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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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General overview.A) Based on metabolome data and reconstructed metabolic network, we obtain the feasible set of kinetic parameters, k-cone. A point in this space represents a vector whose dimension is given by the number of reactions in the metabolic network. B) To explore the relationship between physiology and dynamic behavior for a perturbed metabolic network where kinetic parameters is lacking, we construct a Jacobian library taking into account the k-cone space. A point in Jacobian library represents a square matrix with dimension determined by the number of metabolites. C) In turn, for each Jacobian we obtain a modal matrix, we called this new space the Modal library. D) In order to analyze the variability for each of the modes along the ensemble we define i-th metabolic pool library, a subspace of the entire modal library. E) We calculate the average properties, the dispersion for the time scales and the metabolic pools generated along the library. F) The statistical analysis of the time scales and the metabolic organization are interrelated to infer metabolites with potential physiological meaning.
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pone-0004967-g001: General overview.A) Based on metabolome data and reconstructed metabolic network, we obtain the feasible set of kinetic parameters, k-cone. A point in this space represents a vector whose dimension is given by the number of reactions in the metabolic network. B) To explore the relationship between physiology and dynamic behavior for a perturbed metabolic network where kinetic parameters is lacking, we construct a Jacobian library taking into account the k-cone space. A point in Jacobian library represents a square matrix with dimension determined by the number of metabolites. C) In turn, for each Jacobian we obtain a modal matrix, we called this new space the Modal library. D) In order to analyze the variability for each of the modes along the ensemble we define i-th metabolic pool library, a subspace of the entire modal library. E) We calculate the average properties, the dispersion for the time scales and the metabolic pools generated along the library. F) The statistical analysis of the time scales and the metabolic organization are interrelated to infer metabolites with potential physiological meaning.

Mentions: In this work I suggest a statistical framework to analyze dynamical properties of a metabolic network when its metabolite concentrations are slightly perturbed around a steady-state. To overcome the lack of kinetic parameters, this approach uses high throughput metabolome data for obtaining a kinetic library conformed by all the kinetic parameters which dynamically ensure the existence of a steady-state solution. Subsequently, through this kinetic space, one constructs a library of dynamical models, all of them characterized by the same metabolic network but predicting dynamic behavior with different kinetic parameters. As this paper suggests, a statistical analysis applied over the library of dynamical models allows us to survey general properties even in the absence of accurate kinetic information. The library of dynamical models constitutes a fundamental space required to explore two immediately questions: how and how fast a metabolic network reaches its steady-state after a slightly external perturbation has occurred. The workflow of the method is such that it integrates three main components: metabolome data [26], [27], the stoichiometric matrix (holding the metabolic biochemical reactions in the organism) and the classical theory of modal analysis [28]. A schematic overview of the approach is depicted in Figure 1.


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

Resendis-Antonio O - PLoS ONE (2009)

General overview.A) Based on metabolome data and reconstructed metabolic network, we obtain the feasible set of kinetic parameters, k-cone. A point in this space represents a vector whose dimension is given by the number of reactions in the metabolic network. B) To explore the relationship between physiology and dynamic behavior for a perturbed metabolic network where kinetic parameters is lacking, we construct a Jacobian library taking into account the k-cone space. A point in Jacobian library represents a square matrix with dimension determined by the number of metabolites. C) In turn, for each Jacobian we obtain a modal matrix, we called this new space the Modal library. D) In order to analyze the variability for each of the modes along the ensemble we define i-th metabolic pool library, a subspace of the entire modal library. E) We calculate the average properties, the dispersion for the time scales and the metabolic pools generated along the library. F) The statistical analysis of the time scales and the metabolic organization are interrelated to infer metabolites with potential physiological meaning.
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC2654918&req=5

pone-0004967-g001: General overview.A) Based on metabolome data and reconstructed metabolic network, we obtain the feasible set of kinetic parameters, k-cone. A point in this space represents a vector whose dimension is given by the number of reactions in the metabolic network. B) To explore the relationship between physiology and dynamic behavior for a perturbed metabolic network where kinetic parameters is lacking, we construct a Jacobian library taking into account the k-cone space. A point in Jacobian library represents a square matrix with dimension determined by the number of metabolites. C) In turn, for each Jacobian we obtain a modal matrix, we called this new space the Modal library. D) In order to analyze the variability for each of the modes along the ensemble we define i-th metabolic pool library, a subspace of the entire modal library. E) We calculate the average properties, the dispersion for the time scales and the metabolic pools generated along the library. F) The statistical analysis of the time scales and the metabolic organization are interrelated to infer metabolites with potential physiological meaning.
Mentions: In this work I suggest a statistical framework to analyze dynamical properties of a metabolic network when its metabolite concentrations are slightly perturbed around a steady-state. To overcome the lack of kinetic parameters, this approach uses high throughput metabolome data for obtaining a kinetic library conformed by all the kinetic parameters which dynamically ensure the existence of a steady-state solution. Subsequently, through this kinetic space, one constructs a library of dynamical models, all of them characterized by the same metabolic network but predicting dynamic behavior with different kinetic parameters. As this paper suggests, a statistical analysis applied over the library of dynamical models allows us to survey general properties even in the absence of accurate kinetic information. The library of dynamical models constitutes a fundamental space required to explore two immediately questions: how and how fast a metabolic network reaches its steady-state after a slightly external perturbation has occurred. The workflow of the method is such that it integrates three main components: metabolome data [26], [27], the stoichiometric matrix (holding the metabolic biochemical reactions in the organism) and the classical theory of modal analysis [28]. A schematic overview of the approach is depicted in Figure 1.

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