Much of the data generated and analyzed in the life sciences can be interpreted and represented by networks or graphs. Network analysis and visualization methods help in investigating them, and many universal as well as specialpurpose tools and libraries are available for this task. However, the two fields of graph drawing and network biology are still largely disconnected. Hence, visualization...

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This lecture describes methods for analyzing networks in terms of their motif content. Network motifs are defined as ”recurring patterns of interactions that are significantly over-represented”. The motivation for analyzing the motif content of the network lies in the basic assumption that the over-representation of a certain motif in a network indicates it has some functional importance. Thus,...

Network analysis of living systems is an essential component of contemporary systems biology. It is targeted at assemblance of mutual dependences between interacting systems elements into an integrated view of whole-system functioning. In the following chapter we describe the existing classification of what is referred to as biological networks and show how complex interdependencies in biologic...

So far (lecture 2), two extreme views of network topology were given and analyzed: structured and random networks. However, many biological, technological and social networks lie somewhere between these two extremes. In order to explain the topology of these real networks, Watts and Strogatz [12] suggested a model, which combines these two views, thus having the advantages of both models when c...

Acknowledgments My experience as a graduate student at Boston University has been excellent. Having been an undergraduate here as well, I already knew a lot of the faculty when I decided to enroll in the Ph.D. program. Throughout the years I have felt at home here, and was always greeted with a smile whenever I saw professors around the building. My interest in interdisciplinary research began ...

Many complex systems can be represented and analyzed as networks, and examples that have benefited from this approach span the natural sciences. For instance, we now know that systems as disparate as the World Wide Web, the Internet, scientific collaborations, food webs, protein interactions and metabolism all have common features in their organization, the most salient of which are their scale...

As biological inquiry produces ever more network data, such as protein-protein interaction networks, gene regulatory networks, and metabolic networks, many algorithms have been proposed for the purpose of pairwise network alignment– finding a mapping from the nodes of one network to the nodes of another in such a way that the mapped nodes can be considered to correspond with respect to both the...

Highlighting similarities and differences between networks is an informative task in investigating many biological processes. Typical examples are detecting differences between an inferred network and the corresponding gold standard, or evaluating changes in a dynamic network along time. Although fruitful insights can be drawn by qualitative or feature-based methods, a distance must be used whe...