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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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Metabolic engineering strain consistency score.The normalized consistency score for an E. coli strain designed to produce lactate indicate that the Δpta ΔadhE strain has a metabolic gene expression state consistent with the simultaneous production of lactate and growth when compared with the wild-type. This higher normalized consistency score indicates that the gene expression data from the double deletion strain is more consistent with the metabolic engineering objective than the wild-type strain, in accordance with experimental measurements.
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pcbi-1000082-g005: Metabolic engineering strain consistency score.The normalized consistency score for an E. coli strain designed to produce lactate indicate that the Δpta ΔadhE strain has a metabolic gene expression state consistent with the simultaneous production of lactate and growth when compared with the wild-type. This higher normalized consistency score indicates that the gene expression data from the double deletion strain is more consistent with the metabolic engineering objective than the wild-type strain, in accordance with experimental measurements.

Mentions: Metabolic engineering seeks to optimize bacterial strains to produce a valuable product from a less expensive set of molecules. Rational design of strains for metabolic engineering is possible with genome-scale metabolic models [20]. Adaptive evolution of knock-out strains of E. coli can be used to optimize such strains [21]. We determined the inconsistency scores with GIMME for replicates of a Δpta ΔadhE strain that is designed to produce lactate as a byproduct of anaerobic growth on glucose, as described in [21], as well as wild-type strains. The objective used was growth and lactate production was fixed at a rate consistent with experimental data from [21]. As shown in Figure 5, the designed strain has gene expression data that is more consistent with growth-coupled lactate production, exactly as experimental data indicates. The gene deletions and subsequent evolution have led to a global metabolic gene expression state that is more consistent with growth-coupled lactate production than the wild-type strain.


Context-specific metabolic networks are consistent with experiments.

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

Metabolic engineering strain consistency score.The normalized consistency score for an E. coli strain designed to produce lactate indicate that the Δpta ΔadhE strain has a metabolic gene expression state consistent with the simultaneous production of lactate and growth when compared with the wild-type. This higher normalized consistency score indicates that the gene expression data from the double deletion strain is more consistent with the metabolic engineering objective than the wild-type strain, in accordance with experimental measurements.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi-1000082-g005: Metabolic engineering strain consistency score.The normalized consistency score for an E. coli strain designed to produce lactate indicate that the Δpta ΔadhE strain has a metabolic gene expression state consistent with the simultaneous production of lactate and growth when compared with the wild-type. This higher normalized consistency score indicates that the gene expression data from the double deletion strain is more consistent with the metabolic engineering objective than the wild-type strain, in accordance with experimental measurements.
Mentions: Metabolic engineering seeks to optimize bacterial strains to produce a valuable product from a less expensive set of molecules. Rational design of strains for metabolic engineering is possible with genome-scale metabolic models [20]. Adaptive evolution of knock-out strains of E. coli can be used to optimize such strains [21]. We determined the inconsistency scores with GIMME for replicates of a Δpta ΔadhE strain that is designed to produce lactate as a byproduct of anaerobic growth on glucose, as described in [21], as well as wild-type strains. The objective used was growth and lactate production was fixed at a rate consistent with experimental data from [21]. As shown in Figure 5, the designed strain has gene expression data that is more consistent with growth-coupled lactate production, exactly as experimental data indicates. The gene deletions and subsequent evolution have led to a global metabolic gene expression state that is more consistent with growth-coupled lactate production than the wild-type strain.

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