BioSCENTer - Bioinformatics and Systems Biology

Contact person

Prof. K. Marchal
Katholieke Universiteit Leuven
Centre of Microbial and Plant Genetics
Kasteelpark Arenberg 20 - bus 2460, 3001 Heverlee, Belgium
Tel: +32 16 329685 or +32 16 321631
Fax: +32 16 321966

Prof. Y. Moreau
Katholieke Universiteit Leuven
Kasteelpark Arenberg 10 bus 2446, 3001 Heverlee, Belgium
Tel: +32 16 328645 of +32 16 321709
Fax: +32 16 321970



Associated partner: High-throughput Facilities
  • MicroArray Facility, VIB (Van Hummelen Paul)
  • ProMeta, KUL (Waelkens Etienne, Vanderleyden Jos, De Moor Bart, Schoofs Liliane, Rozenski Jef, Proost Pol). ProMeta is the Interfaculty Center for Proteomics and Metabolomics research of the K.U.Leuven. On the one hand, it provides services to the university, its association members, and the industry. On the other hand, it performs research in the framework of joint research projects between the center and academic or industrial research groups. ESAT-SCD and CMPG are two of the six pilot groups that have initiated ProMeta.

Background and goal

During the last decade, molecular biology has faced a technological revolution with the development of high throughput omics methods. The latest sequence technologies offer the opportunity to map the genomic sequence of different organisms and even individuals in a cheap and fast way. Other technologies capture the cellular behavior of an organism in a snapshot: all types of genetic entities (transcriptome, proteome, metabolome) and their respective interactions can be measured simultaneously. Integrating these unprecedented amounts of information allows gaining true mechanistic insights in the inherent complexity of biological processes, which offers a huge potential for biotechnological, medical and environmental applications.
Complementary to these high throughput technologies, characterized by a high throughput and low resolution are techniques that offer an extremely high resolution but at low throughput (detailed enzyme kinetics linking to biomint). Also these technologies result in an extreme data overload.

The analysis of this tremendous dataflow is however, not obvious. Generating huge amounts of data has as a disadvantage that simple interpretation of experiments is no longer possible and that complex data-analysis is a requirement. Moreover, high throughput experiments are noisy in nature and of a very high dimensionality. Classical statistical approaches are often not straightforward to apply and even for the most basic analysis novel methods have to be devised (preprocessing, normalization). One of the major potentials of the "omics" era consists in integrating these different complementary sources of information in order to answer specific biological questions. The core of a bioinformatics center consists of defining global or specific biological questions that can be solved using data rich measurements and developing the right methods/strategies to do so. This not only results in "biological breakthroughs" but as the nature of the modeling is of such complexity, often new statistical or mathematical frameworks have to be devised. So it definitely can also lead to mathematical/statistical breakthroughs.

The type of modeling is very much driven by the biological question to be asked. The overview given below is far from exhaustive.

  • Gene prioritization: As high throughput data are screening procedures, they are hypothesis generating. By measuring all genetic entities simultaneously they allow pinpointing all entities that play a role in the cellular context upon a certain cue. However, as high throughput experiments are noisy, the level of confidence one can have in the predicted entities involved in the process to e studied s quite low. However, integrating different omics datasets, each of which describes the same biological system from a slightly different point of view can largely decrease the number of reliable predictions (i.e, reduction of the search space from a whole genome to a few candidates).
  • Pathway analysis goes one step beyond as it aims not only at prioritizing genes that are involved in a certain phenotype or pathway but also tries to find the causal connectivities between the identified entities. It is the study of a cell, viewed as an integrated and interacting network of genes, proteins and biochemical reactions. It aims at gaining insight in the structure of these molecular and regulatory networks through integrative modeling of large amounts of complementary high-throughput molecular data. Pathway analysis results fundamental insights in the basic cellular signaling mechanisms with a wide range of applications in mechanistic toxicology, cancer research, synthetic biology...As the cellular decision making which result in a specific phenotype follows from the action of the underlying molecular pathways, a mechanistic model of such pathway can be used as input to more complex systems that model the interaction of a cell with its environment (models of biofilms, ) Relation with the virtual life..
  • Disease management: Omics technologies largely facilitate biomarker development and disease management. The expression of genes/proteins in a cell can be viewed as a footprint of that cell. By comparing the characteristic profiles of for instance, cancer versus normal cells, features or markers indicative for a specific disease phenotype can be pinpointed. Using such biomarkers can help with the diagnosis, prognosis of certain diseases or in accelerating the detection of adverse effects in toxicology testing. As fast sequencing becomes possible it is to be expected that soon the genome sequence of each individual will be known offering the potential to develop methodologies towards personalized medicine.
  • Evolutionary studies: Tree construction is used to reconstruct phylogenies of protein and gene families. Large scale genome sequencing nowadays allows for the construction of phylogenetic relationships based on whole genome sequences. The sequencing of thousands of novel species helps gaining insight in the evolutionary history of an organism but also offers large opportunities for comparative approaches. (linking to the cluster of Filip Volckaert)
  • Metagenomic approaches sequence whole communities and consortia of organisms (mainly bacteria at this stage) that allow us to study complex interactions in communities and gaining better insight in complex ecosystems. (linking to the cluster of Filip Volckaert)

Strategic mission

  • Develop high impact tools for breakthroughs in biology, biomedicine, disease management (multifactorial diseases) and drugs discovery;
  • Understand the signal processing mechanisms (e.g. quorum sensing)
  • Sustainable microbial ecology (biofuels, biodegradability, ...)

Research lines

  • Development of pathway analysis tools with applications
  • Microbial systems biology: Model organism: Salmonalla typhimurium quorum sensing (QS) system. Using high throughput analysis we would like to reconstruct the molecular mechanism of bacterial luxS dependent QS and its relation to biofilm production.
    • The preliminary model will be used as input for a supercellular model that models the formation of biofilms.
    • Cross comparison of the QS dependent pathway across related and unrelated species will allow gaining insight in specific ecological niches.
  • VitD dependent pathway analysis (collaboration with LEGENDO)
  • ...

Track record

Key publications

S. C. De Keersmaecker, I. M. Thijs, J. Vanderleyden, and K. Marchal. Integration of omics data: how well does it work for bacteria? Molecular Microbiology, 2006, 62:1239-50

K. Lemmens, T. Dhollander, T. De Bie, P. Monsieurs, K. Engelen, B. Smets, J. Winderickx, B. De Moor, and K. Marchal. Inferring transcriptional module networks from ChIP-chip-, motif- and microarray data. Genome Biology, 2006, 7:R37

K. Marchal, S. De Keersmaecker, P. Monsieurs, N. Van Boxel, K. Lemmens, G. Thijs, J. Vanderleyden, and B. De Moor. In silico identification and experimental validation of PmrAB targets in Salmonella typhimurium by regulatory motif detection. Genome Biology, 2004, 5:R9.1-R9.20, Epub

R. Van Hellemont, P. Monsieurs, G. Thijs, B. De Moor, Y. Van de Peer, K. Marchal. A novel approach to identify regulatory motifs in distantly related genomes. Genome Biology, 2006, 6:R113.1-R113.17

Large projects

  • IWT-SBO: BioFrame: A bioinformatics framework for top down systems biology, Project coordinator, (2007-2011)
  • IWT-SBO: MoKa: Molecular Karyotyping (2007-2011)
  • Human Science Foundation grant application 2006: The biological role of tandem repeats in genomes. Main Applicant K. Verstrepen, Harvard Bauer Center for Genomics Research, Harvard University Cambridge MA. Accepted.
  • Belgian Science Policy IAP P6/25: BIOMAGNET: Bioinformatics and Modeling: from Genomes to Networks (2007 2011)
  • K.U.Leuven EF/05/007: SYMBIOSYS: K.U.Leuven Center for Computational Systems Biology (2005 2010)
  • K.U.Leuven GOA/2005/11: AMBIORICS: Algorithms for Medical and Biological Research, Integration, Computation and Software (2004 2009)


Silicos NV: In silico drug screening company,
Cartagenia: Specializing in IT solutions for clinical genetic diagnosis,