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dc.contributor.authorMinas, Giorgos
dc.contributor.authorJenkins, Dafyd J.
dc.contributor.authorRand, David A.
dc.contributor.authorFinkenstädt, Bärbel
dc.identifier.citationMinas , G , Jenkins , D J , Rand , D A & Finkenstädt , B 2017 , ' Inferring transcriptional logic from multiple dynamic experiments ' , Bioinformatics , vol. 33 , no. 21 , pp. 3437-3444 .
dc.identifier.otherPURE: 255153680
dc.identifier.otherPURE UUID: 41a3c19f-69cd-411f-85aa-a422671dd3e7
dc.identifier.otherScopus: 85050446142
dc.identifier.otherPubMed: 28666320
dc.identifier.otherORCID: /0000-0001-7953-706X/work/47136662
dc.descriptionThis work was supported by the Biotechnology and Biological Sciences Research Council [BB/F005806/1, BB/K003097/1], the Engineering and Physical Sciences Research Council [EP/C544587/1 to DAR] and the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement n° 305564.en
dc.description.abstractMotivation: The availability of more data of dynamic gene expression under multiple experimental conditions provides new information that makes the key goal of identifying not only the transcriptional regulators of a gene but also the underlying logical structure attainable. Results: We propose a novel method for inferring transcriptional regulation using a simple, yet biologically interpretable, model to find the logic by which a set of candidate genes and their associated transcription factors (TFs) regulate the transcriptional process of a gene of interest. Our dynamic model links the mRNA transcription rate of the target gene to the activation states of the TFs assuming that these interactions are consistent across multiple experiments and over time. A trans-dimensional Markov Chain Monte Carlo (MCMC) algorithm is used to efficiently sample the regulatory logic under different combinations of parents and rank the estimated models by their posterior probabilities. We demonstrate and compare our methodology with other methods using simulation examples and apply it to a study of transcriptional regulation of selected target genes of Arabidopsis Thaliana from microarray time series data obtained under multiple biotic stresses. We show that our method is able to detect complex regulatory interactions that are consistent under multiple experimental conditions. Availability and implementation: Programs are written in MATLAB and Statistics Toolbox Release 2016b, The MathWorks, Inc., Natick, Massachusetts, United States and are available on GitHub and at
dc.rights© The Author 2017. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.en
dc.subjectQA Mathematicsen
dc.subjectQH301 Biologyen
dc.subjectMolecular Biologyen
dc.subjectComputational Theory and Mathematicsen
dc.subjectComputer Science Applicationsen
dc.subjectComputational Mathematicsen
dc.subjectStatistics and Probabilityen
dc.titleInferring transcriptional logic from multiple dynamic experimentsen
dc.typeJournal articleen
dc.description.versionPublisher PDFen
dc.contributor.institutionUniversity of St Andrews.Statisticsen
dc.description.statusPeer revieweden

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