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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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Related in: MedlinePlus

Lactate-evolved strain consistency scores.This figure demonstrates the same result as Figure 3, but with strains evolved on lactate. The normalized consistency scores for growth on each of the tested carbon sources are higher for evolved strains, indicating that the gene expression data from the evolved strains are more consistent with efficient growth on each carbon source.
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pcbi-1000082-g004: Lactate-evolved strain consistency scores.This figure demonstrates the same result as Figure 3, but with strains evolved on lactate. The normalized consistency scores for growth on each of the tested carbon sources are higher for evolved strains, indicating that the gene expression data from the evolved strains are more consistent with efficient growth on each carbon source.

Mentions: The gene expression data used to construct the models consists of CEL files containing the data described in [17], normalized using GCRMA [14]. The data was mapped from genes to reactions using the gene-protein-reaction associations from the reconstruction [18]. The threshold (xcutoff) was set at 12, meaning that reactions assigned a normalized value greater than 12 are assumed to be present; similar results were noted at other thresholds. The RMF was growth on a given carbon source, and the context-specific metabolic networks were forced to grow no less than 90% of optimal growth. Because the evolved strains nearly always grow better than wild-type strains on a variety of carbon sources [19], metabolic networks for optimal growth on nine carbon sources were constructed. The results are shown in Figure 3 (glycerol evolved strains) and Figure 4 (lactate evolved strains). For these figures, the inconsistency scores were used to calculate normalized consistency scores (see Materials and Methods for details); a higher normalized consistency score indicates that the gene expression profile is more consistent with the objective. The figures show that the gene expression state of evolved strains are always more consistent with growth on the nine substrates, paralleling the phenotypic findings from [19] in nearly all cases. These findings demonstrate that the evolved strains have gene expression states that are more consistent (than wild type strains) with usage of the optimal networks for growth on a variety of carbon sources.


Context-specific metabolic networks are consistent with experiments.

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

Lactate-evolved strain consistency scores.This figure demonstrates the same result as Figure 3, but with strains evolved on lactate. The normalized consistency scores for growth on each of the tested carbon sources are higher for evolved strains, indicating that the gene expression data from the evolved strains are more consistent with efficient growth on each carbon source.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2366062&req=5

pcbi-1000082-g004: Lactate-evolved strain consistency scores.This figure demonstrates the same result as Figure 3, but with strains evolved on lactate. The normalized consistency scores for growth on each of the tested carbon sources are higher for evolved strains, indicating that the gene expression data from the evolved strains are more consistent with efficient growth on each carbon source.
Mentions: The gene expression data used to construct the models consists of CEL files containing the data described in [17], normalized using GCRMA [14]. The data was mapped from genes to reactions using the gene-protein-reaction associations from the reconstruction [18]. The threshold (xcutoff) was set at 12, meaning that reactions assigned a normalized value greater than 12 are assumed to be present; similar results were noted at other thresholds. The RMF was growth on a given carbon source, and the context-specific metabolic networks were forced to grow no less than 90% of optimal growth. Because the evolved strains nearly always grow better than wild-type strains on a variety of carbon sources [19], metabolic networks for optimal growth on nine carbon sources were constructed. The results are shown in Figure 3 (glycerol evolved strains) and Figure 4 (lactate evolved strains). For these figures, the inconsistency scores were used to calculate normalized consistency scores (see Materials and Methods for details); a higher normalized consistency score indicates that the gene expression profile is more consistent with the objective. The figures show that the gene expression state of evolved strains are always more consistent with growth on the nine substrates, paralleling the phenotypic findings from [19] in nearly all cases. These findings demonstrate that the evolved strains have gene expression states that are more consistent (than wild type strains) with usage of the optimal networks for growth on a variety of carbon sources.

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
Related in: MedlinePlus