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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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A comparison of skeletal muscle models.This heat map displays the level of difference in each pair of models. Darker squares represent models that are more similar to each other than lighter squares. A black square (as on the diagonal) indicates identical models, and a white square indicates the most different pair of models. The three darker blocks that surround the main diagonal are the comparisons of samples within each dataset to each other. These darker blocks show that the models within each dataset tend to be more similar to each other than to models from other datasets. The models from a particular expression array type also appear to be more similar to each other than to models from a different array types, but the data available do not allow us to show that this is actually true, as is shown in Figure 11.
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pcbi-1000082-g010: A comparison of skeletal muscle models.This heat map displays the level of difference in each pair of models. Darker squares represent models that are more similar to each other than lighter squares. A black square (as on the diagonal) indicates identical models, and a white square indicates the most different pair of models. The three darker blocks that surround the main diagonal are the comparisons of samples within each dataset to each other. These darker blocks show that the models within each dataset tend to be more similar to each other than to models from other datasets. The models from a particular expression array type also appear to be more similar to each other than to models from a different array types, but the data available do not allow us to show that this is actually true, as is shown in Figure 11.

Mentions: Each of the 42 (6+12+24) gene expression datasets was used with the GIMME algorithm to create a model that produces ATP at no less than half the optimal efficiency and matches the data as closely as possible. These models were compared on a pairwise basis by finding the number of reactions that are different in the two models under comparison. On average, two models differ by 340 reactions, which is approximately 10% of the reactions in the global model. The pairwise distances are shown graphically in Figure 10. Darker squares represent pairs of networks that are more similar than lighter squares. Two trends are immediately apparent. First, the metabolic networks that are derived from each dataset are more similar to others derived from that same dataset, as shown by the three large dark squares that surround the diagonal. Secondly, it appears that the GI and FO models are more similar to each other than to the GB models. Initially, we suspected that the gene chip might bias this result, so we recomputed the distance between each pair of models, ignoring the 179 reactions that are not present on the U133A array. This result is graphically depicted in Figure 11, showing that the FO models are similar to both the GB and GI models, but the GI models are not similar to the GB models. Comparing models generated with different gene expression platforms must be done with caution. The bottom line is that there are 179 metabolic reactions that the GB models could elect not to use based on data, but the GI and FO models cannot because no data is present; the GIMME procedure will only turn off a reaction in the presence of some data mapped to that reaction. Better coverage of metabolic reactions on gene expression arrays will lead to fewer extraneous reactions in resulting models.


Context-specific metabolic networks are consistent with experiments.

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

A comparison of skeletal muscle models.This heat map displays the level of difference in each pair of models. Darker squares represent models that are more similar to each other than lighter squares. A black square (as on the diagonal) indicates identical models, and a white square indicates the most different pair of models. The three darker blocks that surround the main diagonal are the comparisons of samples within each dataset to each other. These darker blocks show that the models within each dataset tend to be more similar to each other than to models from other datasets. The models from a particular expression array type also appear to be more similar to each other than to models from a different array types, but the data available do not allow us to show that this is actually true, as is shown in Figure 11.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi-1000082-g010: A comparison of skeletal muscle models.This heat map displays the level of difference in each pair of models. Darker squares represent models that are more similar to each other than lighter squares. A black square (as on the diagonal) indicates identical models, and a white square indicates the most different pair of models. The three darker blocks that surround the main diagonal are the comparisons of samples within each dataset to each other. These darker blocks show that the models within each dataset tend to be more similar to each other than to models from other datasets. The models from a particular expression array type also appear to be more similar to each other than to models from a different array types, but the data available do not allow us to show that this is actually true, as is shown in Figure 11.
Mentions: Each of the 42 (6+12+24) gene expression datasets was used with the GIMME algorithm to create a model that produces ATP at no less than half the optimal efficiency and matches the data as closely as possible. These models were compared on a pairwise basis by finding the number of reactions that are different in the two models under comparison. On average, two models differ by 340 reactions, which is approximately 10% of the reactions in the global model. The pairwise distances are shown graphically in Figure 10. Darker squares represent pairs of networks that are more similar than lighter squares. Two trends are immediately apparent. First, the metabolic networks that are derived from each dataset are more similar to others derived from that same dataset, as shown by the three large dark squares that surround the diagonal. Secondly, it appears that the GI and FO models are more similar to each other than to the GB models. Initially, we suspected that the gene chip might bias this result, so we recomputed the distance between each pair of models, ignoring the 179 reactions that are not present on the U133A array. This result is graphically depicted in Figure 11, showing that the FO models are similar to both the GB and GI models, but the GI models are not similar to the GB models. Comparing models generated with different gene expression platforms must be done with caution. The bottom line is that there are 179 metabolic reactions that the GB models could elect not to use based on data, but the GI and FO models cannot because no data is present; the GIMME procedure will only turn off a reaction in the presence of some data mapped to that reaction. Better coverage of metabolic reactions on gene expression arrays will lead to fewer extraneous reactions in resulting models.

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