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.
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,
Algorithm and Software development
In addition to our primary research motivated by biological questions, we also develop algorithms and produce software. Three products under active development are SyBiL (a Systems Biology Library for constraint-based modelling), WhopGenome (a library for accessing huge data files containing variance or sequence information), and PopGenome (a library for population genomic analyses). All three are programmed in the powerful, open-source, statistical computing environment R.