Evolution of Cellular Networks
Previous and current research
In order to study the evolutionary dynamics and functional importance of post-translational regulatory networks we are developing a resource of experimentally derived post-translational modifications (PTMs) for different species in collaboration with mass-spectrometry groups. This data is being used to develop novel computational methods to predict PTM function and regulatory interactions. The combination of these resources will allow us to understand how genetic variation results in changes in PTM interactions and function.
Changes in cellular interaction networks underlie the variation in the cellular responses and sensitivity to environmental perturbations or small-molecules. As we model and study the evolution of cellular interaction networks, we expect to gain an understanding about how different individuals or species diverge in their response to drugs. We aim to study this relationship and to develop methods to predict how genetic changes result in specific sensitivity to drug combinations.
Function and Evolution of Post-translational networks
In collaboration with mass-spectrometry groups we are using a growing resource of PTMs from different species (PTMfunc.com) to study the functional relevance and evolutionary properties of post-translational networks. Current projects in this area include: the study and prediction of enzyme specificity and PTM interaction networks; using information on conditional changes of PTM abundance to study signalling specificity and functional co-regulation of PTMs; development of novel methods to prioritize functionally relevant PTMs within large-scale datasets (related publication 1 and 2).
Evolution of chemical-genetic and genetic-interaction networks
The group is also interested in understanding the genetic architecture of cells. How disrupting simultaneously combinations of parts can have effects on fitness that deviate from a neutral expectation. Studying these genetic-interactions and how they change during evolution should allow us also to understand how combinations of drugs can have synergistic effects that are specific to a given species. Ongoing efforts in this area aim to: integrate different data types such as structural and chemical-genetic information to predict drug targets; develop predictors for the combinatorial effects of small-molecules; (related publication).
With the recent improvements in high-throughput methodologies it is becoming feasible to perform large-scale characterization of different individuals of the same species. Ultimately this information will allow us to better understand how the genetic variation in a population relates to the variations in phenotypes. In this area of research we are developing methods to predict phenotypic variation in different strains of S. cerevisiae from complete genome sequences making use of the accumulated knowledge for the well characterized lab strain. We plan also to apply these approaches to study the variation in individuals of other species (see post for example).