HVIDSTEN RESEARCH GROUP

 

 

 

Regulatory networks in plants

 

We are using machine learning methods and large genomics datasets to model how genes interact in regulatory networks and how such networks give rise to properties characteristic to individuals and species.

 

We have developed a tool for network comparison across species (comparative regulomics): http://complex.plantgenie.org.

 

 

 

 

Local descriptors of protein structure

 

We have introduced the concept of local descriptors of protein structure to characterize local neighborhoods of amino acids in proteins including short- and long-range interactions. We have build a library of recurring local descriptors and show that this library is general enough to allow assembly of unseen protein structures. Thus the method identifies, in a systematic way, the local building blocks that are common to many proteins with otherwise unrelated global structures (folds). The descriptor building block approach has many possible applications and we have applied them to prediction of protein-ligand interactions, fold recognition, residue-residue contact prediction and prediction of function from structure.

 

Presentation at Stockholm Bioinformatics Centre, 2009.

 

Articles about local descriptors of protein structure.

 

 

Gene regulation

 

We have shown that we can describe the specific mechanism behind the combinatorial nature of gene regulation by constructing IF-THEN rules that identify sets of binding sites (IF-part) that are associated with particular gene expression profiles (THEN-part). The approach is firmly based on a formal mathematical framework that describes the regulatory logic in terms of IF-THEN rules. The proposed regulatory mechanisms are completely transparent and can be inspected and understood by experts.

 

Presentation in Warsaw, 2008.

 

We have later devised an approach to network inference that incorporates interactions between regulators, including synergistic and competitive relationships, by evaluating increasingly more complex regulatory mechanisms. The approach integrates promoter information and gene expression profiles to reverse-engineer regulatory networks.