eQuilibrator is a simple web interface designed to enable easy thermodynamic analysis of biochemical systems. eQuilibrator enables free-text search for biochemical compounds and reactions and provides thermodynamic estimates for both in a variety of conditions. Estimation of thermodynamic parameters (ΔrG and ΔfG) elucidates how much energy is required to drive a particular biochemical reaction and in which direction the reaction will flow in particular cellular conditions .
Because experimental measurement of the free energy of formation (ΔfG°) of compounds is technically challenging, the vast majority of known metabolites have not been thermodynamically characterized. eQuilibrator uses a well-studied approximation of ΔfG called group contribution , enabling thermodynamic analysis of many biochemical reactions and pathways.
Currently, eQuilibrator can provide estimates for many compounds in the KEGG database (about 4500). Individual compounds and enzymes can be searched for by their common names (“water”, “glucosamine”, “hexokinase”), and reactions can be entered in a simple, free-text format (“ribulose bisphosphate + CO2 + water => 2 3-phosphoglycerate”) that eQuilibrator parses automatically. eQuilibrator also allows manipulation of the conditions of a reaction - pH, ionic strength, and reactant and product concentrations - to help explore the thermodynamic landscape of a biochemical reaction.
eQuilibrator is a project of the Milo Lab at the Weizmann Institute in Rehovot, Israel. If you have any thoughts or questions feel free to write us on the eQuilibrator Users Google Group
Implementation of eQuilibrator
eQuilibrator makes heavy use of open source libraries and frameworks and is open-source itself. You can find the eQuilibrator source code on
our GitHub repository called equilibrator.
The eQuilibrator back-end is implemented in pure Python using the Django web framework and several other open-source Python libraries, including NumPy and Pyparsing.
All thermodynamic data presented by eQuilibrator was generated using the Component Contribution method [DOI: 10.1371/journal.pcbi.1003098] which was implemented also in Python and is open-source as well. You can find the source code on our GitHub repository called component-contribution.