Abstract
The objective of the project reported in the present chapter was the reverse engineering of gene regulatory networks related to quorum sensing in the plant pathogen Pectobacterium atrosepticum from micorarray gene expression profiles, obtained from the wild-type and eight knockout strains. To this end, we have applied various recent methods from multivariate statistics and machine learning: graphical Gaussian models, sparse Bayesian regression, LASSO (least absolute shrinkage and selection operator), Bayesian networks, and nested effects models. We have investigated the degree of similarity between the predictions obtained with the different approaches, and we have assessed the consistency of the reconstructed networks in terms of global topological network properties, based on the node degree distribution. The chapter concludes with a biological evaluation of the predicted network structures.
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Notes
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1 See further information in ArrayExpress-http://www.ebi.ac.uk/microarray-as/aer/.
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3 Note that in contrast to most home-spotted cDNA microarrays, Agilent arrays are not printed by different print tips and thus are not subdivided into separate subblocks within the array. For this reason, it was not necessary to use the print-tip lowess algorithm, which applies the same curve fitting technique separately to each subblock on the array.
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9 In statistics, this is called a type-II maximum likelihood estimation.
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10 Note that one can always draw a straight line through two points.
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Lin, K. et al. (2010). Reverse Engineering Gene Regulatory Networks Related to Quorum Sensing in the Plant Pathogen Pectobacterium atrosepticum . In: Fenyö, D. (eds) Computational Biology. Methods in Molecular Biology, vol 673. Humana Press, Totowa, NJ. https://doi.org/10.1007/978-1-60761-842-3_17
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