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Descripción
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| We introduce a novel method to represent time independent scalar data sets as complex networks. Application of our method to investigate gene expression in the response to osmotic stress of Arabidopsis thaliana revealed that the most important genes for the stress response turned out to be the nodes with highest centrality in the reconstructed networks. To validate the obtained predictions we performed a target experiment in which the candidate genes were artificially induced and plant phenotypes verified under osmotic stress. The joint application of the network reconstruction method and of the in vivo experiments allowed identifying 15 previously unknown key genes, and provided models of their mutual relationships. This novel representation extends the use of graph theory to data sets hitherto considered outside of the realm of its application, vastly simplifying the characterization of their underlying structure. | |
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Internacional
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Si |
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Entidad
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ICREA |
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Lugar
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Barcelona |
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Páginas
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Referencia/URL
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Tipo de publicación
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Comunicación en Congreso |