Limits...
Estimating the size of the solution space of metabolic networks.

Braunstein A, Mulet R, Pagnani A - BMC Bioinformatics (2008)

Bottom Line: Then we test the effect of gene knock-outs on the size of the solution space in the case of E. coli central metabolism.The method is shown to obtain, quantitatively and qualitatively compatible results with the ones of standard algorithms (where this comparison is possible) being still efficient on the analysis of large biological systems, where exact deterministic methods experience an explosion in algorithmic time.The algorithm we propose can be considered as an alternative to Monte Carlo sampling methods.

View Article: PubMed Central - HTML - PubMed

Affiliation: Politecnico di Torino, Corso Duca degli Abruzzi 34, I-10129, Torino, Italy. alfredo.braunstein@polito.it

ABSTRACT

Background: Cellular metabolism is one of the most investigated system of biological interactions. While the topological nature of individual reactions and pathways in the network is quite well understood there is still a lack of comprehension regarding the global functional behavior of the system. In the last few years flux-balance analysis (FBA) has been the most successful and widely used technique for studying metabolism at system level. This method strongly relies on the hypothesis that the organism maximizes an objective function. However only under very specific biological conditions (e.g. maximization of biomass for E. coli in reach nutrient medium) the cell seems to obey such optimization law. A more refined analysis not assuming extremization remains an elusive task for large metabolic systems due to algorithmic limitations.

Results: In this work we propose a novel algorithmic strategy that provides an efficient characterization of the whole set of stable fluxes compatible with the metabolic constraints. Using a technique derived from the fields of statistical physics and information theory we designed a message-passing algorithm to estimate the size of the affine space containing all possible steady-state flux distributions of metabolic networks. The algorithm, based on the well known Bethe approximation, can be used to approximately compute the volume of a non full-dimensional convex polytope in high dimensions. We first compare the accuracy of the predictions with an exact algorithm on small random metabolic networks. We also verify that the predictions of the algorithm match closely those of Monte Carlo based methods in the case of the Red Blood Cell metabolic network. Then we test the effect of gene knock-outs on the size of the solution space in the case of E. coli central metabolism. Finally we analyze the statistical properties of the average fluxes of the reactions in the E. coli metabolic network.

Conclusion: We propose a novel efficient distributed algorithmic strategy to estimate the size and shape of the affine space of a non full-dimensional convex polytope in high dimensions. The method is shown to obtain, quantitatively and qualitatively compatible results with the ones of standard algorithms (where this comparison is possible) being still efficient on the analysis of large biological systems, where exact deterministic methods experience an explosion in algorithmic time. The algorithm we propose can be considered as an alternative to Monte Carlo sampling methods.

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Flux knock-out curves. Flux knock-out impact on the volume of the space of solutions in E. coli central metabolism. On the x-axis we display the percentage of reduction of any given flux, and on the y-axis the relative volume difference with respect to the unperturbed system. We use upper-case keys for internal fluxes, and lower-case to exchange fluxes. We indicate keys only for the 20 fluxes having larger impact on the volume (dots) and we display the rest fluxes with thin scattered lines.
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Figure 8: Flux knock-out curves. Flux knock-out impact on the volume of the space of solutions in E. coli central metabolism. On the x-axis we display the percentage of reduction of any given flux, and on the y-axis the relative volume difference with respect to the unperturbed system. We use upper-case keys for internal fluxes, and lower-case to exchange fluxes. We indicate keys only for the 20 fluxes having larger impact on the volume (dots) and we display the rest fluxes with thin scattered lines.

Mentions: In Figure 8 we display the whole set of S0 - SKO() vs. knock-out percentage curves. We can observe how heterogeneous is the impact of the different fluxes on the volume. Moreover one can observe how different curves may cross depending on the knock-out percentage displaying thus an intriguing scenario of differential flux-reduction impacts. Let us now concentrate on the 20 fluxes having larger impact on the space of solutions: in Figure 9 we display complete knock-outs values S0 -SKO( = 0). Focusing only on internal fluxes (the first two fluxes are indeed exchange fluxes of water and protons), one can observe that the first half of them basically compose the backbone of glycolysis showing little pathway redundancy in the network, while reactions like FUM, ACONT, SUC and SUCCD1i appear in the Krebs cycle and again show little pathway redundancy in the network (see Additional file 2).


Estimating the size of the solution space of metabolic networks.

Braunstein A, Mulet R, Pagnani A - BMC Bioinformatics (2008)

Flux knock-out curves. Flux knock-out impact on the volume of the space of solutions in E. coli central metabolism. On the x-axis we display the percentage of reduction of any given flux, and on the y-axis the relative volume difference with respect to the unperturbed system. We use upper-case keys for internal fluxes, and lower-case to exchange fluxes. We indicate keys only for the 20 fluxes having larger impact on the volume (dots) and we display the rest fluxes with thin scattered lines.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2483728&req=5

Figure 8: Flux knock-out curves. Flux knock-out impact on the volume of the space of solutions in E. coli central metabolism. On the x-axis we display the percentage of reduction of any given flux, and on the y-axis the relative volume difference with respect to the unperturbed system. We use upper-case keys for internal fluxes, and lower-case to exchange fluxes. We indicate keys only for the 20 fluxes having larger impact on the volume (dots) and we display the rest fluxes with thin scattered lines.
Mentions: In Figure 8 we display the whole set of S0 - SKO() vs. knock-out percentage curves. We can observe how heterogeneous is the impact of the different fluxes on the volume. Moreover one can observe how different curves may cross depending on the knock-out percentage displaying thus an intriguing scenario of differential flux-reduction impacts. Let us now concentrate on the 20 fluxes having larger impact on the space of solutions: in Figure 9 we display complete knock-outs values S0 -SKO( = 0). Focusing only on internal fluxes (the first two fluxes are indeed exchange fluxes of water and protons), one can observe that the first half of them basically compose the backbone of glycolysis showing little pathway redundancy in the network, while reactions like FUM, ACONT, SUC and SUCCD1i appear in the Krebs cycle and again show little pathway redundancy in the network (see Additional file 2).

Bottom Line: Then we test the effect of gene knock-outs on the size of the solution space in the case of E. coli central metabolism.The method is shown to obtain, quantitatively and qualitatively compatible results with the ones of standard algorithms (where this comparison is possible) being still efficient on the analysis of large biological systems, where exact deterministic methods experience an explosion in algorithmic time.The algorithm we propose can be considered as an alternative to Monte Carlo sampling methods.

View Article: PubMed Central - HTML - PubMed

Affiliation: Politecnico di Torino, Corso Duca degli Abruzzi 34, I-10129, Torino, Italy. alfredo.braunstein@polito.it

ABSTRACT

Background: Cellular metabolism is one of the most investigated system of biological interactions. While the topological nature of individual reactions and pathways in the network is quite well understood there is still a lack of comprehension regarding the global functional behavior of the system. In the last few years flux-balance analysis (FBA) has been the most successful and widely used technique for studying metabolism at system level. This method strongly relies on the hypothesis that the organism maximizes an objective function. However only under very specific biological conditions (e.g. maximization of biomass for E. coli in reach nutrient medium) the cell seems to obey such optimization law. A more refined analysis not assuming extremization remains an elusive task for large metabolic systems due to algorithmic limitations.

Results: In this work we propose a novel algorithmic strategy that provides an efficient characterization of the whole set of stable fluxes compatible with the metabolic constraints. Using a technique derived from the fields of statistical physics and information theory we designed a message-passing algorithm to estimate the size of the affine space containing all possible steady-state flux distributions of metabolic networks. The algorithm, based on the well known Bethe approximation, can be used to approximately compute the volume of a non full-dimensional convex polytope in high dimensions. We first compare the accuracy of the predictions with an exact algorithm on small random metabolic networks. We also verify that the predictions of the algorithm match closely those of Monte Carlo based methods in the case of the Red Blood Cell metabolic network. Then we test the effect of gene knock-outs on the size of the solution space in the case of E. coli central metabolism. Finally we analyze the statistical properties of the average fluxes of the reactions in the E. coli metabolic network.

Conclusion: We propose a novel efficient distributed algorithmic strategy to estimate the size and shape of the affine space of a non full-dimensional convex polytope in high dimensions. The method is shown to obtain, quantitatively and qualitatively compatible results with the ones of standard algorithms (where this comparison is possible) being still efficient on the analysis of large biological systems, where exact deterministic methods experience an explosion in algorithmic time. The algorithm we propose can be considered as an alternative to Monte Carlo sampling methods.

Show MeSH
Related in: MedlinePlus