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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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Dispersion in modal matrix.(A) Dispersion modal matrix shows those metabolites whose participation is robust to changes in kinetic parameter. In green-yellow we denote the metabolites whose dispersion was less than 1. B) Coefficients of variation obtained for some ratio of metabolites. As Figure shows, metabolites with robust participation in metabolic pools can be potentially identified at slow time scales. As in (A), these appear mainly at slow time scales.
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pone-0004967-g004: Dispersion in modal matrix.(A) Dispersion modal matrix shows those metabolites whose participation is robust to changes in kinetic parameter. In green-yellow we denote the metabolites whose dispersion was less than 1. B) Coefficients of variation obtained for some ratio of metabolites. As Figure shows, metabolites with robust participation in metabolic pools can be potentially identified at slow time scales. As in (A), these appear mainly at slow time scales.

Mentions: On the other hand, Figures 4A shows the resulting N matrix. As previous interpretation, i-th row represents the dispersion of the metabolic pool formed at the i-th time scales and the columns quantify the dispersion of each metabolite. Figure 4A highlights in green-yellow the metabolites whose dispersion was lower than the unit, they assigned as potential metabolites that invariantly participates in metabolic pools independent of the used kinetic parameters. In addition, a statistical analysis accomplished for rates between some of these reveals the presence of additional robust properties, see Figure 4B. Based in these results one concludes that the organization of some metabolites to responds under changes in kinetic parameters tends to be robust at slow time scales.


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

Resendis-Antonio O - PLoS ONE (2009)

Dispersion in modal matrix.(A) Dispersion modal matrix shows those metabolites whose participation is robust to changes in kinetic parameter. In green-yellow we denote the metabolites whose dispersion was less than 1. B) Coefficients of variation obtained for some ratio of metabolites. As Figure shows, metabolites with robust participation in metabolic pools can be potentially identified at slow time scales. As in (A), these appear mainly at slow time scales.
© Copyright Policy
Related In: Results  -  Collection

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

pone-0004967-g004: Dispersion in modal matrix.(A) Dispersion modal matrix shows those metabolites whose participation is robust to changes in kinetic parameter. In green-yellow we denote the metabolites whose dispersion was less than 1. B) Coefficients of variation obtained for some ratio of metabolites. As Figure shows, metabolites with robust participation in metabolic pools can be potentially identified at slow time scales. As in (A), these appear mainly at slow time scales.
Mentions: On the other hand, Figures 4A shows the resulting N matrix. As previous interpretation, i-th row represents the dispersion of the metabolic pool formed at the i-th time scales and the columns quantify the dispersion of each metabolite. Figure 4A highlights in green-yellow the metabolites whose dispersion was lower than the unit, they assigned as potential metabolites that invariantly participates in metabolic pools independent of the used kinetic parameters. In addition, a statistical analysis accomplished for rates between some of these reveals the presence of additional robust properties, see Figure 4B. Based in these results one concludes that the organization of some metabolites to responds under changes in kinetic parameters tends to be robust at slow time scales.

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