Due to the continuous explosive accumulation of sequence data, which is driven by novel sequencing techniques such as, e.g., pyrosequencing, the application of high performance computing techniques will become crucial to the success of Evolutionary Bioinformatics. In addition, emerging parallel multi - and many-core computer architectures pose new challenges for the field, since a large number of widely used applications will have to be ported to these systems.
I will outline how the application of high performance computing methods can contribute to solve challenging problems such as large-scale phylogenetic inference under the Maximum Likelihood criterion, phylogenetic classification of query sequences, and co-phylogenetic analyses based on statistical models. Within this context I will review current algorithmic and computational problems in evolutionary Bioinformatics and address programming and performance issues ranging from small 8-core architectures up to the SGI Altix 4700 and the IBM BlueGene supercomputer.
I will conclude with an overview of current and future challenges.
Alexandros Stamatakis received his Diploma in Computer Science in March 2001 from the Technical University of Munich. In October 2004 he received his Ph.D. for research on "Distributed and Parallel Algorithms and Systems for Inference of Huge Phylogenetic Trees based on the Maximum Likelihood Method" also from the Technical University of Munich.
From January 2005 to June 2006 he worked as postdoctoral researcher at the Institute of Computer Science in Heraklion, Greece. In July 2006 he joined Bernard Moret's group at the Swiss Federal Institute of Technology at Lausanne as a PostDoc.
In January 2008 he moved back to Munich to set up a junior research group, that is funded under the auspices of the Emmy-Noether program by the German Science Foundation (DFG), at the Bioinformatics department of the Ludwig-Maximilians University of Munich.
His main research interest are: technical and algorithmic solutions for inference of huge phylogenetic trees, applications of High Performance Computing techniques in Bioinformatics, and challenging phylogenetic analyses of real-world datasets in collaboration with Biologists.
