1. Biological networks
CS 5263 Bioinformatics

2. Administrative issues
Today is last lecture of the semester
No class on Wed
All presentations on Wed, Dec 10, 7:00-9:30 pm
Turn in your project report the same day
soft copy required, hard copy appreciated

3. Presentation details 12 teams to present
Each team will have up to 12 minutes. (10 min presentation, 2 min questions)
Since time is limited, you don’t need to cover all the methods in detail in your presentation.
Focus on at most two to three methods
More details in your project report

4. Today’s lecture: biological networks
One of the most dynamic research areas
Involves people from math/physics/cs/stats/bio/…
I’ll provide you a brief survey about some basic concepts, and a few interesting (but may be controversial) research results

5. Lecture outline Basic terminology and concepts in networks
Biological networks (what kind? How to get them?)
Network properties
Some interesting results in bio networks

6. Why (biological) networks?
For complex systems, the actual output may not be predictable by looking at only individual components:
The whole is greater than the sum of its parts

7. Network A network refers to a graph
An useful concept in analyzing the interactions of different components in a system

8. Biological networks
An abstract of the complex relationships among molecules in the cell
Many types.
Protein-protein interaction networks
Protein-DNA(RNA) interaction networks
Genetic interaction network
Metabolic network
Signal transduction networks
(real) neural networks
Many others
In some networks, edges have more precisely meaning. In some others, meaning of edges is obscure

9. Protein Interaction: Transcription Regulation

10. Protein-protein interaction networks

11. Obtaining biological networks
Direct experimental methods
Protein-protein interaction networks
Yeast-2-hybrid
Tandem affinity purification
Co-immunoprecipitation
Protein-DNA interaction
Chromatin Immunoprecipitation (followed by microarray or sequencing, ChIP-chip, ChIP-seq)
Usually have high level of noises (false-positive and false-negative)
Computational prediction methods
Even higher-level of noises
Often cannot differentiate direct and indirect interactions

12. Structural properties of networks
Degree distribution
Mean shortest distance
Clustering coefficient
Community structure
Degree correlation
Assumption:
Structural determine function
Important (i.e. functional) structure properties may be shared by different types of real networks (bio or non-bio), but may be missing in random networks
It is possible to categorize networks based on their structural properties and to obtain insights into the organizing principles of complex systems

13. Degree/connectivity, k
How many links the node has to other nodes?
Undirected network
Characterized by an average degree <k> = 2L/N
N nodes and L links
Directed network
Incoming degree, kin
Outgoing degree, kout

14. Shortest and mean path length
Distance in networks is measured with the path length
As there are many alternative paths between two nodes, the shortest path between the selected nodes has a special role.
In directed networks,
AB is often different from the BA
Often there is no direct path between two nodes.
The average path length between all pairs of nodes offers a measure of a network’s overall navigability.

15. Degree distribution P(k)
The probability that a selected node has exactly (or approximately) k links.
P(k) is obtained by counting the number of nodes N(k) with k = 1, 2… links dividing by the total number of nodes N.

16. Clustering coefficient
Your clustering coefficient: the probability that two of your friends are also friends
You have m friends
Among your m friends, there are n pairs of friends
The maximum is m * (m-1) / 2
C = 2 n / (m^2-m)
Clustering coefficient of a network: the average clustering coefficient of all individuals

18. Degree correlation
Do rich people tend to hang together with rich people (rich-club)?
Or do they tend to interact with less wealthy people?
Do high degree nodes tend to connect to low degree nodes or high degree ones?

19. Community structure

20. Basic properties of biological networks
What’s the characteristic differences between real biological networks and random networks?
Small-world
Scale-free
What do we mean by random networks?

21. Erdos-Renyi model
Each pair of nodes have a probability p to form an edge
Most nodes have about the same # of connections
Degree distribution is binomial or Poisson

22. Real networks: scale-free
Heavy tail distribution
Power-law distribution
P(k) = k-r

23. Robust yet fragile nature of networks

24. Other properties of biological networks
Small-world
Small mean shortest distances
High clustering coefficient
Negative degree correlation
Community structure
What are the biological significance of these properties?

25. Some interesting findings from biological networks
Jeong, Lethality and centrality in protein networks. Nature 411, (3 May 2001)
Roger Guimerà and Luís A. Nunes Amaral, Functional cartography of complex metabolic networks. Nature 433, (24 February 2005)
Han, et. al. Evidence for dynamically organized modularity in the yeast protein–protein interaction network. Nature 430, (1 July 2004)

26. Connectivity vs essentiality
% of essential proteins
Number of connections
Jeong et. al. Nature 2001

27. Community role vs essentiality
Effect of a perturbation cannot depend on the node’s degree only!
Many hub genes are not essential
Some non-hub genes are essential
Maybe a gene’s role in her community is also important
Local leader? Global leader? Ambassador?
Guimerà and Amaral, Nature 433, 2005

28. Role 1, 2, 3: non-hubs with increasing participation indices
Role 5, 6: hubs with increasing participation indices

29. Dynamically organized modularity in the yeast PPI network
Protein interaction networks are static
Two proteins cannot interact if one is not expressed
We should look at the gene expression level
Han, et. al, Nature 430, 2004

30. Obtaining Data

31. Distinguish party hubs from date hubs
Red curve – hubs
Cyan curve – nonhubs
Black curve – randomized
Partners of date hubs are significantly more diverse in spatial distribution than partners of party hubs

32. Effect of removal of nodes on average geodesic distance
Original Network
On removal of date hubs
On removal of party hubs
Green – nonhub nodes
Brown – hubs
Red – date hubs
Blue – party hubs
The ‘breakdown point’ is the threshold after which the main component of the network starts disintegrating.

33. Dynamically organized modularity
Red circles – Date hubs
Blue squares - Modules