{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,10,1]],"date-time":"2023-10-01T17:21:15Z","timestamp":1696180875948},"reference-count":59,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2016,10,2]],"date-time":"2016-10-02T00:00:00Z","timestamp":1475366400000},"content-version":"vor","delay-in-days":1375,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/3.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2013,2,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Motivation: Genome-wide fitness is an emerging type of high-throughput biological data generated for individual organisms by creating libraries of knockouts, subjecting them to broad ranges of environmental conditions, and measuring the resulting clone-specific fitnesses. Since fitness is an organism-scale measure of gene regulatory network behaviour, it may offer certain advantages when insights into such phenotypical and functional features are of primary interest over individual gene expression. Previous works have shown that genome-wide fitness data can be used to uncover novel gene regulatory interactions, when compared with results of more conventional gene expression analysis. Yet, to date, few algorithms have been proposed for systematically using genome-wide mutant fitness data for gene regulatory network inference.<\/jats:p>\n               <jats:p>Results: In this article, we describe a model and propose an inference algorithm for using fitness data from knockout libraries to identify underlying gene regulatory networks. Unlike most prior methods, the presented approach captures not only structural, but also dynamical and non-linear nature of biomolecular systems involved. A state\u2013space model with non-linear basis is used for dynamically describing gene regulatory networks. Network structure is then elucidated by estimating unknown model parameters. Unscented Kalman filter is used to cope with the non-linearities introduced in the model, which also enables the algorithm to run in on-line mode for practical use. Here, we demonstrate that the algorithm provides satisfying results for both synthetic data as well as empirical measurements of GAL network in yeast Saccharomyces cerevisiae and TyrR\u2013LiuR network in bacteria Shewanella oneidensis.<\/jats:p>\n               <jats:p>Availability: MATLAB code and datasets are available to download at http:\/\/www.duke.edu\/\u223clw174\/Fitness.zip and http:\/\/genomics.lbl.gov\/supplemental\/fitness-bioinf\/<\/jats:p>\n               <jats:p>Contact: \u00a0wangx@ee.columbia.edu or mssamoilov@lbl.gov<\/jats:p>\n               <jats:p>Supplementary information: \u00a0Supplementary data are available at Bioinformatics online<\/jats:p>","DOI":"10.1093\/bioinformatics\/bts634","type":"journal-article","created":{"date-parts":[[2012,12,28]],"date-time":"2012-12-28T01:55:13Z","timestamp":1356659713000},"page":"338-346","source":"Crossref","is-referenced-by-count":9,"title":["Inference of gene regulatory networks from genome-wide knockout fitness data"],"prefix":"10.1093","volume":"29","author":[{"given":"Liming","family":"Wang","sequence":"first","affiliation":[{"name":"1 Department of Electrical and Computer Engineering, Duke University, Durham, NC 27708, USA, 2Department of Electrical Engineering, Columbia University, New York, NY 10027, USA, 3Department of Bioengineering, University of California Berkeley, Berkeley, CA 94720, USA and 4Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA"}]},{"given":"Xiaodong","family":"Wang","sequence":"additional","affiliation":[{"name":"1 Department of Electrical and Computer Engineering, Duke University, Durham, NC 27708, USA, 2Department of Electrical Engineering, Columbia University, New York, NY 10027, USA, 3Department of Bioengineering, University of California Berkeley, Berkeley, CA 94720, USA and 4Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA"}]},{"given":"Adam P.","family":"Arkin","sequence":"additional","affiliation":[{"name":"1 Department of Electrical and Computer Engineering, Duke University, Durham, NC 27708, USA, 2Department of Electrical Engineering, Columbia University, New York, NY 10027, USA, 3Department of Bioengineering, University of California Berkeley, Berkeley, CA 94720, USA and 4Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA"},{"name":"1 Department of Electrical and Computer Engineering, Duke University, Durham, NC 27708, USA, 2Department of Electrical Engineering, Columbia University, New York, NY 10027, USA, 3Department of Bioengineering, University of California Berkeley, Berkeley, CA 94720, USA and 4Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA"}]},{"given":"Michael S.","family":"Samoilov","sequence":"additional","affiliation":[{"name":"1 Department of Electrical and Computer Engineering, Duke University, Durham, NC 27708, USA, 2Department of Electrical Engineering, Columbia University, New York, NY 10027, USA, 3Department of Bioengineering, University of California Berkeley, Berkeley, CA 94720, USA and 4Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA"}]}],"member":"286","published-online":{"date-parts":[[2012,12,27]]},"reference":[{"key":"2023012810230709400_bts634-B1","first-page":"17","article-title":"Identification of genetic networks from a small number of gene expression patterns under the Boolean network model","volume-title":"Pacific Symposium on Biocomputing","author":"Akutsu","year":"1999"},{"key":"2023012810230709400_bts634-B2","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1093\/bioinformatics\/bti014","article-title":"A Bayesian approach to reconstructing genetic regulatory networks with hidden factors","volume":"21","author":"Beal","year":"2005","journal-title":"Bioinformatics"},{"key":"2023012810230709400_bts634-B3","doi-asserted-by":"crossref","first-page":"e9","DOI":"10.1371\/journal.pbio.0020009","article-title":"Similarities and differences in genome-wide expression data of six organisms","volume":"2","author":"Bergmann","year":"2003","journal-title":"PLoS Biol."},{"key":"2023012810230709400_bts634-B4","doi-asserted-by":"crossref","first-page":"R36","DOI":"10.1186\/gb-2006-7-5-r36","article-title":"The Inferelator: an algorithm for learning parsimonious regulatory networks from systems-biology data sets de novo","volume":"7","author":"Bonneau","year":"2006","journal-title":"Genome Biol."},{"key":"2023012810230709400_bts634-B5","doi-asserted-by":"crossref","first-page":"S85","DOI":"10.1098\/rsif.2008.0132.focus","article-title":"Boolean network models of cellular regulation: prospects and limitations","volume":"5","author":"Bornholdt","year":"2008","journal-title":"J. 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