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Scatter search applied to the inference of a development gene network

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dc.contributor.author Abdol, Amir Masoud
dc.contributor.author Cicin Sain, Damjan
dc.contributor.author Kaandorp, Jaap A.
dc.contributor.author Crombach, Anton
dc.date.accessioned 2023-02-13T08:54:25Z
dc.date.available 2023-02-13T08:54:25Z
dc.date.issued 2017
dc.identifier.citation Abdol AM, Cicin-Sain D, Kaandorp JA, Crombach A. Scatter search applied to the inference of a development gene network. Computation. 2017 Jun;5(2):22. DOI: 10.3390/computation5020022
dc.identifier.issn 2079-3197
dc.identifier.uri http://hdl.handle.net/10230/55731
dc.description.abstract Efficient network inference is one of the challenges of current-day biology. Its application to the study of development has seen noteworthy success, yet a multicellular context, tissue growth, and cellular rearrangements impose additional computational costs and prohibit a wide application of current methods. Therefore, reducing computational cost and providing quick feedback at intermediate stages are desirable features for network inference. Here we propose a hybrid approach composed of two stages: exploration with scatter search and exploitation of intermediate solutions with low temperature simulated annealing. We test the approach on the well-understood process of early body plan development in flies, focusing on the gap gene network. We compare the hybrid approach to simulated annealing, a method of network inference with a proven track record. We find that scatter search performs well at exploring parameter space and that low temperature simulated annealing refines the intermediate results into excellent model fits. From this we conclude that for poorly-studied developmental systems, scatter search is a valuable tool for exploration and accelerates the elucidation of gene regulatory networks.
dc.description.sponsorship We thank Johannes Jaeger for critical feedback and scientific advice. We thankfully acknowledge the computer resources, technical expertise and assistance provided by the Barcelona Supercomputing Center, which is part of the Red Española de Supercomputación. We thank SURFsara (www.surfsara.nl) for the support in using the Lisa Compute Cluster. The Centre for Genomic Regulation (CRG) acknowledges support from the Spanish Ministry of Economy and Competitiveness, ‘Centro de Excelencia Severo Ochoa 2013-2017’, SEV-2012-0208. AC kindly acknowledges Fondation Bettencourt Schueller.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher MDPI
dc.relation.ispartof Computation. 2017 Jun;5(2):22
dc.rights © 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
dc.rights.uri http://creativecommons.org/licenses/by/4.0/
dc.title Scatter search applied to the inference of a development gene network
dc.type info:eu-repo/semantics/article
dc.identifier.doi http://dx.doi.org/10.3390/computation5020022
dc.subject.keyword Network inference
dc.subject.keyword Scatter search
dc.subject.keyword Parallel simulated annealing
dc.subject.keyword Gap gene network
dc.subject.keyword D. melanogaster
dc.relation.projectID info:eu-repo/grantAgreement/ES/3PN/SEV-2012-0208
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/publishedVersion


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