28,172 research outputs found
The Function of Communities in Protein Interaction Networks at Multiple Scales
Background: If biology is modular then clusters, or communities, of proteins
derived using only protein interaction network structure should define protein
modules with similar biological roles. We investigate the link between
biological modules and network communities in yeast and its relationship to the
scale at which we probe the network.
Results: Our results demonstrate that the functional homogeneity of
communities depends on the scale selected, and that almost all proteins lie in
a functionally homogeneous community at some scale. We judge functional
homogeneity using a novel test and three independent characterizations of
protein function, and find a high degree of overlap between these measures. We
show that a high mean clustering coefficient of a community can be used to
identify those that are functionally homogeneous. By tracing the community
membership of a protein through multiple scales we demonstrate how our approach
could be useful to biologists focusing on a particular protein.
Conclusions: We show that there is no one scale of interest in the community
structure of the yeast protein interaction network, but we can identify the
range of resolution parameters that yield the most functionally coherent
communities, and predict which communities are most likely to be functionally
homogeneous.Comment: 26 pages, 6 figure
Link communities reveal multiscale complexity in networks
Networks have become a key approach to understanding systems of interacting
objects, unifying the study of diverse phenomena including biological organisms
and human society. One crucial step when studying the structure and dynamics of
networks is to identify communities: groups of related nodes that correspond to
functional subunits such as protein complexes or social spheres. Communities in
networks often overlap such that nodes simultaneously belong to several groups.
Meanwhile, many networks are known to possess hierarchical organization, where
communities are recursively grouped into a hierarchical structure. However, the
fact that many real networks have communities with pervasive overlap, where
each and every node belongs to more than one group, has the consequence that a
global hierarchy of nodes cannot capture the relationships between overlapping
groups. Here we reinvent communities as groups of links rather than nodes and
show that this unorthodox approach successfully reconciles the antagonistic
organizing principles of overlapping communities and hierarchy. In contrast to
the existing literature, which has entirely focused on grouping nodes, link
communities naturally incorporate overlap while revealing hierarchical
organization. We find relevant link communities in many networks, including
major biological networks such as protein-protein interaction and metabolic
networks, and show that a large social network contains hierarchically
organized community structures spanning inner-city to regional scales while
maintaining pervasive overlap. Our results imply that link communities are
fundamental building blocks that reveal overlap and hierarchical organization
in networks to be two aspects of the same phenomenon.Comment: Main text and supplementary informatio
Protein multi-scale organization through graph partitioning and robustness analysis: Application to the myosin-myosin light chain interaction
Despite the recognized importance of the multi-scale spatio-temporal
organization of proteins, most computational tools can only access a limited
spectrum of time and spatial scales, thereby ignoring the effects on protein
behavior of the intricate coupling between the different scales. Starting from
a physico-chemical atomistic network of interactions that encodes the structure
of the protein, we introduce a methodology based on multi-scale graph
partitioning that can uncover partitions and levels of organization of proteins
that span the whole range of scales, revealing biological features occurring at
different levels of organization and tracking their effect across scales.
Additionally, we introduce a measure of robustness to quantify the relevance of
the partitions through the generation of biochemically-motivated surrogate
random graph models. We apply the method to four distinct conformations of
myosin tail interacting protein, a protein from the molecular motor of the
malaria parasite, and study properties that have been experimentally addressed
such as the closing mechanism, the presence of conserved clusters, and the
identification through computational mutational analysis of key residues for
binding.Comment: 13 pages, 7 Postscript figure
Detecting highly overlapping community structure by greedy clique expansion
In complex networks it is common for each node to belong to several
communities, implying a highly overlapping community structure. Recent advances
in benchmarking indicate that existing community assignment algorithms that are
capable of detecting overlapping communities perform well only when the extent
of community overlap is kept to modest levels. To overcome this limitation, we
introduce a new community assignment algorithm called Greedy Clique Expansion
(GCE). The algorithm identifies distinct cliques as seeds and expands these
seeds by greedily optimizing a local fitness function. We perform extensive
benchmarks on synthetic data to demonstrate that GCE's good performance is
robust across diverse graph topologies. Significantly, GCE is the only
algorithm to perform well on these synthetic graphs, in which every node
belongs to multiple communities. Furthermore, when put to the task of
identifying functional modules in protein interaction data, and college dorm
assignments in Facebook friendship data, we find that GCE performs
competitively.Comment: 10 pages, 7 Figures. Implementation source and binaries available at
http://sites.google.com/site/greedycliqueexpansion
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