{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,26]],"date-time":"2025-10-26T14:05:56Z","timestamp":1761487556134},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"1","content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"published-print":{"date-parts":[[2006,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:sec>\n            <jats:title>Background<\/jats:title>\n            <jats:p>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 (<jats:bold>S<\/jats:bold>). Singular value decomposition (SVD) of the ExPa matrix <jats:bold>P<\/jats:bold> 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.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>We show that the SVD of a matrix (<jats:bold>W<\/jats:bold>) formed of random samples from the steady-state solution space of the hRBC metabolic network gives similar insights into the regulatory properties of the network as was obtained with SVD of <jats:bold>P<\/jats:bold>. This new approach has two main advantages. First, it works with a direct representation of the shape of the metabolic solution space without the confounding factor of a non-uniform distribution of the extreme pathways and second, the SVD procedure can be applied to a very large number of samples, such as will be produced from genome-scale networks.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusion<\/jats:title>\n            <jats:p>These results show that we are now in a position to study the network aspects of the regulatory problem in genome-scale metabolic networks through the use of random sampling.<\/jats:p>\n            <jats:p>\n              <jats:bold>Contact<\/jats:bold>: palsson@ucsd.edu<\/jats:p>\n          <\/jats:sec>","DOI":"10.1186\/1471-2105-7-132","type":"journal-article","created":{"date-parts":[[2006,3,21]],"date-time":"2006-03-21T19:14:23Z","timestamp":1142968463000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Network-level analysis of metabolic regulation in the human red blood cell using random sampling and singular value decomposition"],"prefix":"10.1186","volume":"7","author":[{"given":"Christian L","family":"Barrett","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nathan D","family":"Price","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bernhard O","family":"Palsson","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2006,3,13]]},"reference":[{"key":"871_CR1","doi-asserted-by":"publisher","first-page":"130","DOI":"10.1038\/nrg1769","volume":"7","author":"JL Reed","year":"2006","unstructured":"Reed JL, Famili I, Thiele I, Palsson BO: Towards multidimensional genome annotation. 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