It has recently become possible to record the EEG simultaneously with fMRI, providing whole-brain maps of the hemodynamic (fMRI) correlates of electrophysiological (EEG) activity. The combination of the EEG high temporal resolution with the fMRI high spatial resolution offers a unique opportunity for studying the spatio-temporal dynamics of brain activity noninvasively. Here, I will focus on the application of EEG-fMRI to the study of spontaneous epileptic activity in patients undergoing pre-surgical evaluation.

I will give an introduction to hybrid modeling methods for bioprocess and biochemical networks modeling. Hybrid methods combine parameter-free modeling with statistical modeling tools. They enable to blend mechanistic knowledge and statistical relationships into models with improved performance and broader scope.

Computational modelling of protein interactions (docking) is an important endeavour because protein complexes are difficult to determine by experimental methods alone. Nevertheless, computational prediction of protein interactions is no trivial task either and there is much to be done to improve the reliability of protein docking methods. Protein coevolution traces that are identifiable from the analysis of multiple sequence alignments can help predict protein interacting contacts and can be used to constrain the search space of constrained docking algorithms, such as BiGGER.

Systems biology provides new approaches for in silico metabolic engineering and drug development through the application of analysis, simulation and optimization methods for metabolic models. In silico modeling of cellular metabolism is divided between genome-scale stoichiometric models and small-scale kinetic models. While the former are analyzed using optimal assumptions predicting intracellular microbial fluxes and growth rates, the later are used for dynamic behaviour simulations. However, there is currently a separation between these two modeling approaches.

Subcellular location is an important property of proteins, carefully regulated
by the cells. To determine subcellular location on a proteome-wide scale,
fluorescent image data is most commonly used and a classification system is
employed for analysis. These systems assign each protein to one of a small set
of predefined location classes (typically the major organelles).

This is a limited representation of the underlying biology as proteins are
often in multiple organelles. I will present techniques that go beyond the

The importance of a system theory based approach in understanding immunological diseases, in particular the HIV-1 infection, is being increasingly recognized. The dynamics of virus infection may be effectively represented by compact state space models in the form of nonlinear ordinary differential equations (ODEs).
Nonlinear Bayesian filtering offers various online tools for system identification of parametric ordinary differential equation models. Since parameters may change with time, it is a relevant question to assess how well time-varying parameters can be estimated from data.

In the Computational Genomics Lab we combine the study of evolutionary cell biology with translational, or medical bioinformatics. We study evolutionary cell biology, i.e. the evolutionary mechanisms underlying the origins and evolution of cellular life and the complex structures within the cell. We are also very interested in the medical, or translational applications of bioinformatics and evolutionary genomics, and are conducing collaborative projects on pathogenic bacteria, protozoa and several types of human cancers.

Complex networks are ubiquitous in real-world systems. In order to understand their design principles, the concept of network motifs emerged. These are recurrent overrepresented patterns of interconnections that can be seen as building blocks of networks. Algorithmically, discovering these motifs is a hard problem, which limits their practical applicability.

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The talk will have two parts. In the first part I will give a broad overview of the area of Inductive Logic Programming (ILP) as a promising approach to Relational Data Mining. Advantages of such approach together with their main applications will then be presented. In the second part I will focus on: i) a technique for conceptual clustering in Relational Data Mining that I have been working recently; ii) the application of ILP in rational Drug Design; iii) work on using ILP for Protein Folding.