Network-level analysis of metabolic regulation in the human red blood cell using random sampling and singular value decomposition |
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Authors: | Christian L Barrett Nathan D Price Bernhard O Palsson |
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Institution: | (1) Bioengineering Department, University of California – San Diego, 9500 Gilman Drive, La Jolla, California 92093-0412, USA;(2) Institute for Systems Biology, 1441 North 34th Street, Seattle, WA 98103, USA |
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Abstract: | Background Extreme pathways (ExPas) have been shown to be valuable for studying the functions and capabilities of metabolic networks
through characterization of the null space of the stoichiometric matrix (S). Singular value decomposition (SVD) of the ExPa matrix P has previously been used to characterize the metabolic regulatory problem in the human red blood cell (hRBC) from a network
perspective. The calculation of ExPas is NP-hard, and for genome-scale networks the computation of ExPas has proven to be
infeasible. Therefore an alternative approach is needed to reveal regulatory properties of steady state solution spaces of
genome-scale stoichiometric matrices. |
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