Post on 04-Jan-2016
Biological networks
Tutorial 12
• Protein-Protein interactions– STRING
• Protein and genetic interactions– BioGRID
• Network visualization– Cytoscape
• Cool story of the day
How to model natural selection
Biological networks
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
Protein and genetic interactions
Protein and genetic interactions
Signaling pathways
Hearing and vision map
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
The node with the highest betweenness
Betweenness: quantify the number of all shortest paths that pass through a node.
Network visualization - Cytoscape
Know your network type:Directed – for regulatory networksUndirected – for protein-protein networks
Network visualization - Cytoscape
(Analysis of another network)
Network visualization - Cytoscape
Highest degree = bigHighest 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 case two tasks
The case of a trade-off between two tasks may explain the widespread occurrence of linearrelations between traits.
The Pareto Front
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 inthe organism’s habitat.
Evidence for triangular suites of variation in classic studies
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 ishow much of each gene to express.
Trade-off: rapid growth (ribosomes) vs. survival (stress response proteins)
Beyond animal morphology
Corr. of the top 200 temporally varying genes
E.coli promoter activity
Promoter activity of 3 genes at different time points
Thank you!Hope you enjoyed the course!!