Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 452935
Title Inferring the Gene Network Underlying the Branching of Tomato Inflorescence
Author(s) Astola, L.; Stigter, J.D.; Dijk, A.D.J. van; Daelen, R. van; Molenaar, J.
Source PLoS ONE 9 (2014)4. - ISSN 1932-6203 - 7 p.
DOI https://doi.org/10.1371/journal.pone.0089689
Department(s) Mathematical and Statistical Methods - Biometris
BIOS Applied Bioinformatics
PE&RC
Publication type Refereed Article in a scientific journal
Publication year 2014
Keyword(s) flowering time - reproductive structure - regulatory networks - abscission zone - inference - meristem - lycopersicon - falsiflora - jointless
Abstract The architecture of tomato inflorescence strongly affects flower production and subsequent crop yield. To understand the genetic activities involved, insight into the underlying network of genes that initiate and control the sympodial growth in the tomato is essential. In this paper, we show how the structure of this network can be derived from available data of the expressions of the involved genes. Our approach starts from employing biological expert knowledge to select the most probable gene candidates behind branching behavior. To find how these genes interact, we develop a stepwise procedure for computational inference of the network structure. Our data consists of expression levels from primary shoot meristems, measured at different developmental stages on three different genotypes of tomato. With the network inferred by our algorithm, we can explain the dynamics corresponding to all three genotypes simultaneously, despite their apparent dissimilarities. We also correctly predict the chronological order of expression peaks for the main hubs in the network. Based on the inferred network, using optimal experimental design criteria, we are able to suggest an informative set of experiments for further investigation of the mechanisms underlying branching behavior
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