An integrated open framework for thermodynamics of reactions that combines accuracy and coverage.
BACKGROUND
The laws of thermodynamics describe a direct, quantitative relationship between metabolite concentrations and reaction directionality. Despite great efforts, thermodynamic data suffer from limited coverage, scattered accessibility and non-standard annotations. We present a framework for unifying thermodynamic data from multiple sources and demonstrate two new techniques for extrapolating the Gibbs energies of unmeasured reactions and conditions.


RESULTS
Both methods account for changes in cellular conditions (pH, ionic strength, etc.) by using linear regression over the ΔG(○) of pseudoisomers and reactions. The Pseudoisomeric Reactant Contribution method systematically infers compound formation energies using measured K' and pK(a) data. The Pseudoisomeric Group Contribution method extends the group contribution method and achieves a high coverage of unmeasured reactions. We define a continuous index that predicts the reversibility of a reaction under a given physiological concentration range. In the characteristic physiological range 3μM-3mM, we find that roughly half of the reactions in Escherichia coli's metabolism are reversible. These new tools can increase the accuracy of thermodynamic-based models, especially in non-standard pH and ionic strengths. The reversibility index can help modelers decide which reactions are reversible in physiological conditions.


BACKGROUND
Freely available on the web at: http://equilibrator.weizmann.ac.il. Website implemented in Python, MySQL, Apache and Django, with all major browsers supported. The framework is open-source (code.google.com/p/milo-lab), implemented in pure Python and tested mainly on Linux.


BACKGROUND
ron.milo@weizmann.ac.il


BACKGROUND
Supplementary data are available at Bioinformatics online.
Department of Plant Sciences, Weizmann Institute of Science, Rehovot 76100, Israel.
Elad
E
Noor
Arren
A
Bar-Even
Avi
A
Flamholz
Yaniv
Y
Lubling
Dan
D
Davidi
Ron
R
Milo
D019295:Computational Biology; D004734:Energy Metabolism; D004926:Escherichia coli / Q000378:metabolism*; D006863:Hydrogen-Ion Concentration; D007700:Kinetics; D012984:Software; D013816:Thermodynamics