Network reconstruction, network learning and biological pathways
Given the ample amount of “omics” data that is being generated, it becomes more and more important to devise schemes to best profit from the mass of generated data and to extract the most likely biological interpretation.
The group is thus aiming to develop methods and tools to abstract, visualize and interpret high throughput data. Here the group is focussing specifically on metabolomics and transcriptomic data sets but as proteomics matures more and more data is being integrated as well.
These tools are applied to data that is generated in the group to find new candidate genes for cell wall synthesis and the response to carbon limitation.
Furthermore these approaches are used to build predictive models.