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Fluxomers: a new approach for 13C metabolic flux analysis.

Srour O, Young JD, Eldar YC - BMC Syst Biol (2011)

Bottom Line: These composite variables combine both fluxes and isotopomer abundances, which results in a simply-posed formulation and an improved error model that is insensitive to isotopomer measurement normalization.Substantial improvements in convergence time and statistical quality of results can be achieved by applying fluxomer variables and the FIA algorithm to compute best-fit solutions to MFA models.We expect that the fluxomer formulation will provide a more suitable basis for future algorithms that analyze very large scale networks and design optimal isotope labeling experiments.

View Article: PubMed Central - HTML - PubMed

Affiliation: Dept. of Electrical Engineering, Technion-Israel Institute of Technology, Haifa, Israel.

ABSTRACT

Background: The ability to perform quantitative studies using isotope tracers and metabolic flux analysis (MFA) is critical for detecting pathway bottlenecks and elucidating network regulation in biological systems, especially those that have been engineered to alter their native metabolic capacities. Mathematically, MFA models are traditionally formulated using separate state variables for reaction fluxes and isotopomer abundances. Analysis of isotope labeling experiments using this set of variables results in a non-convex optimization problem that suffers from both implementation complexity and convergence problems.

Results: This article addresses the mathematical and computational formulation of (13)C MFA models using a new set of variables referred to as fluxomers. These composite variables combine both fluxes and isotopomer abundances, which results in a simply-posed formulation and an improved error model that is insensitive to isotopomer measurement normalization. A powerful fluxomer iterative algorithm (FIA) is developed and applied to solve the MFA optimization problem. For moderate-sized networks, the algorithm is shown to outperform the commonly used 13CFLUX cumomer-based algorithm and the more recently introduced OpenFLUX software that relies upon an elementary metabolite unit (EMU) network decomposition, both in terms of convergence time and output variability.

Conclusions: Substantial improvements in convergence time and statistical quality of results can be achieved by applying fluxomer variables and the FIA algorithm to compute best-fit solutions to MFA models. We expect that the fluxomer formulation will provide a more suitable basis for future algorithms that analyze very large scale networks and design optimal isotope labeling experiments.

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E.Coli EMP and PPP Metabolic Pathways. The Embden-Meyerhof and Pentose Phosphate metabolic pathways of Escherichia coli.
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Figure 1: E.Coli EMP and PPP Metabolic Pathways. The Embden-Meyerhof and Pentose Phosphate metabolic pathways of Escherichia coli.

Mentions: In this section we examine a network representing the Embden-Meyerhof and Pentose Phosphate pathways of E. coli, which is based upon the tutorial supplied by Wiechert et al. as part of their 13CFLUX software package. Since our FIA implementation natively supports 13CFLUX input files (i.e. "FTBL" files), the same input files can be used for both algorithms. (Note, however, that FIA does not require definition of free fluxes nor initial values, and thus these are simply ignored when imported). Figure 1 shows the simple network used along with the nomenclature used in previous publications. In addition to the network structure, the models are provided with flux and isotopic measurements as shown in Table 1.


Fluxomers: a new approach for 13C metabolic flux analysis.

Srour O, Young JD, Eldar YC - BMC Syst Biol (2011)

E.Coli EMP and PPP Metabolic Pathways. The Embden-Meyerhof and Pentose Phosphate metabolic pathways of Escherichia coli.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3750106&req=5

Figure 1: E.Coli EMP and PPP Metabolic Pathways. The Embden-Meyerhof and Pentose Phosphate metabolic pathways of Escherichia coli.
Mentions: In this section we examine a network representing the Embden-Meyerhof and Pentose Phosphate pathways of E. coli, which is based upon the tutorial supplied by Wiechert et al. as part of their 13CFLUX software package. Since our FIA implementation natively supports 13CFLUX input files (i.e. "FTBL" files), the same input files can be used for both algorithms. (Note, however, that FIA does not require definition of free fluxes nor initial values, and thus these are simply ignored when imported). Figure 1 shows the simple network used along with the nomenclature used in previous publications. In addition to the network structure, the models are provided with flux and isotopic measurements as shown in Table 1.

Bottom Line: These composite variables combine both fluxes and isotopomer abundances, which results in a simply-posed formulation and an improved error model that is insensitive to isotopomer measurement normalization.Substantial improvements in convergence time and statistical quality of results can be achieved by applying fluxomer variables and the FIA algorithm to compute best-fit solutions to MFA models.We expect that the fluxomer formulation will provide a more suitable basis for future algorithms that analyze very large scale networks and design optimal isotope labeling experiments.

View Article: PubMed Central - HTML - PubMed

Affiliation: Dept. of Electrical Engineering, Technion-Israel Institute of Technology, Haifa, Israel.

ABSTRACT

Background: The ability to perform quantitative studies using isotope tracers and metabolic flux analysis (MFA) is critical for detecting pathway bottlenecks and elucidating network regulation in biological systems, especially those that have been engineered to alter their native metabolic capacities. Mathematically, MFA models are traditionally formulated using separate state variables for reaction fluxes and isotopomer abundances. Analysis of isotope labeling experiments using this set of variables results in a non-convex optimization problem that suffers from both implementation complexity and convergence problems.

Results: This article addresses the mathematical and computational formulation of (13)C MFA models using a new set of variables referred to as fluxomers. These composite variables combine both fluxes and isotopomer abundances, which results in a simply-posed formulation and an improved error model that is insensitive to isotopomer measurement normalization. A powerful fluxomer iterative algorithm (FIA) is developed and applied to solve the MFA optimization problem. For moderate-sized networks, the algorithm is shown to outperform the commonly used 13CFLUX cumomer-based algorithm and the more recently introduced OpenFLUX software that relies upon an elementary metabolite unit (EMU) network decomposition, both in terms of convergence time and output variability.

Conclusions: Substantial improvements in convergence time and statistical quality of results can be achieved by applying fluxomer variables and the FIA algorithm to compute best-fit solutions to MFA models. We expect that the fluxomer formulation will provide a more suitable basis for future algorithms that analyze very large scale networks and design optimal isotope labeling experiments.

Show MeSH
Related in: MedlinePlus