{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,24]],"date-time":"2025-10-24T08:12:58Z","timestamp":1761293578754},"reference-count":8,"publisher":"Oxford University Press (OUP)","issue":"18","license":[{"start":{"date-parts":[[2017,6,29]],"date-time":"2017-06-29T00:00:00Z","timestamp":1498694400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/about_us\/legal\/notices"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017,9,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Due to their universal applicability, global stochastic optimization methods are popular for designing improvements of biochemical networks. The drawbacks of global stochastic optimization methods are: (i) no guarantee of finding global optima, (ii) no clear optimization run termination criteria and (iii) no criteria to detect stagnation of an optimization run. The impact of these drawbacks can be partly compensated by manual work that becomes inefficient when the solution space is large due to combinatorial explosion of adjustable parameters or for other reasons.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>SpaceScanner uses parallel optimization runs for automatic termination of optimization tasks in case of consensus and consecutively applies a pre-defined set of global stochastic optimization methods in case of stagnation in the currently used method. Automatic scan of adjustable parameter combination subsets for best objective function values is possible with a summary file of ranked solutions.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>https:\/\/github.com\/atiselsts\/spacescanner.<\/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\/btx363","type":"journal-article","created":{"date-parts":[[2017,6,2]],"date-time":"2017-06-02T19:17:52Z","timestamp":1496431072000},"page":"2966-2967","source":"Crossref","is-referenced-by-count":14,"title":["SpaceScanner: COPASI wrapper for automated management of global stochastic optimization experiments"],"prefix":"10.1093","volume":"33","author":[{"given":"Atis","family":"Elsts","sequence":"first","affiliation":[{"name":"Department of Electrical and Electronic Engineering, University of Bristol, Bristol, UK"}]},{"given":"Agris","family":"Pentjuss","sequence":"additional","affiliation":[{"name":"Institute of Microbiology and Biotechnology, University of Latvia, Riga, Latvia"}]},{"given":"Egils","family":"Stalidzans","sequence":"additional","affiliation":[{"name":"Institute of Microbiology and Biotechnology, University of Latvia, Riga, Latvia"}]}],"member":"286","published-online":{"date-parts":[[2017,6,29]]},"reference":[{"key":"2023020206424372300_btx363-B1","doi-asserted-by":"crossref","first-page":"409","DOI":"10.1007\/978-1-4419-7210-1_24","article-title":"Global optimization in systems biology: Stochastic methods and their applications","volume":"736","author":"Balsa-Canto","year":"2012","journal-title":"Adv. Exp. Med. Biol"},{"key":"2023020206424372300_btx363-B2","doi-asserted-by":"crossref","first-page":"3067","DOI":"10.1093\/bioinformatics\/btl485","article-title":"COPASI\u2013a COmplex PAthway SImulator","volume":"22","author":"Hoops","year":"2006","journal-title":"Bioinformatics"},{"key":"2023020206424372300_btx363-B3","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1186\/1752-0509-6-91","article-title":"Condor-COPASI: high-throughput computing for biochemical networks","volume":"6","author":"Kent","year":"2012","journal-title":"BMC Syst. 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Bioinforma"},{"key":"2023020206424372300_btx363-B8","first-page":"1","article-title":"Automatic termination of parallel optimization runs of stochastic global optimization methods in consensus or stagnation cases","volume":"1","author":"Sulins","year":"2012","journal-title":"Biosyst. Inf. Technol"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/33\/18\/2966\/49040919\/bioinformatics_33_18_2966.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/33\/18\/2966\/49040919\/bioinformatics_33_18_2966.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,2]],"date-time":"2023-02-02T06:43:32Z","timestamp":1675320212000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/33\/18\/2966\/3902990"}},"subtitle":[],"editor":[{"given":"Jonathan","family":"Wren","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2017,6,29]]},"references-count":8,"journal-issue":{"issue":"18","published-print":{"date-parts":[[2017,9,15]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btx363","relation":{},"ISSN":["1367-4803","1367-4811"],"issn-type":[{"value":"1367-4803","type":"print"},{"value":"1367-4811","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2017,9,15]]},"published":{"date-parts":[[2017,6,29]]}}}