Efficient Biological Networks Discovery and Analysis

Project Title: Efficient Biological Networks Discovery and Analysis

Organisation: University of Liverpool
Applicants: Professor Leszek Gasieniec, Professor Francesco Falciani & Dr Olga Vasieva
Project Duration: 01 April 2013 – 01 October 2013
NERC Reference: NE/KO11332/1

Summary of proposed research: The hopping PhD student will spend six months in the CCBM in the IIB at Liverpool University, working closely with pure biologists and computational biologists. The main purpose of this placement will be to acquire by the discipline hopper good understanding of biological networks. This project will be used also to set foundation for further development of respective computational tools and to liaison research collaboration between the two research groups in Algorithms and Systems Biology and their respective collaborators.

The main research challenges to be addressed within this project include: 1) NETWORK MODULARIZATION: Network modularization consists in the identification of a portion of large network that share certain characteristics. Most of the available methods that perform well use a definition of network module based on connectivity. Some of the more advanced approaches instead aim at integrating multi-level information (e.g. agglomeration of several gene properties and gene relationships in the module search) within a module and are therefore more suitable for representing biological complexity. Unfortunately, these tend to perform well in small networks (<1000 nodes) and they either fail for larger networks, which are of real interests to biologists. ln search for efficient solutions we will look into new promising clustering methods. The group lead by Prof Gasieniec (Pl) currently develops a tool “Graph Draw” designed for analysis of real datasets gathered from a wide range of social networking mediums and manipulates the layout of the data in order to produce meaningful representation of information, from which can be analysed to achieve some specific goals. Metrics used in Graph Draw include degree centrality, closeness centrality, betweenness centrality, page rank, transitivity, amongst others. This joint project is expected to build further on the success of Graph Draw in the context of complex biological networks analysis. 2) NETWORK VISUALISATION: The visualization and the visual analysis of biological networks are one of the key analysis techniques to cope with the enormous amount of data. In particular, the layout of networks should be in agreement with biological drawing conventions and should be adopted. In general, visualization methods for the life sciences should allow for the layout and navigation of biological networks for both their static presentation as well as their interactive exploration. Such methods need to adhere to constraints that originate from recognized textbook and poster layouts from generally accepted drawing conventions within the life-science community as well as from standardization initiatives such as MIM (Molecular lnteraction Maps) and SBGN (Systems Biology Graphical Notation). The Graph Draw tool provides also some visualisation based on force-directed graph drawing algorithms. Further extensions including mutilayer presentation and animation are sought also within this project.