What do we do?
At the Evolutionary Systems Biology Group we study the evolution and evolvability of biological systems at all levels of biological organization, from genes and genomes to biological networks and whole organisms. We develop 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. Our work uses comparative analysis of genomic data, laboratory evolution experiments and mathematical modelling. We also develop a variety of bioinformatics tools to help us take advantage of the torrent of data in genomics and structural biology.
We developed the Genonets Server, a computational tool that allows the construction of genotype networks, which play an important role in understanding the evolutionary dynamics of evolving molecules. This server can construct genotype networks for categorical and univariate phenotypes from DNA, RNA, amino acid or binary sequences; analyze genotype network topography and how it relates to the navigability of a genotype network via mutation and natural selection; and provide multiple interactive visualizations that facilitate exploratory research and education. We also developed Growthcurver, an R package for obtaining interpretable metrics from microbial growth curves. Plate readers can measure the growth curves of many microbial strains in a high-throughput fashion. The hundreds of absorbance readings collected simultaneously for hundreds of samples create technical hurdles for data analysis. Growthcurver summarizes the growth characteristics of microbial growth curve experiments conducted in a plate reader. The data are fitted to a standard form of the logistic equation, and the parameters have clear interpretations on population-level characteristics, like doubling time, carrying capacity, and growth rate.
Main publications 2016
- Khalid, F., Aguilar-Rodríguez, J., Wagner, A., Payne, J.L. (2016) Genonets server – A web server for the construction, analysis, and visualization of genotype networks. Nucleic Acids Research, 44: W70-W76.
- Sprouffske, K., Wagner, A. (2016) Growthcurver: An R package for obtaining interpretable metrics from microbial growth curves. BMC Bioinformatics 17, 1.
- Wagner, A., Ortman, S., Maxfield, R. (2016) From the primordial soup to self-driving cars: standards and their role in natural and technological innovation. Journal of the Royal Society Interface 13, 20151086.