References of "Palsson, B. O"
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See detailA community-driven global reconstruction of human metabolism.
Thiele, Ines UL; Swainston, N.; Fleming, Ronan MT UL et al

in Nature Biotechnology (2013), 31

Multiple models of human metabolism have been reconstructed, but each represents only a subset of our knowledge. Here we describe Recon 2, a community-driven, consensus ‘metabolic reconstruction’, which ... [more ▼]

Multiple models of human metabolism have been reconstructed, but each represents only a subset of our knowledge. Here we describe Recon 2, a community-driven, consensus ‘metabolic reconstruction’, which is the most comprehensive representation of human metabolism that is applicable to computational modeling. Compared with its predecessors, the reconstruction has improved topological and functional features, including ~2× more reactions and ~1.7× more unique metabolites. Using Recon 2 we predicted changes in metabolite biomarkers for 49 inborn errors of metabolism with 77% accuracy when compared to experimental data. Mapping metabolomic data and drug information onto Recon 2 demonstrates its potential for integrating and analyzing diverse data types. Using protein expression data, we automatically generated a compendium of 65 cell type–specific models, providing a basis for manual curation or investigation of cell-specific metabolic properties. Recon 2 will facilitate many future biomedical studies and is freely available at http://humanmetabolism.org/. [less ▲]

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See detailA variational principle for computing nonequilibrium fluxes and potentials in genome-scale biochemical networks
Fleming, Ronan MT UL; Maes, C. M.; Saunders, M. A. et al

in Journal of Theoretical Biology (2012), 292

We derive a convex optimization problem on a steady-state no nequilibrium network of biochemical reactions, with the property that energy conservation and the second law of thermodynamics both hold at the ... [more ▼]

We derive a convex optimization problem on a steady-state no nequilibrium network of biochemical reactions, with the property that energy conservation and the second law of thermodynamics both hold at the problem solution. This suggests a new variational principle for biochemical networks that can be implemented in a computationally tractable manner. We derive the Lagrange dual of the optimization problem and use strong duality to demonstrate that a biochemical analogue of Tellegen’s theorem holds at optimality. Each optimal flux is dependent on a free parameter that we relate to an elementary kinetic parameter when mass action kinetics is assumed. [less ▲]

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