Research
Due to recent developments in sequencing, complete genomes are being determined at an ever increasing rate. However, the correct identification of many genes remains a major challenge. In order to identify and determine the structure of genes, for which no experimental information is available yet, and spliced-alignments between the transcript and the genomic sequence cannot be produced, we need to use predictive in silico methods, based on intrinsic approaches. Our lab is involved in many gene and genome annotation projects, mostly of plants, but also of animals, and protest organisms. Check the 'Genomes' section of our website for more information.
We are interested in the development of methods for reverse-engineering transcription regulatory networks from transcriptome data, identifying functional modules in integrated regulatory and protein interaction networks, understanding how physical interaction networks mediate the condition-dependent response to external stimuli, and modeling the evolution of network modules after whole genome duplications. Our computational methods are validated on experimental data for Saccharomyces cerevisiae, Arabidopsis thaliana, Caenorhabditis elegans, and human.
Detailed analyses of the genomes of several model organisms revealed that gene duplication have played a prominent role in the evolutionary history of many eukaryotes. In addition, many genomes show traces of ancient whole genome duplications. Evidence for large-scale gene duplication or entire genome duplication events often comes from the detection of block or segmental duplications. However, the detection of segmental duplications in genomes is not self-evident. We have developed dedicated software that tries to find colinearity between and within genomes. Apart from looking for evidence for such large-scale gene duplication events, we also try to study the consequences of these events for biological evolution. Other areas of interest are genome evolution in general, the functional divergence of genes, (evolution of) regulation and evolutionary robotics.
  Although it is easy to understand that changes in the place or time of gene expression can create new or alternative molecular interactions, little
information about the organization and evolution of transcriptional regulation in plants is known. However, this knowledge is essential because each gene
is flanked by regulatory sequences which, together with the expression and activity of other proteins, determine the amount, place, and timing of
expression. Therefore, characterizing these regulatory motifs is required in order to understand the regulatory interactions between trans-acting proteins
and the promoters of thousands of genes within a eukaryotic genome. This information is also essential when studying biological processes from a holistic
point of view by integrating complementary functional data sets. We are studying the architecture of plant promoter sequences and try to identify
cis-regulatory elements (or TFBS), which play an important role in transcriptional regulation. Fully sequenced plant genomes, together with extensive
expression data sets are used to characterize the basic composition of plant promoters (e.g. TFBS, cis-regulatory modules) and to study their evolution
within the green plant lineage.
The application of machine learning techniques has become fundamental in extracting knowledge from biological data and in the process of automating important tasks. In biology, where structures or processes are described by a large number of features, and complex interactions exist between these features, techniques from machine learning are the only practical way to explore these datasets. Amongst other things, we are applying machine learning techniques to gene prediction and annotation and text mining.
a young and dynamic lab at the Plant Systems Biology department of VIB and Ghent University. The goal of the Evolutionary Systems Biology lab is to understand how plant developmental systems work and how they evolve.
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