This special focus is jointly sponsored by the Center for Discrete Mathematics and Theoretical Computer Science (DIMACS), the Biological, Mathematical, and Physical Sciences Interfaces Institute for Quantitative Biology (BioMaPS), and the Rutgers Center for Molecular Biophysics and Biophysical Chemistry (MB Center).
Workshop on Clustering Problems in Biological Networks Clustering techniques are essential to a wide variety of applications. Network clustering approaches are becoming common in the analysis of massive data sets arising in various branches of science, engineering, government and industry. In particular, network clustering techniques emerge as an important tool in computational biology, where they can be used for analysis of gene and protein networks and other important problems.
As an example, understanding gene expression and regulation is one of the major problems in biology. Network models have become common in this field, and clustering approaches play a central role in such models. In gene networks, the vertices correspond to genes and the edges represent functional relations between these genes that are identified using the comparative genomics methods. Solving clustering problems in gene networks allows to identify groups of genes which have similar expression patterns. This information is crucial for understanding the nature of genetic diseases.
Similarly, in protein networks the proteins serve as nodes and nodes corresponding to two proteins are connected by an edge if they interact with each other. The importance of studying the protein networks is increasing as more information on protein interactions in various organisms is becoming available from the protein databanks. Some important properties of protein networks have been recently studied by a number of researchers. For example, it has been discovered that the degree distribution in such networks follows the power law - a property that has been observed in networks arising in a variety of diverse applications. This structure has important implication on the cell's survivability. Clustering models in protein network are important for understanding the structure of protein interactions in a cell.
Due to a wide range of applications of network clustering techniques, a large part of the previously developed methodology can be transferred to the study of networks arising in biology. However, some of the clustering problems of interest in computational biology have their own specifics. This workshop will provide a forum for leading as well as beginning researchers to discuss recent advances and identify current and future challenges arising in the research concerning clustering problems in computational biology.
Papers presented at the conference will be considered for publication in special issues of international journals.