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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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System matrices complexity reduction. H1, H2, LH1, LH2 and UH for the simple E. coli example. A substantial reduction in nonzero elements between the H and L matrices can clearly be seen.
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Figure 4: System matrices complexity reduction. H1, H2, LH1, LH2 and UH for the simple E. coli example. A substantial reduction in nonzero elements between the H and L matrices can clearly be seen.

Mentions: When expressed in terms of the variable y, our system becomes much more sparse. This is illustrated in Figure 4 which shows H1, H2, LH1, LH2 and UH for the Embden-Meyerhof and Pentose Phosphate Pathway example. The transformation has two essential benefits:


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

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

System matrices complexity reduction. H1, H2, LH1, LH2 and UH for the simple E. coli example. A substantial reduction in nonzero elements between the H and L matrices can clearly be seen.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 4: System matrices complexity reduction. H1, H2, LH1, LH2 and UH for the simple E. coli example. A substantial reduction in nonzero elements between the H and L matrices can clearly be seen.
Mentions: When expressed in terms of the variable y, our system becomes much more sparse. This is illustrated in Figure 4 which shows H1, H2, LH1, LH2 and UH for the Embden-Meyerhof and Pentose Phosphate Pathway example. The transformation has two essential benefits:

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