Limits...
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.

Show MeSH
The computation of inconsistency scores.Inconsistency scores for each reaction are computed by multiplying the deviation from a threshold by the required flux through a reaction. In the example here, the green reactions have data above the threshold, set to 12 (this is a parameter; see text). The red reactions have data below the threshold (11.4 and 8.2). The calculation of the inconsistency score corresponding to each reaction is shown numerically as flux multiplied by the deviation from the cutoff. They each increase the inconsistency score, implying that the data are less consistent with the objective of growing on lactate. Greater required fluxes and greater deviation from the threshold both increase the inconsistency scores. The total inconsistency score is the sum of all individual reaction scores.
© Copyright Policy
Related In: Results  -  Collection


getmorefigures.php?uid=PMC2366062&req=5

pcbi-1000082-g002: The computation of inconsistency scores.Inconsistency scores for each reaction are computed by multiplying the deviation from a threshold by the required flux through a reaction. In the example here, the green reactions have data above the threshold, set to 12 (this is a parameter; see text). The red reactions have data below the threshold (11.4 and 8.2). The calculation of the inconsistency score corresponding to each reaction is shown numerically as flux multiplied by the deviation from the cutoff. They each increase the inconsistency score, implying that the data are less consistent with the objective of growing on lactate. Greater required fluxes and greater deviation from the threshold both increase the inconsistency scores. The total inconsistency score is the sum of all individual reaction scores.

Mentions: Simply speaking, reactions that correspond to mRNA transcript levels below a specified threshold are tentatively declared inactive. If the cell cannot achieve the desired functionality without at least one of these reactions, linear optimization is used to find the most consistent set of reactions to reactivate. Inconsistency scores are calculated based on the product of distance from threshold and necessary flux for each reaction required to be reactivated, as illustrated in Figure 2. A smaller inconsistency score indicates that the data is more consistent with the RMF. The GIMME algorithm produces the network with the minimal inconsistency score through the following two-step procedure:


Context-specific metabolic networks are consistent with experiments.

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

The computation of inconsistency scores.Inconsistency scores for each reaction are computed by multiplying the deviation from a threshold by the required flux through a reaction. In the example here, the green reactions have data above the threshold, set to 12 (this is a parameter; see text). The red reactions have data below the threshold (11.4 and 8.2). The calculation of the inconsistency score corresponding to each reaction is shown numerically as flux multiplied by the deviation from the cutoff. They each increase the inconsistency score, implying that the data are less consistent with the objective of growing on lactate. Greater required fluxes and greater deviation from the threshold both increase the inconsistency scores. The total inconsistency score is the sum of all individual reaction scores.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi-1000082-g002: The computation of inconsistency scores.Inconsistency scores for each reaction are computed by multiplying the deviation from a threshold by the required flux through a reaction. In the example here, the green reactions have data above the threshold, set to 12 (this is a parameter; see text). The red reactions have data below the threshold (11.4 and 8.2). The calculation of the inconsistency score corresponding to each reaction is shown numerically as flux multiplied by the deviation from the cutoff. They each increase the inconsistency score, implying that the data are less consistent with the objective of growing on lactate. Greater required fluxes and greater deviation from the threshold both increase the inconsistency scores. The total inconsistency score is the sum of all individual reaction scores.
Mentions: Simply speaking, reactions that correspond to mRNA transcript levels below a specified threshold are tentatively declared inactive. If the cell cannot achieve the desired functionality without at least one of these reactions, linear optimization is used to find the most consistent set of reactions to reactivate. Inconsistency scores are calculated based on the product of distance from threshold and necessary flux for each reaction required to be reactivated, as illustrated in Figure 2. A smaller inconsistency score indicates that the data is more consistent with the RMF. The GIMME algorithm produces the network with the minimal inconsistency score through the following two-step procedure:

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