Systems biology
| Expert Scientist Tom Michoel |
| Postdoc Vanessa Vermeirssen | | Software Developer Thomas Van Parys | | Postdoc Eric Bonnet | | Phd Student Ying He | | Phd Student Anagha Joshi |
| Postdoc Nathalie Pochet | | Tech Transfer Officer Xin-Ying Ren | | Postdoc Kevin Vanneste |
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.
Selected publications
Joshi, A., Van Parys, T., Van de Peer, Y., Michoel, T. (2010)
Characterizing regulatory path motifs in integrated networks using perturbational data. Genome Biol. 11(3):R32.
Joshi, A., De Smet, R., Marchal, K., Van de Peer, Y., Michoel, T. (2009)
Module networks revisited: computational assessment and prioritization of model predictions. Bioinformatics 25(4):490-6.
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 Syst. Biol. 3:49.
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 5(12):1817-30.
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(2):176-83.
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