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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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Pairwise comparisons of consistency for anaerobic conditions.A graphical representation of the log2 transform of the difference between inconsistency scores. A green box indicates that the sample on the y-axis is more consistent with anaerobic growth than the sample on the x-axis. Red boxes indicate the opposite. Differences that do not meet p<0.05 are left blank. The shade of red or green quantifies the log2 of the difference in inconsistency scores. The position of green and red blocks shows that in nearly all cases that are statistically significant, gene expression data for strains grown without oxygen is more consistent with efficient anaerobic growth than strains grown with oxygen.
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pcbi-1000082-g007: Pairwise comparisons of consistency for anaerobic conditions.A graphical representation of the log2 transform of the difference between inconsistency scores. A green box indicates that the sample on the y-axis is more consistent with anaerobic growth than the sample on the x-axis. Red boxes indicate the opposite. Differences that do not meet p<0.05 are left blank. The shade of red or green quantifies the log2 of the difference in inconsistency scores. The position of green and red blocks shows that in nearly all cases that are statistically significant, gene expression data for strains grown without oxygen is more consistent with efficient anaerobic growth than strains grown with oxygen.

Mentions: The growth of E. coli varies depending on the availability of terminal electron acceptors, usually oxygen or nitrate. Gene expression data from a total of 21 different strain/electron acceptor conditions was analyzed to construct the most consistent models for growth with oxygen, without oxygen, and with nitrate. The expectation is that the strain data taken from a given condition (for example, aerobic) should be more consistent with growth on that condition (again, aerobic) than strain data from a different condition. Pairwise comparisons were made between all consistency scores, and the results are shown in Figures 6, 7, and 8, for aerobic, anaerobic, and anaerobic nitrate conditions, respectively. A green box indicates that the strain/condition indicated on the y axis is more consistent with growth than the strain on the x-axis; a red box indicates the opposite. The intensity of the green or red color indicates the difference in inconsistency scores, after log2 transformation for visualization scaling. Black boxes indicate that no statistically significant (p<0.05) conclusion could be reached from the data. In all cases where statistically significant conclusions were possible, gene expression in strains grown with oxygen is more consistent with aerobic growth than gene expression from strains grown without (Figure 6). In 99% of cases that are statistically significant, gene expression in strains grown anaerobically is more consistent with anaerobic growth than the data from strains grown with oxygen (Figure 7). The trend holds 90% of the time for anaerobic growth with nitrate (Figure 8). As would be expected, different subsets of reactions are active for each condition. Taken with the previous results, this provides strong support for the inconsistency scores that emerge from the algorithm and provides a positive control.


Context-specific metabolic networks are consistent with experiments.

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

Pairwise comparisons of consistency for anaerobic conditions.A graphical representation of the log2 transform of the difference between inconsistency scores. A green box indicates that the sample on the y-axis is more consistent with anaerobic growth than the sample on the x-axis. Red boxes indicate the opposite. Differences that do not meet p<0.05 are left blank. The shade of red or green quantifies the log2 of the difference in inconsistency scores. The position of green and red blocks shows that in nearly all cases that are statistically significant, gene expression data for strains grown without oxygen is more consistent with efficient anaerobic growth than strains grown with oxygen.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi-1000082-g007: Pairwise comparisons of consistency for anaerobic conditions.A graphical representation of the log2 transform of the difference between inconsistency scores. A green box indicates that the sample on the y-axis is more consistent with anaerobic growth than the sample on the x-axis. Red boxes indicate the opposite. Differences that do not meet p<0.05 are left blank. The shade of red or green quantifies the log2 of the difference in inconsistency scores. The position of green and red blocks shows that in nearly all cases that are statistically significant, gene expression data for strains grown without oxygen is more consistent with efficient anaerobic growth than strains grown with oxygen.
Mentions: The growth of E. coli varies depending on the availability of terminal electron acceptors, usually oxygen or nitrate. Gene expression data from a total of 21 different strain/electron acceptor conditions was analyzed to construct the most consistent models for growth with oxygen, without oxygen, and with nitrate. The expectation is that the strain data taken from a given condition (for example, aerobic) should be more consistent with growth on that condition (again, aerobic) than strain data from a different condition. Pairwise comparisons were made between all consistency scores, and the results are shown in Figures 6, 7, and 8, for aerobic, anaerobic, and anaerobic nitrate conditions, respectively. A green box indicates that the strain/condition indicated on the y axis is more consistent with growth than the strain on the x-axis; a red box indicates the opposite. The intensity of the green or red color indicates the difference in inconsistency scores, after log2 transformation for visualization scaling. Black boxes indicate that no statistically significant (p<0.05) conclusion could be reached from the data. In all cases where statistically significant conclusions were possible, gene expression in strains grown with oxygen is more consistent with aerobic growth than gene expression from strains grown without (Figure 6). In 99% of cases that are statistically significant, gene expression in strains grown anaerobically is more consistent with anaerobic growth than the data from strains grown with oxygen (Figure 7). The trend holds 90% of the time for anaerobic growth with nitrate (Figure 8). As would be expected, different subsets of reactions are active for each condition. Taken with the previous results, this provides strong support for the inconsistency scores that emerge from the algorithm and provides a positive control.

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