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dc.contributor.authorArboleda Rivera, Juan Camilo-
dc.contributor.authorRodríguez Rey, Boris Anghelo-
dc.contributor.authorMachado Rodríguez, Gloria-
dc.contributor.authorGutiérrez, Jayson-
dc.date.accessioned2022-09-19T19:35:40Z-
dc.date.available2022-09-19T19:35:40Z-
dc.date.issued2022-
dc.identifier.citationArboleda-Rivera JC, Machado-Rodr´ıguez G, Rodrı´guez BA, Gutie´rrez J (2022) Elucidating multi-input processing 3-node gene regulatory network topologies capable of generating striped gene expression patterns. PLoS Comput Biol 18(2): e1009704. https://doi.org/10.1371/journal. pcbi.1009704spa
dc.identifier.issn1553-734X-
dc.identifier.urihttps://hdl.handle.net/10495/30700-
dc.description.abstractABSTRACT: A central problem in developmental and synthetic biology is understanding the mechanisms by which cells in a tissue or a Petri dish process external cues and transform such information into a coherent response, e.g., a terminal differentiation state. It was long believed that this type of positional information could be entirely attributed to a gradient of concentration of a specific signaling molecule (i.e., a morphogen). However, advances in experimental methodologies and computer modeling have demonstrated the crucial role of the dynamics of a cell’s gene regulatory network (GRN) in decoding the information carried by the morphogen, which is eventually translated into a spatial pattern. This morphogen interpretation mechanism has gained much attention in systems biology as a tractable system to investigate the emergent properties of complex genotype-phenotype maps. In this study, we apply a Markov chain Monte Carlo (MCMC)-like algorithm to probe the design space of three-node GRNs with the ability to generate a band-like expression pattern (target phenotype) in the middle of an arrangement of 30 cells, which resemble a simple (1-D) morphogenetic field in a developing embryo. Unlike most modeling studies published so far, here we explore the space of GRN topologies with nodes having the potential to perceive the same input signal differently. This allows for a lot more flexibility during the search space process, and thus enables us to identify a larger set of potentially interesting and realizable morphogen interpretation mechanisms. Out of 2061 GRNs selected using the search space algorithm, we found 714 classes of network topologies that could correctly interpret the morphogen. Notably, the main network motif that generated the target phenotype in response to the input signal was the type 3 Incoherent Feed-Forward Loop (I3-FFL), which agrees with previous theoretical expectations and experimental observations. Particularly, compared to a previously reported pattern forming GRN topologies, we have uncovered a great variety of novel network designs, some of which might be worth inquiring through synthetic biology methodologies to test for the ability of network design with minimal regulatory complexity to interpret a developmental cue robustly.spa
dc.format.extent21spa
dc.format.mimetypeapplication/pdfspa
dc.language.isoengspa
dc.publisherPublic Library of Sciencespa
dc.type.hasversioninfo:eu-repo/semantics/publishedVersionspa
dc.rightsinfo:eu-repo/semantics/openAccessspa
dc.titleElucidating multi-input processing 3-node gene regulatory network topologies capable of generating striped gene expression patternsspa
dc.typeinfo:eu-repo/semantics/articlespa
dc.publisher.groupFundamentos y Enseñanza de la Física y los Sistemas Dinámicosspa
dc.identifier.doi10.1371/journal.pcbi.1009704-
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85spa
dc.rights.accessrightshttp://purl.org/coar/access_right/c_abf2spa
dc.identifier.eissn1553-7358-
oaire.citationtitlePLoS Computational Biologyspa
oaire.citationstartpage1spa
oaire.citationendpage21spa
oaire.citationvolume18spa
oaire.citationissue2spa
dc.rights.creativecommonshttps://creativecommons.org/licenses/by/4.0/spa
oaire.fundernameUniversidad de Antioquia. Vicerrectoría de investigación. Comité para el Desarrollo de la Investigación - CODIspa
dc.publisher.placeSan Francisco, Estados Unidosspa
dc.type.coarhttp://purl.org/coar/resource_type/c_2df8fbb1spa
dc.type.redcolhttps://purl.org/redcol/resource_type/ARTspa
dc.type.localArtículo de investigaciónspa
dc.subject.decsBiología Evolutiva-
dc.subject.decsDevelopmental Biology-
dc.subject.decsBiología Computacional-
dc.subject.decsComputational Biology-
dc.subject.decsRedes Reguladoras de Genes-
dc.subject.decsGene Regulatory Networks-
dc.subject.decsBiología sintética-
dc.subject.decsSynthetic Biology-
dc.subject.lembExpresión Génica-
dc.subject.lembGene expression-
dc.description.researchgroupidCOL0139559spa
oaire.awardnumber2017-14367spa
dc.relation.ispartofjournalabbrevPLoS Comput Biolspa
Aparece en las colecciones: Artículos de Revista en Ciencias Exactas y Naturales

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