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Measuring Carbon-based Contaminant Mineralization Using Combined CO2 Flux and Radiocarbon Analyses

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Published on: October 21, 2016

Refining carbon flux paths using atomic trace data.

Jon Pey¹, Francisco J Planes, John E Beasley

¹CEIT and TECNUN, University of Navarra, Manuel de Lardizabal 15, 20018 San Sebastian, Spain and Mathematical Sciences, Brunel University, Kingston Lane, UB8 3PH, Uxbridge, UK.

Bioinformatics (Oxford, England)

|November 26, 2013

Summary

This summary is machine-generated.

Atomic carbon flux paths (aCFP) improve metabolic pathway analysis by incorporating atomic fate information, reducing false positives and enhancing biological relevance in systems biology. This method aids in interpreting omics data for metabolic networks.

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Stable Isotopic Profiling of Intermediary Metabolic Flux in Developing and Adult Stage Caenorhabditis elegans

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Published on: February 27, 2011

Area of Science:

Systems biology
Metabolic network analysis
Bioinformatics

Background:

Omics data analysis commonly employs pathway analysis tools.
Existing carbon flux paths (CFPs) identify linear metabolic pathways but can yield biologically irrelevant false positives.
A novel pathway concept, CFPs, was developed for linear pathways, satisfying mass balance and thermodynamic constraints.

Purpose of the Study:

To enhance the biological relevance of carbon flux paths (CFPs) by incorporating atomic fate information.
To introduce the atomic CFP (aCFP) approach for more accurate metabolic pathway identification.
To overcome limitations of existing CFP methods in capturing biologically meaningful pathways.

Main Methods:

Amended the formulation of carbon flux paths (CFPs) to include atomic fate information, creating atomic CFPs (aCFPs).
Utilized stoichiometric model constraints including mass balancing and thermodynamics (irreversibility).
Benchmarked aCFP against CFP using metabolic networks in Escherichia coli.

Main Results:

Atomic CFP (aCFP) demonstrated superior performance over CFP in recovering biologically relevant pathways.
aCFP effectively reduced false positive pathways, leading to more interpretable results.
The aCFP method was successfully applied to genome-scale metabolic networks.

Conclusions:

Atomic CFP (aCFP) significantly improves the biological relevance of pathway analysis in systems biology.
The aCFP approach offers a valuable tool for interpreting omics data, especially with advancing genome-scale metabolic reconstructions.
This method enhances the discovery of meaningful metabolic routes from complex biological data.