Thermodynamics-based metabolic flux analysis.

By Christopher S. Henry, Linda J. Broadbelt, Vassily Hatzimanikatis. Biophysical Journal, 2007

620 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Molecular Biology, Physical and Theoretical Chemistry

View Paper: https://www.semanticscholar.org/paper/3f97d8bcab31a7f8041c6871d28af4f7955e9b1b

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Citations in the corpus

Constraining the metabolic genotype-phenotype relationship using a phylogeny of in silico methods (2012, methodology): “These values can help determine the direction of reactions 54,102 , predict reasonable concentration levels 98 and allow the use of metabolite concentrations 103 and of ranges for kinetic parameters 99 as constraints.”
COBRApy: COnstraints-Based Reconstruction and Analysis for Python (2013, background): “These constraints include compartmentalization, mass conservation, molecular crowding [11], thermodynamic directionality [12], and transcription factor activity [13].”
The Ehrlich Pathway for Fusel Alcohol Production: a Century of Research on Saccharomyces cerevisiae Metabolism (2008, background): “The transaminating reaction is followed by decarboxylation, which is generally characterized by a strongly negative Gibbs free-energy change (22).”
Genome-scale models of bacterial metabolism: reconstruction and applications (2008, methodology): “Flux variability analysis has been broadly used to predict the activity of reactions for speciﬁc sets of metabolic constraints (Mahadevan & Schilling, 2003; Reed & Palsson, 2004; Teusink et al ., 2006; Feist et al ., 2007; Henry et al ., 2007; Shlomi et al ., 2007a).”
eQuilibrator—the biochemical thermodynamics calculator (2011, methodology): “However, even this approach is somewhat limited as there are thousands more metabolically-interesting compounds and reactions that remain thermodynamically uncharacterized (11,17,18).”
Absolute Metabolite Concentrations and Implied Enzyme Active Site Occupancy in Escherichia coli (2009, background): “With a few exceptions, these changes did not reshape the overall molar metabolome composition: only four abundant metabolites (absolute concentration > 2 mM in any carbon source) changed more than 5-fold between carbon sources: 6-phosphogluconate (12), FBP, and dTTP (13) were highest in glucose-fed ”
Consistent Estimation of Gibbs Energy Using Component Contributions (2013, background): “This is an important improvement as standard error has previously been shown to affect predictions made based on reaction Gibbs energy estimates [6,8,10].”
Using Genome-Scale Models to Predict Biological Capabilities (2015, positive): “Intracellular quantitative metabolomics combined with reaction free energies can discern condition-speciﬁc reaction directionalities (Henry et al., 2007).”
A genome-scale metabolic reconstruction for Escherichia coli K-12 MG1655 that accounts for 1260 ORFs and thermodynamic information (2007, background): “Conversely, the thermodynamic properties represent physicochemical limitations that can be estimated and taken into account (Beard et al, 2002; Kümmel et al, 2006a; Henry et al, 2007).”
Basic and applied uses of genome-scale metabolic network reconstructions of Escherichia coli (2013, methodology): “…loops that violate energy balance (Ederer and Gilles, 2007; Fleming et al , 2009), by reﬁning reaction directionality, and by deﬁning allowable ﬂux ranges via calculation of the in vivo change of Gibbs free energyof reactions (Ku¨mmel et al , 2006; Henry et al , 2007; Flamholz et al , 2012).”