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Functional topology in
a network of protein
interactions
http://bioinformatics.oxfordjournals.or
g/cgi/content/abstract/20/3/340
Bioinformatics, 2004
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Contributions
 Computational models
 Describing and Predicting the properties of lethal mutations
and proteins participating in genetic interactions, functional
groups, protein complexes and signaling pathways.
 The existence of alternative paths that bypass viable
proteins in PPI networks, while such paths do not exist for
lethal mutations
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Lethal Mutations
 Mutations
 Lethal, viable and genetic
 Lethal mutations are mutations that lead to a
phenotype incapable of effective reproduction
 Observations
 Highly connected within the network (Hubs)
 Their removal causes a disruption in network structure
(Articulation points)
 A point of disconnection in the network.
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System & Methods
 Degrees
 Groups of nodes with selected graph properties
 Shortest paths
 Clusters
 Important proteins
 Pathways
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Degrees
 The degree of a node in a graph is equal to the
number of edges containing that node.
 Can be computed using LEDA’s degree operations.
 The average, the SD (standard deviation) and the skew for
degrees can be also computed.
 MIPS(Munich Information Center for Protein
Sequences)
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LEDA
LEDA
A Platform for Combinatorial and Geometric Computing
Leda manual
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Groups of nodes
 Articulation point
 A node whose removal disconnects the graph.
 Can be determined by modifying LEDA’s implementation for
testing bi-connectedness of a graph
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Groups of nodes
 Hubs
 Highly connected nodes on MST (Minimum spanning tree)
of the graph.
 Only around 6% of nodes of the graph have a degree of at
least 5.
 All edges have a weight of 1.
 LEDA’s implementation of an MST algorithm
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Groups of nodes
 Siblings
 Nodes having the same neighborhood, where a
neighborhood of a node v is a set of all nodes that are
adjacent to v.
 Viable mutations.
 Comparing the rows and the columns corresponding to
every pair of nodes in the adjacency matrix of the graph.
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Groups of nodes
 Adjacency Matrix
1
2
3
4
5
1
0
1
1
0
0
2
1
0
0
0
0
3
1
0
0
1
1
4
0
0
1
0
1
5
0
0
1
1
0
1
3
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 Adjacency List
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1
2
2
1
3
1
4
4
3
5
5
3
4
2
3
5
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Shortest paths
 The minimum number of edges that has to be
traversed in the graph to get from one node to the
other.
 LEDA’s routine: AllPairsShortestPaths
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Clusters
 Highly Connected Subgraphs (HCS) algorithm
 HCS is a subgraph such that the minimum number k of
edges whose removal disconnects the graph is bigger than
n/2.
 Good homogeneity & separation properties
 LEDA’s routine: Components & HCS algorithm
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Discussions
 Lethal proteins
 Are more frequent in the top 3% of high degree nodes
 Highly connected within the network (Hubs)
 Their removal causes a disruption in network structure
(Articulation points)
 A point of disconnection in the network.
 Viable proteins
 Are more frequent in the nodes of degree 1
 Could be described as siblings or proteins participating in
genetic interactions
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Discussions
 The existence of alternative paths that bypass
viable proteins in PPI networks, while such paths
do not exist for lethal mutations.
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Discussions
 Protein complexes
 Determining which of the myriad of interactions comprise
true protein complexes
 Hypothesis: Highly connected subgraph or clusters within a
PPI network could indicate protein complexes
 Increasing size of the PPI graph,
the number of nodes in individual clusters increases,
while the number of identified clusters decreases.
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