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A U-system approach for predicting metabolic behaviors and responses based on an alleged metabolic reaction network.

Sriyudthsak K, Sawada Y, Chiba Y, Yamashita Y, Kanaya S, Onouchi H, Fujiwara T, Naito S, Voit EO, Shiraishi F, Hirai MY - BMC Syst Biol (2014)

Bottom Line: The data may also be corrupted by experimental uncertainties and sometimes do not contain all information regarding variables that cannot be measured for technical reasons.The U-system model does not necessarily fit all data well but is often sufficient for predicting metabolic behavior of metabolites which cannot be simultaneously measured, identifying inconsistencies between experimental data and the assumed underlying pathway structure, as well as predicting system responses to a modification of gene or enzyme.The U-system approach can effectively predict metabolic behaviors and responses based on structures of an alleged metabolic reaction network.

View Article: PubMed Central - HTML - PubMed

ABSTRACT

Background: Progress in systems biology offers sophisticated approaches toward a comprehensive understanding of biological systems. Yet, computational analyses are held back due to difficulties in determining suitable model parameter values from experimental data which naturally are subject to biological fluctuations. The data may also be corrupted by experimental uncertainties and sometimes do not contain all information regarding variables that cannot be measured for technical reasons.

Results: We show here a streamlined approach for the construction of a coarse model that allows us to set up dynamic models with minimal input information. The approach uses a hybrid between a pure mass action system and a generalized mass action (GMA) system in the framework of biochemical systems theory (BST) with rate constants of 1, normal kinetic orders of 1, and -0.5 and 0.5 for inhibitory and activating effects, named Unity (U)-system. The U-system model does not necessarily fit all data well but is often sufficient for predicting metabolic behavior of metabolites which cannot be simultaneously measured, identifying inconsistencies between experimental data and the assumed underlying pathway structure, as well as predicting system responses to a modification of gene or enzyme. The U-system approach was validated with small, generic systems and implemented to model a large-scale metabolic reaction network of a higher plant, Arabidopsis. The dynamic behaviors obtained by predictive simulations agreed with actually available metabolomic time-series data, identified probable errors in the experimental datasets, and estimated probable behavior of unmeasurable metabolites in a qualitative manner. The model could also predict metabolic responses of Arabidopsis with altered network structures due to genetic modification.

Conclusions: The U-system approach can effectively predict metabolic behaviors and responses based on structures of an alleged metabolic reaction network. Thus, it can be a useful first-line tool of data analysis, model diagnostics and aid the design of next-step experiments.

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Simulations from modified U-system approach compared with relative concentrations obtained by metabolome and amino acid analyses. The red and blue dots represent relative concentrations (see Materials and Methods) of lysine and threonine supplementation experiment with coefficient of variations less than or more than 20%, respectively, which are obtained by metabolome (upper boxes) and amino acid (middle boxes) analyses. Red lines represent simulations from the modified U-system approach. The normalized peak intensities of lysine and threonine-supplemented samples and control samples are shown as red dots and black dots, respectively (lower boxes). a, threonine (X26); b, lysine (X93); c, glutamate (X37); d, aspartate (X78).
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Figure 6: Simulations from modified U-system approach compared with relative concentrations obtained by metabolome and amino acid analyses. The red and blue dots represent relative concentrations (see Materials and Methods) of lysine and threonine supplementation experiment with coefficient of variations less than or more than 20%, respectively, which are obtained by metabolome (upper boxes) and amino acid (middle boxes) analyses. Red lines represent simulations from the modified U-system approach. The normalized peak intensities of lysine and threonine-supplemented samples and control samples are shown as red dots and black dots, respectively (lower boxes). a, threonine (X26); b, lysine (X93); c, glutamate (X37); d, aspartate (X78).

Mentions: The modified U-system includes various approximations so that it is necessary to validate whether it is practicability and conformable to experimental data. Figure 6 (upper boxes) shows the time courses after lysine and threonine application of the relative concentrations of four amino acids measured in metabolome analysis. The threonine concentration (X26) exhibited small biological fluctuations among sample replicates, increasing from its steady-state level to a maximum at around 24 h, from where it decreased back to its steady-state level. The lysine concentrations (X93) contained more biological fluctuations among the sample replications, but it is still possible to detect a clear pattern over time. The glutamate concentration (X37) increased within 2 h and then seemed to be constant. The aspartate concentration (X78) increased slightly, then decreased, and finally tended to increase considerably.


A U-system approach for predicting metabolic behaviors and responses based on an alleged metabolic reaction network.

Sriyudthsak K, Sawada Y, Chiba Y, Yamashita Y, Kanaya S, Onouchi H, Fujiwara T, Naito S, Voit EO, Shiraishi F, Hirai MY - BMC Syst Biol (2014)

Simulations from modified U-system approach compared with relative concentrations obtained by metabolome and amino acid analyses. The red and blue dots represent relative concentrations (see Materials and Methods) of lysine and threonine supplementation experiment with coefficient of variations less than or more than 20%, respectively, which are obtained by metabolome (upper boxes) and amino acid (middle boxes) analyses. Red lines represent simulations from the modified U-system approach. The normalized peak intensities of lysine and threonine-supplemented samples and control samples are shown as red dots and black dots, respectively (lower boxes). a, threonine (X26); b, lysine (X93); c, glutamate (X37); d, aspartate (X78).
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4305983&req=5

Figure 6: Simulations from modified U-system approach compared with relative concentrations obtained by metabolome and amino acid analyses. The red and blue dots represent relative concentrations (see Materials and Methods) of lysine and threonine supplementation experiment with coefficient of variations less than or more than 20%, respectively, which are obtained by metabolome (upper boxes) and amino acid (middle boxes) analyses. Red lines represent simulations from the modified U-system approach. The normalized peak intensities of lysine and threonine-supplemented samples and control samples are shown as red dots and black dots, respectively (lower boxes). a, threonine (X26); b, lysine (X93); c, glutamate (X37); d, aspartate (X78).
Mentions: The modified U-system includes various approximations so that it is necessary to validate whether it is practicability and conformable to experimental data. Figure 6 (upper boxes) shows the time courses after lysine and threonine application of the relative concentrations of four amino acids measured in metabolome analysis. The threonine concentration (X26) exhibited small biological fluctuations among sample replicates, increasing from its steady-state level to a maximum at around 24 h, from where it decreased back to its steady-state level. The lysine concentrations (X93) contained more biological fluctuations among the sample replications, but it is still possible to detect a clear pattern over time. The glutamate concentration (X37) increased within 2 h and then seemed to be constant. The aspartate concentration (X78) increased slightly, then decreased, and finally tended to increase considerably.

Bottom Line: The data may also be corrupted by experimental uncertainties and sometimes do not contain all information regarding variables that cannot be measured for technical reasons.The U-system model does not necessarily fit all data well but is often sufficient for predicting metabolic behavior of metabolites which cannot be simultaneously measured, identifying inconsistencies between experimental data and the assumed underlying pathway structure, as well as predicting system responses to a modification of gene or enzyme.The U-system approach can effectively predict metabolic behaviors and responses based on structures of an alleged metabolic reaction network.

View Article: PubMed Central - HTML - PubMed

ABSTRACT

Background: Progress in systems biology offers sophisticated approaches toward a comprehensive understanding of biological systems. Yet, computational analyses are held back due to difficulties in determining suitable model parameter values from experimental data which naturally are subject to biological fluctuations. The data may also be corrupted by experimental uncertainties and sometimes do not contain all information regarding variables that cannot be measured for technical reasons.

Results: We show here a streamlined approach for the construction of a coarse model that allows us to set up dynamic models with minimal input information. The approach uses a hybrid between a pure mass action system and a generalized mass action (GMA) system in the framework of biochemical systems theory (BST) with rate constants of 1, normal kinetic orders of 1, and -0.5 and 0.5 for inhibitory and activating effects, named Unity (U)-system. The U-system model does not necessarily fit all data well but is often sufficient for predicting metabolic behavior of metabolites which cannot be simultaneously measured, identifying inconsistencies between experimental data and the assumed underlying pathway structure, as well as predicting system responses to a modification of gene or enzyme. The U-system approach was validated with small, generic systems and implemented to model a large-scale metabolic reaction network of a higher plant, Arabidopsis. The dynamic behaviors obtained by predictive simulations agreed with actually available metabolomic time-series data, identified probable errors in the experimental datasets, and estimated probable behavior of unmeasurable metabolites in a qualitative manner. The model could also predict metabolic responses of Arabidopsis with altered network structures due to genetic modification.

Conclusions: The U-system approach can effectively predict metabolic behaviors and responses based on structures of an alleged metabolic reaction network. Thus, it can be a useful first-line tool of data analysis, model diagnostics and aid the design of next-step experiments.

Show MeSH
Related in: MedlinePlus