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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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Time scale and metabolic pools for human Red Blood Cell metabolism.Figure (A) shows the distribution of the eigenvalues (log10) obtained from the Jacobian library. Coefficient of variation (CV) calculated for the resulting eigenvalues distribution is depicted at the right side of the plot. Figure (B) depicts the average modal matrix. At slow times scales it is possible to identify metabolic pools that correlate with physiological functions [15]. The metabolites are denoted by: 13dpg ,1,3-Diphosphoglycerate; 23dpg, 2,3-Diphosphoglycerate; 2pg, 2 Phosphoglycerate; 3pg, 3-Phosphoglycerate; and, Adenine; adp, Adenosine diphosphate; amp, Adenosine monophosphate; atp, Adenosine triphosphate; dhap, Dihydroxyacetone phosphate; e4p, Erythrose-4-phosphate; f6p, Fructose-6-phosphate; fdp, Fructose-1,6-diphosphate; g3p, Glyceraldehyde 3-phosphate; g6p, D-glucose 6-phosphate; hxan, Hypoxanthine; imp, Inosine monophosphate; ins, Inosine; k, Potassium; lac-D, D-lactate; na1, Sodium; nadh, Nicotinamide adenine dinucleotide; pep, Phosphoenolpyruvate; prpp, 5-Phospho-alpha-D-ribose-1-diphosphate; pyr, Pyruvate; r1p, alpha-D-Ribose 1-phosphate; r5p, alpha-D-Ribose 5-phosphate; ru5p-D, D-Ribulose 5-phosphate; s7p, Sedoheptulose 7-phosphate; xu5p-D, D-Xylulose 5-phosphate.
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pone-0004967-g002: Time scale and metabolic pools for human Red Blood Cell metabolism.Figure (A) shows the distribution of the eigenvalues (log10) obtained from the Jacobian library. Coefficient of variation (CV) calculated for the resulting eigenvalues distribution is depicted at the right side of the plot. Figure (B) depicts the average modal matrix. At slow times scales it is possible to identify metabolic pools that correlate with physiological functions [15]. The metabolites are denoted by: 13dpg ,1,3-Diphosphoglycerate; 23dpg, 2,3-Diphosphoglycerate; 2pg, 2 Phosphoglycerate; 3pg, 3-Phosphoglycerate; and, Adenine; adp, Adenosine diphosphate; amp, Adenosine monophosphate; atp, Adenosine triphosphate; dhap, Dihydroxyacetone phosphate; e4p, Erythrose-4-phosphate; f6p, Fructose-6-phosphate; fdp, Fructose-1,6-diphosphate; g3p, Glyceraldehyde 3-phosphate; g6p, D-glucose 6-phosphate; hxan, Hypoxanthine; imp, Inosine monophosphate; ins, Inosine; k, Potassium; lac-D, D-lactate; na1, Sodium; nadh, Nicotinamide adenine dinucleotide; pep, Phosphoenolpyruvate; prpp, 5-Phospho-alpha-D-ribose-1-diphosphate; pyr, Pyruvate; r1p, alpha-D-Ribose 1-phosphate; r5p, alpha-D-Ribose 5-phosphate; ru5p-D, D-Ribulose 5-phosphate; s7p, Sedoheptulose 7-phosphate; xu5p-D, D-Xylulose 5-phosphate.

Mentions: To uncover the role that kinetic parameters have onto time scale profiles, a statistical analysis over the hierarchical time scale library was accomplished. Thus, proceeding as described in methods section (Equation 6), the average and the standard deviation for the inverse of each time scale obtained from Jacobian library were calculated and plotted in Figure 2A Two relevant results emerge from this result. Firstly, inverse of times scale reveals a low dispersion respect to the average, thus indicating the presence of robust statistical properties on kinetic parameters, see Figure 2A, left panel. Furthermore, repeating the same statistical analysis now directly over the profile of time scales, one obtain that robustness prevails for all time scales, except for the last three slowest ones, indicating its sensitivity to kinetic parameters, see Figure S1 in supporting information. Robustness is a property that has been reported in a variety of biological systems [34], [35]. In this paper, we present evidence to indicate that time scale associated with a perturbed metabolic networks is statistical robust to kinetic parameters for most time scales, and only in the case of slow time regimens they can have a considerable influence on dynamics. To evaluate the robustness of this result and heuristically evaluate the dispersion intrinsic of the network and the coming from sampling artifact, I have explored how the statistical properties of the eigenvalues and time scales vary at different k-cone sample sizes. Figure 3A shows the average and standard deviation (both in log10 scale) for the eigenvalues and time scales obtained when one select 4, 600 and 12586 points in k-cone space. Particularly, we note that in all cases the average and dispersion converge according the number of sample size increase. Notably, this approach allows to identify time scales that are closely similar to those obtained by using a detail kinetic model for hRBC [15], [18], see Figure 3B.


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

Resendis-Antonio O - PLoS ONE (2009)

Time scale and metabolic pools for human Red Blood Cell metabolism.Figure (A) shows the distribution of the eigenvalues (log10) obtained from the Jacobian library. Coefficient of variation (CV) calculated for the resulting eigenvalues distribution is depicted at the right side of the plot. Figure (B) depicts the average modal matrix. At slow times scales it is possible to identify metabolic pools that correlate with physiological functions [15]. The metabolites are denoted by: 13dpg ,1,3-Diphosphoglycerate; 23dpg, 2,3-Diphosphoglycerate; 2pg, 2 Phosphoglycerate; 3pg, 3-Phosphoglycerate; and, Adenine; adp, Adenosine diphosphate; amp, Adenosine monophosphate; atp, Adenosine triphosphate; dhap, Dihydroxyacetone phosphate; e4p, Erythrose-4-phosphate; f6p, Fructose-6-phosphate; fdp, Fructose-1,6-diphosphate; g3p, Glyceraldehyde 3-phosphate; g6p, D-glucose 6-phosphate; hxan, Hypoxanthine; imp, Inosine monophosphate; ins, Inosine; k, Potassium; lac-D, D-lactate; na1, Sodium; nadh, Nicotinamide adenine dinucleotide; pep, Phosphoenolpyruvate; prpp, 5-Phospho-alpha-D-ribose-1-diphosphate; pyr, Pyruvate; r1p, alpha-D-Ribose 1-phosphate; r5p, alpha-D-Ribose 5-phosphate; ru5p-D, D-Ribulose 5-phosphate; s7p, Sedoheptulose 7-phosphate; xu5p-D, D-Xylulose 5-phosphate.
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC2654918&req=5

pone-0004967-g002: Time scale and metabolic pools for human Red Blood Cell metabolism.Figure (A) shows the distribution of the eigenvalues (log10) obtained from the Jacobian library. Coefficient of variation (CV) calculated for the resulting eigenvalues distribution is depicted at the right side of the plot. Figure (B) depicts the average modal matrix. At slow times scales it is possible to identify metabolic pools that correlate with physiological functions [15]. The metabolites are denoted by: 13dpg ,1,3-Diphosphoglycerate; 23dpg, 2,3-Diphosphoglycerate; 2pg, 2 Phosphoglycerate; 3pg, 3-Phosphoglycerate; and, Adenine; adp, Adenosine diphosphate; amp, Adenosine monophosphate; atp, Adenosine triphosphate; dhap, Dihydroxyacetone phosphate; e4p, Erythrose-4-phosphate; f6p, Fructose-6-phosphate; fdp, Fructose-1,6-diphosphate; g3p, Glyceraldehyde 3-phosphate; g6p, D-glucose 6-phosphate; hxan, Hypoxanthine; imp, Inosine monophosphate; ins, Inosine; k, Potassium; lac-D, D-lactate; na1, Sodium; nadh, Nicotinamide adenine dinucleotide; pep, Phosphoenolpyruvate; prpp, 5-Phospho-alpha-D-ribose-1-diphosphate; pyr, Pyruvate; r1p, alpha-D-Ribose 1-phosphate; r5p, alpha-D-Ribose 5-phosphate; ru5p-D, D-Ribulose 5-phosphate; s7p, Sedoheptulose 7-phosphate; xu5p-D, D-Xylulose 5-phosphate.
Mentions: To uncover the role that kinetic parameters have onto time scale profiles, a statistical analysis over the hierarchical time scale library was accomplished. Thus, proceeding as described in methods section (Equation 6), the average and the standard deviation for the inverse of each time scale obtained from Jacobian library were calculated and plotted in Figure 2A Two relevant results emerge from this result. Firstly, inverse of times scale reveals a low dispersion respect to the average, thus indicating the presence of robust statistical properties on kinetic parameters, see Figure 2A, left panel. Furthermore, repeating the same statistical analysis now directly over the profile of time scales, one obtain that robustness prevails for all time scales, except for the last three slowest ones, indicating its sensitivity to kinetic parameters, see Figure S1 in supporting information. Robustness is a property that has been reported in a variety of biological systems [34], [35]. In this paper, we present evidence to indicate that time scale associated with a perturbed metabolic networks is statistical robust to kinetic parameters for most time scales, and only in the case of slow time regimens they can have a considerable influence on dynamics. To evaluate the robustness of this result and heuristically evaluate the dispersion intrinsic of the network and the coming from sampling artifact, I have explored how the statistical properties of the eigenvalues and time scales vary at different k-cone sample sizes. Figure 3A shows the average and standard deviation (both in log10 scale) for the eigenvalues and time scales obtained when one select 4, 600 and 12586 points in k-cone space. Particularly, we note that in all cases the average and dispersion converge according the number of sample size increase. Notably, this approach allows to identify time scales that are closely similar to those obtained by using a detail kinetic model for hRBC [15], [18], see Figure 3B.

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