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• W2006661775 abstract "Mathematical analysis and modeling of biochemical reaction networks requires knowledge of the permitted directionality of reactions and membrane transport processes. This information can be gathered from the standard Gibbs energy changes (ΔG0) of reactions and the concentration ranges of their reactants. Currently, experimental ΔG0 values are not available for the vast majority of cellular biochemical processes. We propose what we believe to be a novel computational method to infer the unknown ΔG0 value of a reaction from the known ΔG0 value of the chemically most similar reaction. The chemical similarity of two arbitrary reactions is measured by the relative number (T) of co-occurring changes in the chemical attributes of their reactants. Testing our method across a validated reference set of 173 biochemical reactions with experimentally determined ΔG0 values, we found that a minimum reaction similarity of T = 0.6 is required to infer ΔG0 values with an error of <10 kJ/mol. Applying this criterion, our method allows us to assign ΔG0 values to 458 additional reactions of the BioPath database. We believe our approach permits us to minimize the number of ΔG0 measurements required for a full coverage of a given reaction network with reliable ΔG0 values." @default.
• W2006661775 created "2016-06-24" @default.
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• W2006661775 date "2010-06-01" @default.
• W2006661775 modified "2023-10-18" @default.
• W2006661775 title "IGERS: Inferring Gibbs Energy Changes of Biochemical Reactions from Reaction Similarities" @default.
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• W2006661775 doi "https://doi.org/10.1016/j.bpj.2010.02.052" @default.
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