data for both the 133+ 2.0 and the 133A platform. Importantly, 5% (179) of the reactions are only represented on the 133+ 2.0 chip, potentially increasing scores across chips. The average difference score is 340, so a difference of 179 reactions is greater than a 50% impact.
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pcbi-1000082-g009: The mapping of Affymetrix gene chip data to reactions.Reactions in the white area have no usable gene chip data on either platform. Reactions in grey have usable data only on the 133+ 2.0 platform. Reactions in black have usable data for both the 133+ 2.0 and the 133A platform. Importantly, 5% (179) of the reactions are only represented on the 133+ 2.0 chip, potentially increasing scores across chips. The average difference score is 340, so a difference of 179 reactions is greater than a 50% impact.

Mentions: These three datasets were originally gathered for purposes completely distinct from creating context-specific metabolic networks, just as the E. coli datasets described earlier were. Nevertheless, they can be interpreted in the context of a genome-scale metabolic network towards this end. All three datasets were collected using Affymetrix (Santa Clara, CA) gene expression arrays. The GB dataset used U133+ 2.0 arrays, while the GI and FO datasets used U133A arrays. While the arrays are similar, the U133+ 2.0 array is able to provide reliable trancriptomic data for 179 reactions beyond what the U133A array can provide. The coverage of these arrays in terms of model reactions is shown in Figure 9. Each probeset that corresponded to a metabolic gene was mapped to that gene, provided that the annotation information for that particular array type indicated that the probeset sequence was unique to either that gene or a closely related gene. Probesets with sequences that correspond to multiple, unrelated genes were ignored. The values associated with the expression of genes were mapped to reactions through the gene-protein-reaction associations, as described earlier and in Materials and Methods.


Context-specific metabolic networks are consistent with experiments.

Becker SA, Palsson BO - PLoS Comput. Biol. (2008)

The mapping of Affymetrix gene chip data to reactions.Reactions in the white area have no usable gene chip data on either platform. Reactions in grey have usable data only on the 133+ 2.0 platform. Reactions in black have usable data for both the 133+ 2.0 and the 133A platform. Importantly, 5% (179) of the reactions are only represented on the 133+ 2.0 chip, potentially increasing scores across chips. The average difference score is 340, so a difference of 179 reactions is greater than a 50% impact.
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC2366062&req=5

pcbi-1000082-g009: The mapping of Affymetrix gene chip data to reactions.Reactions in the white area have no usable gene chip data on either platform. Reactions in grey have usable data only on the 133+ 2.0 platform. Reactions in black have usable data for both the 133+ 2.0 and the 133A platform. Importantly, 5% (179) of the reactions are only represented on the 133+ 2.0 chip, potentially increasing scores across chips. The average difference score is 340, so a difference of 179 reactions is greater than a 50% impact.
Mentions: These three datasets were originally gathered for purposes completely distinct from creating context-specific metabolic networks, just as the E. coli datasets described earlier were. Nevertheless, they can be interpreted in the context of a genome-scale metabolic network towards this end. All three datasets were collected using Affymetrix (Santa Clara, CA) gene expression arrays. The GB dataset used U133+ 2.0 arrays, while the GI and FO datasets used U133A arrays. While the arrays are similar, the U133+ 2.0 array is able to provide reliable trancriptomic data for 179 reactions beyond what the U133A array can provide. The coverage of these arrays in terms of model reactions is shown in Figure 9. Each probeset that corresponded to a metabolic gene was mapped to that gene, provided that the annotation information for that particular array type indicated that the probeset sequence was unique to either that gene or a closely related gene. Probesets with sequences that correspond to multiple, unrelated genes were ignored. The values associated with the expression of genes were mapped to reactions through the gene-protein-reaction associations, as described earlier and in Materials and Methods.

Bottom Line: Furthermore, the algorithm provides a quantitative inconsistency score indicating how consistent a set of gene expression data is with a particular metabolic objective.We show that this algorithm produces results consistent with biological experiments and intuition for adaptive evolution of bacteria, rational design of metabolic engineering strains, and human skeletal muscle cells.This work represents progress towards producing constraint-based models of metabolism that are specific to the conditions where the expression profiling data is available.

View Article: PubMed Central - PubMed

Affiliation: Department of Bioengineering, University of California San Diego, La Jolla, California, United States of America.

ABSTRACT
Reconstructions of cellular metabolism are publicly available for a variety of different microorganisms and some mammalian genomes. To date, these reconstructions are "genome-scale" and strive to include all reactions implied by the genome annotation, as well as those with direct experimental evidence. Clearly, many of the reactions in a genome-scale reconstruction will not be active under particular conditions or in a particular cell type. Methods to tailor these comprehensive genome-scale reconstructions into context-specific networks will aid predictive in silico modeling for a particular situation. We present a method called Gene Inactivity Moderated by Metabolism and Expression (GIMME) to achieve this goal. The GIMME algorithm uses quantitative gene expression data and one or more presupposed metabolic objectives to produce the context-specific reconstruction that is most consistent with the available data. Furthermore, the algorithm provides a quantitative inconsistency score indicating how consistent a set of gene expression data is with a particular metabolic objective. We show that this algorithm produces results consistent with biological experiments and intuition for adaptive evolution of bacteria, rational design of metabolic engineering strains, and human skeletal muscle cells. This work represents progress towards producing constraint-based models of metabolism that are specific to the conditions where the expression profiling data is available.

Show MeSH