Computational biology

Proteins have been compared to neural networks. Like these weighted networks they are claimed of being able to propagate and store information. Their role is to ensure under a variety of circumstances the proper functional behavior of nature's robots, i.e. the cells.

It is therefore not surprising that algorithms are being conceived to analyze this information processing capacity, since understanding these features would allow researchers to predict the impact of disease-related mutations on the cell’s functional behavior. In this context, computer scientists can provide a valuable contribution.

There are multiple proposals available.

Proteins are amino acid sequences that fold into a three-dimensional structure. To understand their structure one could replace every amino acid by a node and connect these nodes into a graph or network, where links define the proximity between the nodes. The aim of this project is to examine these graph features of proteins in order to improve our understanding of how proteins process information.

Once you know which residues in a protein sequence are important for their function, can you produce an artificial protein sequence that folds into the same structure and has a function equivalent to the original one? Recently it was shown that this actually may work. The aim of this project is to investigate the algorithm that was developed to solve this problem, on the one hand, and, on the other hand, to develop another one using structural information produced by an algorithm designed by the promoter of this project.

Proteins are dynamical units capable of responding in highly sophisticated manners to binding or other post-translational events. Elastic network models (ENM) provide coarse-grained models of proteins and allow one to study the dynamics of proteins. The aim of this project is on the one hand to investigate the extent to which ENM have or could be use to model structural changes In SH3 domains caused by peptide binding or mutations. On the other hand we want to examine how these results can be applied to study the information flow within protein structures.

The genes encoding major histocompatibility (MHC) molecules are among the most polymorphic genes known for vertebrates. Since MHC molecules play an important role in the induction of immune responses, the evolution of MHC polymorphism is often explained in terms of increased protection of hosts against pathogens. Two selective pressures that are thought to be involved are (1) selection favoring MHC heterozygous hosts, and (2) selection for rare MHC alleles by host-pathogen coevolution. the goal of this project is to develop a system capable of simulating this coevolutionary dynamics to study the relative impact of these two mechanisms on the evolution of MHC polymorphism.

Machine learning, programming skills and passion for interdisciplinary research.

There is no extensive biological knowledge required.

If you are interested in one of these subjects, contact Prof. Tom Lenaerts (Tom Lenaerts @ ULB).