Vladimir Filkov (Department of Computer Science, University of California, Davis)
Abstract. Biological networks encode complex molecular systems into convenient structural representations thus enabling their analysis and understanding. The architecture of these networks is often related to observable phenomena, and to biological function. Studying the connection between biological network architecture and their function is made possible by novel methods enabling correct statistical and combinatorial accounting of topological features in the network systems.
Here I describe a set of tools and techniques that help us do that, together with results and insights from our studies. The techniques described come from various scientific disciplines that have had to deal with networked systems for some time, like social sciences, statistical mechanics, and complex network theory among others. In all case we have adapted these tools to biological network analysis and either extended or improved upon them.
Bio: Dr. Vladimir Filkov is an Assistant Professor in the Computer Science Department at UC Davis. His research focuses on developing models and computational methods for the study of complex systems, primarily in molecular biology and bioinformatics (e.g. biological networks), but also in software engineering and social sciences. He has published in top journals and conferences in bioinformatics, software engineering and physics. He collaborates successfully with biologists, computer scientists, and physicists.