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Filling kinetic gaps: dynamic modeling of metabolism where detailed kinetic information is lacking.

Resendis-Antonio O - PLoS ONE (2009)

Bottom Line: Even though the theoretical foundation for modeling metabolic network has been extensively treated elsewhere, the lack of kinetic information has limited the analysis in most of the cases.Furthermore, robust properties in time scale and metabolite organization were identify and one concluded that they are a consequence of the combined performance of redundancies and variability in metabolite participation.For instances, I envisage that this approach can be useful in genomic medicine or pharmacogenomics, where the estimation of time scales and the identification of metabolite organization may be crucial to characterize and identify (dis)functional stages.

View Article: PubMed Central - PubMed

Affiliation: Center for Genomic Sciences-UNAM, Cuernavaca Morelos, Mexico. resendis@ccg.unam.mx

ABSTRACT

Background: Integrative analysis between dynamical modeling of metabolic networks and data obtained from high throughput technology represents a worthy effort toward a holistic understanding of the link among phenotype and dynamical response. Even though the theoretical foundation for modeling metabolic network has been extensively treated elsewhere, the lack of kinetic information has limited the analysis in most of the cases. To overcome this constraint, we present and illustrate a new statistical approach that has two purposes: integrate high throughput data and survey the general dynamical mechanisms emerging for a slightly perturbed metabolic network.

Methodology/principal findings: This paper presents a statistic framework capable to study how and how fast the metabolites participating in a perturbed metabolic network reach a steady-state. Instead of requiring accurate kinetic information, this approach uses high throughput metabolome technology to define a feasible kinetic library, which constitutes the base for identifying, statistical and dynamical properties during the relaxation. For the sake of illustration we have applied this approach to the human Red blood cell metabolism (hRBC) and its capacity to predict temporal phenomena was evaluated. Remarkable, the main dynamical properties obtained from a detailed kinetic model in hRBC were recovered by our statistical approach. Furthermore, robust properties in time scale and metabolite organization were identify and one concluded that they are a consequence of the combined performance of redundancies and variability in metabolite participation.

Conclusions/significance: In this work we present an approach that integrates high throughput metabolome data to define the dynamic behavior of a slightly perturbed metabolic network where kinetic information is lacking. Having information of metabolite concentrations at steady-state, this method has significant relevance due its potential scope to analyze others genome scale metabolic reconstructions. Thus, I expect this approach will significantly contribute to explore the relationship between dynamic and physiology in other metabolic reconstructions, particularly those whose kinetic information is practically s. For instances, I envisage that this approach can be useful in genomic medicine or pharmacogenomics, where the estimation of time scales and the identification of metabolite organization may be crucial to characterize and identify (dis)functional stages.

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Related in: MedlinePlus

Redundancy in modal library.Figure (A) and (B) show a hierarchical cluster applied for the 6nd and 32nd th-metabolic pool library respectively. The presence (absence) of metabolites along the library is immediately observed by green/red (black) regions. From this Figure, metabolites with a redundant participation along the pools are identified. The degree of participation for each metabolite in each i-th metabolic pool library is depicted in (C). Green squares show the metabolites that participate at least 80% over the entire th-metabolic pool library.
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pone-0004967-g005: Redundancy in modal library.Figure (A) and (B) show a hierarchical cluster applied for the 6nd and 32nd th-metabolic pool library respectively. The presence (absence) of metabolites along the library is immediately observed by green/red (black) regions. From this Figure, metabolites with a redundant participation along the pools are identified. The degree of participation for each metabolite in each i-th metabolic pool library is depicted in (C). Green squares show the metabolites that participate at least 80% over the entire th-metabolic pool library.

Mentions: Having performed this classification, we studied the metabolic composition for each i-th metabolic pool library by applying a hierarchical cluster analysis, whose visual representation reveals the degree of metabolite participation, redundancy and (dis)similarily between them. For instance, Figure 5A and 5B show the cluster analysis accomplished for the 6th and 32nd metabolic pool library respectively. In both cases, this graphical representation allows to identify conservative patterns through the space. Thus, for the 6th metabolic pool library, we note that sedoheptulose 7-phosphate (r7p) participates over the pool formation in most of the situations. The inverse situation occurs with D-glucose 6-phosphate (g6p), fructose-6-phosphate (f6p), Fructose-1,6-diphosphate (fdp), dihydroxyacetone phosphate (dhap) and glyceraldehyde 3-phosphate (g3p) whose participation in metabolic pools would appear to be a function of kinetic parameters. Similarly, pyruvate (pyr), 2 phosphoglycerate (2pg) and phosphoenolpyruvate (pep) represent the predominant metabolites participating in the 32nd metabolic pool library.


Filling kinetic gaps: dynamic modeling of metabolism where detailed kinetic information is lacking.

Resendis-Antonio O - PLoS ONE (2009)

Redundancy in modal library.Figure (A) and (B) show a hierarchical cluster applied for the 6nd and 32nd th-metabolic pool library respectively. The presence (absence) of metabolites along the library is immediately observed by green/red (black) regions. From this Figure, metabolites with a redundant participation along the pools are identified. The degree of participation for each metabolite in each i-th metabolic pool library is depicted in (C). Green squares show the metabolites that participate at least 80% over the entire th-metabolic pool library.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2654918&req=5

pone-0004967-g005: Redundancy in modal library.Figure (A) and (B) show a hierarchical cluster applied for the 6nd and 32nd th-metabolic pool library respectively. The presence (absence) of metabolites along the library is immediately observed by green/red (black) regions. From this Figure, metabolites with a redundant participation along the pools are identified. The degree of participation for each metabolite in each i-th metabolic pool library is depicted in (C). Green squares show the metabolites that participate at least 80% over the entire th-metabolic pool library.
Mentions: Having performed this classification, we studied the metabolic composition for each i-th metabolic pool library by applying a hierarchical cluster analysis, whose visual representation reveals the degree of metabolite participation, redundancy and (dis)similarily between them. For instance, Figure 5A and 5B show the cluster analysis accomplished for the 6th and 32nd metabolic pool library respectively. In both cases, this graphical representation allows to identify conservative patterns through the space. Thus, for the 6th metabolic pool library, we note that sedoheptulose 7-phosphate (r7p) participates over the pool formation in most of the situations. The inverse situation occurs with D-glucose 6-phosphate (g6p), fructose-6-phosphate (f6p), Fructose-1,6-diphosphate (fdp), dihydroxyacetone phosphate (dhap) and glyceraldehyde 3-phosphate (g3p) whose participation in metabolic pools would appear to be a function of kinetic parameters. Similarly, pyruvate (pyr), 2 phosphoglycerate (2pg) and phosphoenolpyruvate (pep) represent the predominant metabolites participating in the 32nd metabolic pool library.

Bottom Line: Even though the theoretical foundation for modeling metabolic network has been extensively treated elsewhere, the lack of kinetic information has limited the analysis in most of the cases.Furthermore, robust properties in time scale and metabolite organization were identify and one concluded that they are a consequence of the combined performance of redundancies and variability in metabolite participation.For instances, I envisage that this approach can be useful in genomic medicine or pharmacogenomics, where the estimation of time scales and the identification of metabolite organization may be crucial to characterize and identify (dis)functional stages.

View Article: PubMed Central - PubMed

Affiliation: Center for Genomic Sciences-UNAM, Cuernavaca Morelos, Mexico. resendis@ccg.unam.mx

ABSTRACT

Background: Integrative analysis between dynamical modeling of metabolic networks and data obtained from high throughput technology represents a worthy effort toward a holistic understanding of the link among phenotype and dynamical response. Even though the theoretical foundation for modeling metabolic network has been extensively treated elsewhere, the lack of kinetic information has limited the analysis in most of the cases. To overcome this constraint, we present and illustrate a new statistical approach that has two purposes: integrate high throughput data and survey the general dynamical mechanisms emerging for a slightly perturbed metabolic network.

Methodology/principal findings: This paper presents a statistic framework capable to study how and how fast the metabolites participating in a perturbed metabolic network reach a steady-state. Instead of requiring accurate kinetic information, this approach uses high throughput metabolome technology to define a feasible kinetic library, which constitutes the base for identifying, statistical and dynamical properties during the relaxation. For the sake of illustration we have applied this approach to the human Red blood cell metabolism (hRBC) and its capacity to predict temporal phenomena was evaluated. Remarkable, the main dynamical properties obtained from a detailed kinetic model in hRBC were recovered by our statistical approach. Furthermore, robust properties in time scale and metabolite organization were identify and one concluded that they are a consequence of the combined performance of redundancies and variability in metabolite participation.

Conclusions/significance: In this work we present an approach that integrates high throughput metabolome data to define the dynamic behavior of a slightly perturbed metabolic network where kinetic information is lacking. Having information of metabolite concentrations at steady-state, this method has significant relevance due its potential scope to analyze others genome scale metabolic reconstructions. Thus, I expect this approach will significantly contribute to explore the relationship between dynamic and physiology in other metabolic reconstructions, particularly those whose kinetic information is practically s. For instances, I envisage that this approach can be useful in genomic medicine or pharmacogenomics, where the estimation of time scales and the identification of metabolite organization may be crucial to characterize and identify (dis)functional stages.

Show MeSH
Related in: MedlinePlus