Research

Can we understand evolution on a systems level?

Proteins and RNAs cannot function by themselves, but are embedded in large interacting systems. The best understood cellular systems are the metabolisms of the bacterium E. coli and of baker’s yeast. To understand the evolution of these systems, we use comparative genomics and constraint-based simulation methods (such as flux-balance analysis, FBA). In one particular project, for example, we reconstruct ancestral metabolic networks and let them evolve in different environments to test if we can successfully ‘repeat’ metabolic evolution on the computer. Another example is the modeling of the selective forces that governed the evolution of plant metabolism.

Studying evolution on a systems level does not only require looking at functional (e.g., metabolic) networks. It also forces us to examine how the evolution of one part of the genomic system was influenced by the evolution of other parts. Thus, we also need to look at phylogenetic networks, which visualise the transmission of genetic information not only along a phylogenetic tree, but also on reticulate branches that connect otherwise distinct branches of the ‘tree’. For example, we develop methods to detect ancient hybridisations between ancestral species, and we apply these methods to plant and yeast genomes.

To tackle these questions, we use a wide range of methods from different fields of bioinformatics, e.g.:

• modeling of functional biological networks (metabolism, gene regulation, …),
• comparative genomics,
• population genomics,
• phylogenomics.

Software development

In addition to our primary research motivated by biological questions, we also develop software. Two products under active development are SyBiL (a Systems Biology Library for constraint-based modelling) and PopGenome (a library for population genomic analyses), both programmed in the powerful, open-source, statistical computing environment R.