Efficient Biological Networks Discovery and Analysis

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 projects 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 relation- ships in the module search) within a module and are therefore more suitable for representing biological complexity. Unfortunately, these tend to perform well inl small networks (<1000 nodes) and they either fail for larger networks, which are of real interests to biologists. In search for efficient solutions we will look into new promissing clustering methods. The group lead by Prof Gasieniec (PI) 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 [19]. 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 Interaction 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.

The research and dissemination activities planned in the proposed project have the potential to impact directly the environmental scientific community and longer term are bound to have a profound impact on the development of omics based platforms for understanding and modeling environmental biodiversity.

A key component of this project is to promote networking activities between the two departments directly involved in the hopping and more broadly involving key members of the UK and international community. We plan to achieve this by organizing a workshop where our teams will present the innovative approaches explored in this project and external speakers from relevant areas of science will be invited to present ideas and solutions to specific challenges in the integration of environmental "omics" data within the framework of network science. The direct involvement of leading researchers in the area of network analysis will promote knowledge exchange that will result in the development of long term collaborative frameworks, which we expect will be supported by additional funding (e.g. EPSRC international workshops).

Establishing a strong connection between experts in network analysis from computer science and other non-biology fields will also contribute to enhance UK leadership in environmental informatics, which we expect will play a key role in generating innovation in the foreseeable future.