{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,28]],"date-time":"2025-10-28T18:27:11Z","timestamp":1761676031308},"reference-count":30,"publisher":"Oxford University Press (OUP)","issue":"7","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2013,4,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Motivation: Computational modelling of the dynamics of gene regulatory networks is a central task of systems biology. For networks of small\/medium scale, the dominant paradigm is represented by systems of coupled non-linear ordinary differential equations (ODEs). ODEs afford great mechanistic detail and flexibility, but calibrating these models to data is often an extremely difficult statistical problem.<\/jats:p>\n               <jats:p>Results: Here, we develop a general statistical inference framework for stochastic transcription\u2013translation networks. We use a coarse-grained approach, which represents the system as a network of stochastic (binary) promoter and (continuous) protein variables. We derive an exact inference algorithm and an efficient variational approximation that allows scalable inference and learning of the model parameters. We demonstrate the power of the approach on two biological case studies, showing that the method allows a high degree of flexibility and is capable of testable novel biological predictions.<\/jats:p>\n               <jats:p>Availability and implementation: \u00a0http:\/\/homepages.inf.ed.ac.uk\/gsanguin\/software.html.<\/jats:p>\n               <jats:p>Supplementary information: \u00a0Supplementary data are available at Bioinformatics online.<\/jats:p>\n               <jats:p>Contact: \u00a0G.Sanguinetti@ed.ac.uk<\/jats:p>","DOI":"10.1093\/bioinformatics\/btt069","type":"journal-article","created":{"date-parts":[[2013,2,14]],"date-time":"2013-02-14T04:21:04Z","timestamp":1360815664000},"page":"910-916","source":"Crossref","is-referenced-by-count":34,"title":["Hybrid regulatory models: a statistically tractable approach to model regulatory network dynamics"],"prefix":"10.1093","volume":"29","author":[{"given":"Andrea","family":"Ocone","sequence":"first","affiliation":[{"name":"1 School of Informatics, University of Edinburgh, Edinburgh EH8 9AB, 2SynthSys\u2014Systems and Synthetic Biology, University of Edinburgh, Edinburgh EH9 3JD and 3School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3JR, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andrew J.","family":"Millar","sequence":"additional","affiliation":[{"name":"1 School of Informatics, University of Edinburgh, Edinburgh EH8 9AB, 2SynthSys\u2014Systems and Synthetic Biology, University of Edinburgh, Edinburgh EH9 3JD and 3School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3JR, UK"},{"name":"1 School of Informatics, University of Edinburgh, Edinburgh EH8 9AB, 2SynthSys\u2014Systems and Synthetic Biology, University of Edinburgh, Edinburgh EH9 3JD and 3School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3JR, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guido","family":"Sanguinetti","sequence":"additional","affiliation":[{"name":"1 School of Informatics, University of Edinburgh, Edinburgh EH8 9AB, 2SynthSys\u2014Systems and Synthetic Biology, University of Edinburgh, Edinburgh EH9 3JD and 3School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3JR, UK"},{"name":"1 School of Informatics, University of Edinburgh, Edinburgh EH8 9AB, 2SynthSys\u2014Systems and Synthetic Biology, University of Edinburgh, Edinburgh EH9 3JD and 3School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3JR, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2013,2,13]]},"reference":[{"key":"2023020303304694200_btt069-B1","doi-asserted-by":"crossref","first-page":"1277","DOI":"10.1093\/bioinformatics\/btr113","article-title":"Large-scale learning of combinatorial transcriptional dynamics from gene expression","volume":"27","author":"Asif","year":"2011","journal-title":"Bioinformatics"},{"key":"2023020303304694200_btt069-B2","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1016\/j.cell.2009.01.055","article-title":"A yeast synthetic network for in vivo assessment of reverse-engineering and modeling approaches","volume":"137","author":"Cantone","year":"2009","journal-title":"Cell"},{"key":"2023020303304694200_btt069-B3","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1038\/35002125","article-title":"A synthetic oscillatory network of transcriptional regulators","volume":"403","author":"Elowitz","year":"2000","journal-title":"Nature"},{"key":"2023020303304694200_btt069-B4","doi-asserted-by":"crossref","first-page":"1183","DOI":"10.1126\/science.1070919","article-title":"Stochastic gene expression in a single cell","volume":"297","author":"Elowitz","year":"2002","journal-title":"Science"},{"key":"2023020303304694200_btt069-B5","doi-asserted-by":"crossref","first-page":"3167","DOI":"10.1073\/pnas.1200355109","article-title":"Arabidopsis circadian clock protein, TOC1, is a DNA-binding transcription factor","volume":"109","author":"Gendron","year":"2012","journal-title":"Proc. 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