Transcript Tutorial_13 (2014)

Tutorial 12
Biological networks
Biological networks
• Protein-Protein interactions
– STRING
• Protein and genetic interactions
– BioGRID
• Network visualization
– Cytoscape
• Cool story of the day
How to model natural selection
Protein Protein interactions (PPI)
http://string-db.org/
Protein Protein interactions (PPI)
Protein Protein interactions (PPI)
Protein Protein interactions (PPI)
Protein Protein interactions (PPI)
Protein Protein interactions (PPI)
Protein Protein interactions (PPI)
Will change
according to
the prediction
method you
choose.
Protein Protein interactions (PPI)
Protein Protein interactions (PPI)
Protein Protein interactions (PPI)
Protein and genetic interactions
http://thebiogrid.org/
Protein and genetic interactions
Protein and genetic interactions
Protein and genetic interactions
Signaling pathways
Hearing and vision map
Network visualization - Cytoscape
http://www.cytoscape.org/
Network visualization - Cytoscape
The input is a tab delimited file:
<Protein 1> <interaction type> <Protein 2>
Network visualization - Cytoscape
Network visualization - Cytoscape
Network visualization - Cytoscape
Network visualization - Cytoscape
Network visualization - Cytoscape
Degree: the number of edges that a node has.
The node with the highest degree in the graph
Network visualization - Cytoscape
Closeness: measure how close a node to all other nodes in the network.
The nodes with the highest closeness
Network visualization - Cytoscape
Betweenness: quantify the number of all shortest paths that pass through a node.
The node with the highest betweenness
Network visualization - Cytoscape
Know your network type:
Directed – for regulatory networks
Undirected – for protein-protein networks
Network visualization - Cytoscape
(Analysis of another network)
Network visualization - Cytoscape
Highest degree = big
Highest betweens = red
Network visualization - Cytoscape
Cytoscape has ~200 plugins
http://chianti.ucsd.edu/cyto_web/plugins/
Cool Story of the day
How to model natural selection
Natural Selection
• Consider a biological system whose phenotypes are
defined by v quantitative traits (such as bird beak
length and not DNA sequences).
• Most theories of natural selection maximize a
specific fitness function F(v) resulting in an optimal
phenotype – a point in morpho-space.
• But, in many cases organisms need to perform
multiple tasks that contribute to fitness.
The Pareto Front
The case two tasks
The case of a trade-off between
two tasks may explain the
widespread occurrence of linear
relations between traits.
Pareto front geometry
For three tasks, the Pareto front is the full triangle whose vertices
are the three archetypes. In this case, because a triangle defines
a plane, even high dimensional data on many traits are
expected to collapse onto two dimensions.
The closer a point is to one of the vertices of the triangle, the
more important the corresponding task is to fitness in
the organism’s habitat.
Evidence for triangular suites of variation
in classic studies
Beyond animal morphology
Bacteria face a trade-off in partitioning the total amount of proteins they can
make at a given moment between the different types of proteins, that is
how much of each gene to express.
Trade-off: rapid growth (ribosomes) vs. survival (stress response proteins)
E.coli promoter
activity
Corr. of the top 200 temporally
varying genes
Promoter activity of 3 genes at
different time points
Thank you!
Hope you enjoyed the course!!