{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,5,15]],"date-time":"2023-05-15T05:47:50Z","timestamp":1684129670398},"reference-count":34,"publisher":"Oxford University Press (OUP)","issue":"12","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2013,6,15]]},"abstract":"<jats:p>Motivation: As RNA interference is becoming a standard method for targeted gene perturbation, computational approaches to reverse engineer parts of biological networks based on measurable effects of RNAi become increasingly relevant. The vast majority of these methods use gene expression data, but little attention has been paid so far to other data types.<\/jats:p>\n               <jats:p>Results: Here we present a method, which can infer gene networks from high-dimensional phenotypic perturbation effects on single cells recorded by time-lapse microscopy. We use data from the Mitocheck project to extract multiple shape, intensity and texture features at each frame. Features from different cells and movies are then aligned along the cell cycle time. Subsequently we use Dynamic Nested Effects Models (dynoNEMs) to estimate parts of the network structure between perturbed genes via a Markov Chain Monte Carlo approach. Our simulation results indicate a high reconstruction quality of this method. A reconstruction based on 22 gene knock downs yielded a network, where all edges could be explained via the biological literature.<\/jats:p>\n               <jats:p>Availability: The implementation of dynoNEMs is part of the Bioconductor R-package nem.<\/jats:p>\n               <jats:p>Contact: \u00a0frohlich@bit.uni-bonn.de<\/jats:p>\n               <jats:p>Supplementary information: \u00a0Supplementary Data are available at Bioinformatics online.<\/jats:p>","DOI":"10.1093\/bioinformatics\/btt179","type":"journal-article","created":{"date-parts":[[2013,4,18]],"date-time":"2013-04-18T01:03:27Z","timestamp":1366247007000},"page":"1534-1540","source":"Crossref","is-referenced-by-count":10,"title":["Learning gene network structure from time laps cell imaging in RNAi Knock downs"],"prefix":"10.1093","volume":"29","author":[{"given":"Henrik","family":"Failmezger","sequence":"first","affiliation":[{"name":"1 \u00a01Computational Biology and Regulatory Networks, Max-Planck Institute for Plant Breeding Research, Carl-von-Linne-Weg 10, 50829 Cologne, Germany and 2Algorithmic Bioinformatics, Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universit\u00e4t Bonn, Dahlmannstr. 2, 53113 Bonn, Germany"}]},{"given":"Paurush","family":"Praveen","sequence":"additional","affiliation":[{"name":"1 \u00a01Computational Biology and Regulatory Networks, Max-Planck Institute for Plant Breeding Research, Carl-von-Linne-Weg 10, 50829 Cologne, Germany and 2Algorithmic Bioinformatics, Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universit\u00e4t Bonn, Dahlmannstr. 2, 53113 Bonn, Germany"}]},{"given":"Achim","family":"Tresch","sequence":"additional","affiliation":[{"name":"1 \u00a01Computational Biology and Regulatory Networks, Max-Planck Institute for Plant Breeding Research, Carl-von-Linne-Weg 10, 50829 Cologne, Germany and 2Algorithmic Bioinformatics, Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universit\u00e4t Bonn, Dahlmannstr. 2, 53113 Bonn, Germany"}]},{"given":"Holger","family":"Fr\u00f6hlich","sequence":"additional","affiliation":[{"name":"1 \u00a01Computational Biology and Regulatory Networks, Max-Planck Institute for Plant Breeding Research, Carl-von-Linne-Weg 10, 50829 Cologne, Germany and 2Algorithmic Bioinformatics, Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universit\u00e4t Bonn, Dahlmannstr. 2, 53113 Bonn, Germany"}]}],"member":"286","published-online":{"date-parts":[[2013,4,17]]},"reference":[{"key":"2023012810441909100_btt179-B1","doi-asserted-by":"crossref","first-page":"6447","DOI":"10.1073\/pnas.0809822106","article-title":"Modeling the temporal interplay of molecular signaling and gene expression by using dynamic nested effects models","volume":"106","author":"Anchang","year":"2009","journal-title":"Proc. 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