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    Inference of the Genetic Network Regulating Lateral Root Initiation in Arabidopsis thaliana

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    Type
    Article
    Authors
    Muraro, D.
    Voss, U.
    Wilson, M.
    Bennett, M.
    Byrne, H.
    De Smet, I.
    Hodgman, C.
    King, J.
    KAUST Grant Number
    KUK-013-04
    Date
    2013-01
    Permanent link to this record
    http://hdl.handle.net/10754/598616
    
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    Abstract
    Regulation of gene expression is crucial for organism growth, and it is one of the challenges in systems biology to reconstruct the underlying regulatory biological networks from transcriptomic data. The formation of lateral roots in Arabidopsis thaliana is stimulated by a cascade of regulators of which only the interactions of its initial elements have been identified. Using simulated gene expression data with known network topology, we compare the performance of inference algorithms, based on different approaches, for which ready-to-use software is available. We show that their performance improves with the network size and the inclusion of mutants. We then analyze two sets of genes, whose activity is likely to be relevant to lateral root initiation in Arabidopsis, and assess causality of their regulatory interactions by integrating sequence analysis with the intersection of the results of the best performing methods on time series and mutants. The methods applied capture known interactions between genes that are candidate regulators at early stages of development. The network inferred from genes significantly expressed during lateral root formation exhibits distinct scale free, small world and hierarchical properties and the nodes with a high out-degree may warrant further investigation. © 2004-2012 IEEE.
    Citation
    Muraro D, Voss U, Wilson M, Bennett M, Byrne H, et al. (2013) Inference of the Genetic Network Regulating Lateral Root Initiation in Arabidopsis thaliana. IEEE/ACM Transactions on Computational Biology and Bioinformatics 10: 50–60. Available: http://dx.doi.org/10.1109/TCBB.2013.3.
    Sponsors
    The authors gratefully acknowledge the Biotechnology and Biological Research Council and the Engineering and Sciences Research Council for financial support as part of the CISB Programme Award to CPIB. The work of H. Byrne was supported in part by Award No. KUK-013-04, made by the King Abdullah University of Science and Technology (KAUST). I. De Smet was supported by a BBSRC David Phillips Fellowship (BB_BB/H022457/1) and a Marie Curie European Reintegration grant (PERG06-GA-2009-256354). J.R. King gratefully acknowledges the funding of the Royal Society and Wolfson Foundation. The authors also thank Kim Kenobi for helpful comments.
    Publisher
    Institute of Electrical and Electronics Engineers (IEEE)
    Journal
    IEEE/ACM Transactions on Computational Biology and Bioinformatics
    DOI
    10.1109/TCBB.2013.3
    PubMed ID
    23702543
    ae974a485f413a2113503eed53cd6c53
    10.1109/TCBB.2013.3
    Scopus Count
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