{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,26]],"date-time":"2026-09-26T10:03:42Z","timestamp":1790417022006,"version":"4.1.0"},"reference-count":50,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2017,1,9]],"date-time":"2017-01-09T00:00:00Z","timestamp":1483920000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"ANR Hycklock"},{"name":"ERASMUS EU METALIC"},{"name":"NII MOU Internship"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Background: The modeling of Biological Regulatory Networks (BRNs) relies on background knowledge, deriving either from literature and\/or the analysis of biological observations. However, with the development of high-throughput data, there is a growing need for methods that automatically generate admissible models. Methods: Our research aim is to provide a logical approach to infer BRNs based on given time series data and known influences among genes. Results: We propose a new methodology for models expressed through a timed extension of the automata networks (well suited for biological systems). The main purpose is to have a resulting network as consistent as possible with the observed datasets. Conclusion: The originality of our work is three-fold: (i) identifying the sign of the interaction; (ii) the direct integration of quantitative time delays in the learning approach; and (iii) the identification of the qualitative discrete levels that lead to the systems\u2019 dynamics. We show the benefits of such an automatic approach on dynamical biological models, the DREAM4(in silico) and DREAM8 (breast cancer) datasets, popular reverse-engineering challenges, in order to discuss the precision and the computational performances of our modeling method.<\/jats:p>","DOI":"10.3390\/a10010008","type":"journal-article","created":{"date-parts":[[2017,1,9]],"date-time":"2017-01-09T11:03:23Z","timestamp":1483959803000},"page":"8","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Modeling Delayed Dynamics in Biological Regulatory Networks from Time Series Data"],"prefix":"10.3390","volume":"10","author":[{"given":"Emna","family":"Ben Abdallah","sequence":"first","affiliation":[{"name":"IRCCyN UMR CNRS 6597 (Institut de Recherche en Communications et Cybern\u00e9tique de Nantes), \u00c9cole Centrale de Nantes, 1 rue de la No\u00eb, 44321 Nantes, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tony","family":"Ribeiro","sequence":"additional","affiliation":[{"name":"IRCCyN UMR CNRS 6597 (Institut de Recherche en Communications et Cybern\u00e9tique de Nantes), \u00c9cole Centrale de Nantes, 1 rue de la No\u00eb, 44321 Nantes, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Morgan","family":"Magnin","sequence":"additional","affiliation":[{"name":"IRCCyN UMR CNRS 6597 (Institut de Recherche en Communications et Cybern\u00e9tique de Nantes), \u00c9cole Centrale de Nantes, 1 rue de la No\u00eb, 44321 Nantes, France"},{"name":"National Institute of Informatics, 2-1-2 Hitotsubashi, Chiyoda-ku, Tokyo 101-8430, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Olivier","family":"Roux","sequence":"additional","affiliation":[{"name":"IRCCyN UMR CNRS 6597 (Institut de Recherche en Communications et Cybern\u00e9tique de Nantes), \u00c9cole Centrale de Nantes, 1 rue de la No\u00eb, 44321 Nantes, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Katsumi","family":"Inoue","sequence":"additional","affiliation":[{"name":"National Institute of Informatics, 2-1-2 Hitotsubashi, Chiyoda-ku, Tokyo 101-8430, Japan"},{"name":"Department of Computer Science, Tokyo Institute of Technology, 2-12-1 Oookayama, Meguro-ku, Tokyo 152-8552, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2017,1,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1038\/498255a","article-title":"Biology: The big challenges of big data","volume":"498","author":"Marx","year":"2013","journal-title":"Nature"},{"key":"ref_2","first-page":"69","article-title":"Scientific perspectivism: A philosopher of science\u2019s response to the challenge of big data biology","volume":"43","author":"Callebaut","year":"2012","journal-title":"Stud. 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