Stefan Gruenewald
Professor,Principal Investigator
Biological science
Shanghai Institutes for Biological Sciences
China
Biography
Professor Stefan Grunewald2001: PhD in Mathematics from Bielefeld University in Germany 2001-2005: Postdoc at Bielefeld University, Uppsala University (Sweden), and the University of Canterbury (New Zealand) The main research area of the group is phylogenetics. We develop methods to reconstruct phylogenetic trees or networks, with an emphasis on displaying ambiguity in the data and reticulate evolution. The structures that we would like to construct from biological data can often be described in combinatorial terms
Research Interest
The main research area of the group is phylogenetics. We develop methods to reconstruct phylogenetic trees or networks, with an emphasis on displaying ambiguity in the data and reticulate evolution. The structures that we would like to construct from biological data can often be described in combinatorial terms. For example, an unrooted phylogenetic network can be considered as a collection of bipartitions (splits) of the taxa set and a rooted phylogenetic network is an acyclic directed graph. Compared with the combinatorics of trees, there are many open problems for more general networks. We work on those combinatorial problems and the results are published in mathematical journals. They often have algorithmic consequences and give rise to new methods or insights in the performance or limitations of existing methods. We implement our new algorithms and collaborate with biologists to analyze their data, comparing the results of different phylogenetic methods.
Publications
-
Jialiang Yang, Jun Li, Stefan Gruenewald, Xiu-Feng Wan. BinAligner: a heuristic method to align biological networks. BMC Bioinformatics. 2013, 14(Suppl 14):S8
-
Jialiang Yang, Stefan Gruenewald, Yifei Xu, Xiu-Feng Wan: Quartet-based methods to reconstruct phylogenetic networks. BMC Systems Biology 8: 21 (2014)
-
The main research area of the group is phylogenetics. We develop methods to reconstruct phylogenetic trees or networks, with an emphasis on displaying ambiguity in the data and reticulate evolution. The structures that we would like to construct from biological data can often be described in combinatorial terms. For example, an unrooted phylogenetic network can be considered as a collection of bipartitions (splits) of the taxa set and a rooted phylogenetic network is an acyclic directed graph. Compared with the combinatorics of trees, there are many open problems for more general networks. We work on those combinatorial problems and the results are published in mathematical journals. They often have algorithmic consequences and give rise to new methods or insights in the performance or limitations of existing methods. We implement our new algorithms and collaborate with biologists to analyze their data, comparing the results of different phylogenetic methods.
