Systems Biology

Our main focus to date has been on reverse-engineering transcription regulatory networks from transcriptome data. We have developed a software package LeMoNe for learning module networks which uses ensemble-based techniques to infer a probabilistic model to predict the condition-dependent expression levels of modules of coexpressed genes, based on the combined expression levels of a set of regulators. LeMoNe has been benchmarked and compared with state-of-the-art methods using transcriptome data for yeast and E. coli. Past and ongoing projects have used LeMoNe to infer developmental regulatory modules in C. elegans, microRNA regulatory modules in human cancer cells, regulatory variants underlying heterosis in A. thaliana using SNP and diallel expression data, stress and cell cycle dependent regulatory modules in A. thaliana, and posttranscriptional regulatory modules in yeast.

Functional modules observed in transcriptome data are the result of physical interactions taking place at the protein-DNA or protein-protein level. We have developed a Network Motif Clustering Toolbox to identify modules in integrated networks, which forms a general data integration methodology. It is based on the presence of network motifs, small, frequently occuring subgraphs, which represent functional relationships between heterogeneous data types. We have benchmarked the algorithm on an integrated network in yeast with more than 50,000 transcription factor binding, protein-protein and phosphorylation interactions.

An important question is how physical networks mediate the condition-dependent response to external stimuli that is observed in transcriptome data. We have introduced the notion of regulatory path motifs, short paths in the physical network which occur significantly more often than expected by chance between transcription factors and their targets in the perturbational expression data. A study is yeast has shown that these paths explain a more than five- to ten-fold higher number of perturbed targets compared to using direct transcriptional links only. These paths are organized into functional modules which can be identified using the Network Motif Clustering Toolbox.

The modular organization observed in all biological interaction networks has arisen during evolution by gene and genome duplications followed by interaction gain and loss. While simple duplication-divergence models have been able to explain some large-scale properties of biological networks, such as the appearance of a scale-free-like topology, very little is known about how specific modular subparts have evolved. We have recently started to develop models for the evolution of regulatory and protein interaction networks following whole genome duplication and we are also working on methods for identifying conserved modules between multiple species.

Joshi, A., De Smet, R., Marchal, K., Van de Peer, Y., Michoel, T. (2009) Module networks revisited: computational assessment of model predictions. Bioinformatics 25, 490-6.

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Michoel, T., De Smet, R., Joshi, A., Van de Peer, Y., Marchal, K. (2009) Comparative analysis of module-based versus direct methods for reverse-engineering transcriptional regulatory networks. BMC Systems Biology 3, 49.

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Vermeirssen, V., Joshi, A., Michoel, T., Bonnet, E., Casneuf, T., Van de Peer, Y. (2009) Transcription regulatory networks in Caenorhabditis elegans inferred through reverse-engineering of gene expression profiles constitute biological hypotheses for metazoan development. Molecular BioSystems 2009, 5, 1817-30.

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Joshi, A., Van de Peer, Y., Michoel, T. (2008) Analysis of a Gibbs sampler method for model based clustering of gene expression data. Bioinformatics 24, 176-83.

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