Biological Networks
description
Transcript of Biological Networks
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Biological Networks
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Can a biologist fix a radio?
Lazebnik, Cancer Cell, 2002
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Building models from parts lists
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Protein-DNAinteractions
Gene levels(up/down)
Protein-proteininteractions
Protein levels(present/absent)
Biochemicalreactions
Biochemicallevels
▲ Chromatin IP ▼ DNA microarray
▲ Protein coIP▼ Mass spectrometry
▲noneMetabolic flux ▼
measurements
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Computational tools are needed to distill pathways of interest from large molecular interaction databases
Data integration and statistical mining
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Types of information to integrate• Data that determine the network (nodes and edges)
– protein-protein– protein-DNA, etc…
• Data that determine the state of the system– mRNA expression data– Protein levels– Dynamics over time
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Networks can help to predict function
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Mapping the phenotypic data to the network
Begley TJ, Mol Cancer Res. 2002
•Systematic phenotyping of 1615 gene knockout strains in yeast•Evaluation of growth of each strain in the presence of MMS (and other DNA damaging agents)•Screening against a network of 12,232 protein interactions
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Mapping the phenotypic data to the network
Begley TJ, Mol Cancer Res. 2002
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Mapping the phenotypic data to the network
Begley TJ, Mol Cancer Res. 2002
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Networks can help to predict function
Begley TJ, Mol Cancer Res. 2002.
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Networks Topology
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gene A gene Bregulates
gene A gene Bbinds
gene A gene B
reaction product
is a substrate for
regulatory interactions(protein-DNA)
functional complexB is a substrate of A
(protein-protein)
metabolic pathways
Network Representation
node edge
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Paths:metabolic, signaling pathways
Cliques:protein complexes
Hubs:regulatory modules
Network Analysis
node edge
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Small-world Network
• Social networks, the Internet, and biological networks all exhibit small-world network characteristics
• Every node can be reached from every other by a small number of steps
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Shortest-Path between nodes
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Shortest-Path between nodes
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Longest Shortest-Path
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Small-world Network
• Social networks, the Internet, and biological networks all exhibit small-world network characteristics
• Every node can be reached from every other by a small number of steps
• Small World Networks are characterized by high clustering coefficient and low mean-
shortest path length
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Scale Free Networks
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Scale-Free Networks are Robust• Complex systems (cell, internet, social
networks), are resilient to component failure
• Network topology plays an important role in this robustness– Even if ~80% of nodes fail, the remaining ~20% still maintain
network connectivity– Network is very sensitive if the hubs are “attacked”
• In yeast, only ~20% of proteins are lethal when deleted,
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Features of cellular Networks
• Cellular networks are assortative, hubs tend not to interact directly with other hubs.
• Hubs tend to be “older” proteins (so far claimed for protein-protein interaction networks only)
• Hubs also seem to have more evolutionary pressure—their protein sequences are more conserved than average between species (shown in yeast vs. worm)
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Looking at macromolecular structures as a network
How to Indentify critical position in the newtwork?
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Searching for critical positions in a network ?
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Searching for critical positions in a network ?
High degree
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Searching for critical positions in a network ?
High closeness
High degree
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Searching for critical positions in a network ?
High closeness
High degree
High betweenness
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Looking at macromolecular structures as a network
A1191 = highest degree, closeness, betweenness
A1191
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Identifying Deleterious Mutationsusing a network approach
Strong mutations
Mild mutations
12
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Identifying Deleterious Mutations
p~0
p~0
p=0.01
There is a significant overlap between (predicted) functional nucleotides and critical positions of the network (high betweenness and high closeness