{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,22]],"date-time":"2025-02-22T00:45:02Z","timestamp":1740185102948,"version":"3.37.3"},"reference-count":32,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2017,9,13]],"date-time":"2017-09-13T00:00:00Z","timestamp":1505260800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/about_us\/legal\/notices"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["81620108029"],"award-info":[{"award-number":["81620108029"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,2,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>One of the long-expected goals of genome-scale metabolic modelling is to evaluate the influence of the perturbed enzymes on flux distribution. Both ordinary differential equation (ODE) models and constraint-based models, like Flux balance analysis (FBA), lack the capacity to perform metabolic control analysis (MCA) for large-scale networks.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>In this study, we developed a hyper-cube shrink algorithm (HCSA) to incorporate the enzymatic properties into the FBA model by introducing a pseudo reaction V constrained by enzymatic parameters. Our algorithm uses the enzymatic information quantitatively rather than qualitatively. We first demonstrate the concept by applying HCSA to a simple three-node network, whereby we obtained a good correlation between flux and enzyme abundance. We then validate its prediction by comparison with ODE and with a synthetic network producing voilacein and analogues in Saccharomyces cerevisiae. We show that HCSA can mimic the state-state results of ODE. Finally, we show its capability of predicting the flux distribution in genome-scale networks by applying it to sporulation in yeast. We show the ability of HCSA to operate without biomass flux and perform MCA to determine rate-limiting reactions.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>Algorithm was implemented by Matlab and C\u2009++. The code is available at https:\/\/github.com\/kekegg\/HCSA.<\/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\/btx574","type":"journal-article","created":{"date-parts":[[2017,9,12]],"date-time":"2017-09-12T11:09:45Z","timestamp":1505214585000},"page":"502-510","source":"Crossref","is-referenced-by-count":1,"title":["Genome-scale fluxes predicted under the guidance of enzyme abundance using a novel hyper-cube shrink algorithm"],"prefix":"10.1093","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9572-878X","authenticated-orcid":false,"given":"Zhengwei","family":"Xie","sequence":"first","affiliation":[{"name":"Department of Pharmacology and Institute of Systems Biomedicine, School of Basic Medical Sciences, Peking University, Beijing, China"}]},{"given":"Tianyu","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Life Science, Peking University, Beijing, China"}]},{"given":"Qi","family":"Ouyang","sequence":"additional","affiliation":[{"name":"Condensed Matter Physics, School of Physics, Center for Quantitative Biology and Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China"}]}],"member":"286","published-online":{"date-parts":[[2017,9,13]]},"reference":[{"key":"2023012712322231800_btx574-B1","doi-asserted-by":"crossref","first-page":"e1002518.","DOI":"10.1371\/journal.pcbi.1002518","article-title":"Reconstruction of genome-scale active metabolic networks for 69 human cell types and 16 cancer types using INIT","volume":"8","author":"Agren","year":"2012","journal-title":"PLoS Comput. 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