{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,13]],"date-time":"2025-09-13T16:21:23Z","timestamp":1757780483838,"version":"3.37.3"},"reference-count":40,"publisher":"Oxford University Press (OUP)","issue":"14","license":[{"start":{"date-parts":[[2017,3,7]],"date-time":"2017-03-07T00:00:00Z","timestamp":1488844800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/about_us\/legal\/notices"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["MCB-0958172, MCB-0946595 and MCB-1517588","MCB 1411672","DEB-1241046"],"award-info":[{"award-number":["MCB-0958172, MCB-0946595 and MCB-1517588","MCB 1411672","DEB-1241046"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017,7,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Most metabolic pathways contain more reactions than metabolites and therefore have a wide stoichiometric matrix that corresponds to infinitely many possible flux distributions that are perfectly compatible with the dynamics of the metabolites in a given dataset. This under-determinedness poses a challenge for the quantitative characterization of flux distributions from time series data and thus for the design of adequate, predictive models. Here we propose a method that reduces the degrees of freedom in a stepwise manner and leads to a dynamic flux distribution that is, in a statistical sense, likely to be close to the true distribution.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>We applied the proposed method to the lignin biosynthesis pathway in switchgrass. The system consists of 16 metabolites and 23 enzymatic reactions. It has seven degrees of freedom and therefore admits a large space of dynamic flux distributions that all fit a set of metabolic time series data equally well. The proposed method reduces this space in a systematic and biologically reasonable manner and converges to a likely dynamic flux distribution in just a few iterations. The estimated solution and the true flux distribution, which is known in this case, show excellent agreement and thereby lend support to the method.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and Implementation<\/jats:title>\n                  <jats:p>The computational model was implemented in MATLAB (version R2014a, The MathWorks, Natick, MA). The source code is available at https:\/\/github.gatech.edu\/VoitLab\/Stepwise-Inference-of-Likely-Dynamic-Flux-Distributions and www.bst.bme.gatech.edu\/research.php.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Supplementary information<\/jats:title>\n                  <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btx126","type":"journal-article","created":{"date-parts":[[2017,3,3]],"date-time":"2017-03-03T20:12:04Z","timestamp":1488571924000},"page":"2165-2172","source":"Crossref","is-referenced-by-count":6,"title":["Stepwise inference of likely dynamic flux distributions from metabolic time series data"],"prefix":"10.1093","volume":"33","author":[{"given":"Mojdeh","family":"Faraji","sequence":"first","affiliation":[{"name":"Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, USA"}]},{"given":"Eberhard O","family":"Voit","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, USA"}]}],"member":"286","published-online":{"date-parts":[[2017,3,7]]},"reference":[{"volume-title":"Regression and the Moore-Penrose Pseudoinverse. 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