The Evolutionary Systems Biology (ESB) group studies the evolution and evolvability of biological systems at all levels of biological organization, from genes and genomes to biological networks and whole organisms. It develops bioinformatics tools to integrate data from a variety of sources, including comparative whole-genome sequence data, microarray expression data, and high-throughput protein interaction data.
The field of molecular evolutionary genetics has traditionally focused on individual genes and on the evolutionary forces that influence the evolution of these genes. The field has recently seen an explosion of research activity, driven by the availability of many whole genome sequences, as well as by the massively increased amounts of genome-scale data on mRNA expression, protein expression, gene function, and genetic and physical interactions between gene products. The heterogeneity and size of these data sets require the development of increasingly sophisticated bioinformatics tools for data integration. Such data allow us to study evolutionary processes not only at the level of individual genes, but also for entire genomes and biological networks, including metabolic networks, transcriptional regulation networks and protein interaction networks.
Projects and Services
Evolution of genetic and metabolic networks
Genetic and metabolic networks drive all biological processes: they work like bridges between the organism and the individual molecules (proteins and genes) that form all living cells. Their structure, function, and evolution must be unravelled in order to understand whole organisms. The study of transcriptional regulation networks, protein interaction networks, and metabolic networks is one of the objectives of the ESB group.
We are specifically interested in evolutionary questions such as how natural selection shapes the structure of such networks, how evolutionary innovations arise in such networks, and the evolutionary history of such networks. The structure of the core metabolic network of the bacterium Escherichia coli has been characterized, as well as the evolution of the protein interaction network and the transcriptional regulation network of the yeast Saccharomyces cerevisiae. The metabolic network is an example of a small-world network, a type of network found in many unrelated areas of science, such as sociology (scientific collaboration networks) and computer networks. Many small world networks have the peculiar feature that their structure contains information about their history. This means that we can use the structure of a metabolic network to infer which metabolites appeared early in the evolution of life.
One observation about the yeast-protein interaction network is its rapid rate of evolution, which may replace up to 50% of all protein-protein interactions inside a living cell every 300 million years.
Evolution by gene duplication
Much like humans, gene duplicates may be created equal, but they do not stay that way for long. Many of them are eliminated through deleterious mutations, others diverge in sequence and function, and the remainder may retain similar functions for a long time. What role does natural selection play in this divergence? Do gene duplications cause an increase in gene expression? Does this increase carry a significant energy cost? These are some of the ESB group’s research questions. To answer them, the sequence and functional divergence of thousands of duplicate genes in different organisms are characterized using genome-sequence data as well as high-throughput functional genomics data. Some of the work shows that the sequence and functional divergence of gene duplicates is often asymmetric, meaning that one of two duplicates diverges much more rapidly than the other. This suggests the role of positive selection in functional divergence. In addition, the group’s work provides evidence that the increases in gene expression caused by gene duplication carry an energy cost that is high enough to be selected against in large populations.
Websites for Further Information
Evolutionary Systems Biology Group: http://www.ieu.uzh.ch/wagner/