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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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Comparisons among Monte-Carlo simulations, U-system simulations and original GMA model simulations. The U-system simulations and original GMA model simulations are shown in blue and green lines, respectively. The Monte-Carlo simulations in response to changes of rate constants within ranges of 0.5 and 20 and kinetic orders between 0.2(-0.2) and 0.8(-0.8) are shown in red lines. The concentrations of Xi along the y-axis are scaled using maximum and minimum values of each simulation, while the time along the x-axis is scaled using maximal values of the Xi concentrations before they return to their steady-states.
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Figure 3: Comparisons among Monte-Carlo simulations, U-system simulations and original GMA model simulations. The U-system simulations and original GMA model simulations are shown in blue and green lines, respectively. The Monte-Carlo simulations in response to changes of rate constants within ranges of 0.5 and 20 and kinetic orders between 0.2(-0.2) and 0.8(-0.8) are shown in red lines. The concentrations of Xi along the y-axis are scaled using maximum and minimum values of each simulation, while the time along the x-axis is scaled using maximal values of the Xi concentrations before they return to their steady-states.

Mentions: Figure 3 shows a selection of Monte-Carlo simulation results associated with changes in the rate constants between 0.2 and 20, and kinetic orders between 0.2 (-0.2) and 0.8 (-0.8) for the branched pathway model with inhibition and activation as well as the simulations for U-system model (blue lines) and original GMA-system model (green lines). Parameter combinations not leading to a stable system were removed. For the U-system model, all rate constants and kinetic orders of substrates and enzymes were set to 1, while the kinetic orders of parameters for inhibitions and activations were set to be -0.5 and 0.5, respectively. As a result, the observed metabolite concentrations are no longer the real concentrations, but simply indications of the shapes of their trajectories. The present study calls these concentrations as U-system concentrations (Figure 1b).


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)

Comparisons among Monte-Carlo simulations, U-system simulations and original GMA model simulations. The U-system simulations and original GMA model simulations are shown in blue and green lines, respectively. The Monte-Carlo simulations in response to changes of rate constants within ranges of 0.5 and 20 and kinetic orders between 0.2(-0.2) and 0.8(-0.8) are shown in red lines. The concentrations of Xi along the y-axis are scaled using maximum and minimum values of each simulation, while the time along the x-axis is scaled using maximal values of the Xi concentrations before they return to their steady-states.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 3: Comparisons among Monte-Carlo simulations, U-system simulations and original GMA model simulations. The U-system simulations and original GMA model simulations are shown in blue and green lines, respectively. The Monte-Carlo simulations in response to changes of rate constants within ranges of 0.5 and 20 and kinetic orders between 0.2(-0.2) and 0.8(-0.8) are shown in red lines. The concentrations of Xi along the y-axis are scaled using maximum and minimum values of each simulation, while the time along the x-axis is scaled using maximal values of the Xi concentrations before they return to their steady-states.
Mentions: Figure 3 shows a selection of Monte-Carlo simulation results associated with changes in the rate constants between 0.2 and 20, and kinetic orders between 0.2 (-0.2) and 0.8 (-0.8) for the branched pathway model with inhibition and activation as well as the simulations for U-system model (blue lines) and original GMA-system model (green lines). Parameter combinations not leading to a stable system were removed. For the U-system model, all rate constants and kinetic orders of substrates and enzymes were set to 1, while the kinetic orders of parameters for inhibitions and activations were set to be -0.5 and 0.5, respectively. As a result, the observed metabolite concentrations are no longer the real concentrations, but simply indications of the shapes of their trajectories. The present study calls these concentrations as U-system concentrations (Figure 1b).

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