Biological networks

Biological networks

CS 5263 Bioinformatics

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

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

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

Lecture outline

• Basic terminology and concepts in networks

• Biological networks (what kind? How to get them?)

• Network properties

• Some interesting results in bio networks

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

Network

• A network refers to a graph

• An useful concept in analyzing the interactions of different components in a system

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

Protein Interaction: Transcription Regulation

http://www.cifn.unam.mx/Computational_Genomics/old_research/FIG22.gif

Protein-protein interaction networks

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

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

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

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.

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.

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

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?

Community structure

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?

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

Real networks: scale-free

• Heavy tail distribution– Power-law distribution

• P(k) = k-r

0 10 20 30 40 50 600

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Number of connections

Nu

mbe

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f ge

nes

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10-1

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Number of connections

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Robust yet fragile nature of networks

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?

Some interesting findings from biological networks

• Jeong, Lethality and centrality in protein networks. Nature 411, 41-42 (3 May 2001)

• Roger Guimerà and Luís A. Nunes Amaral, Functional cartography of complex metabolic networks. Nature 433, 895-900 (24 February 2005)

• Han, et. al. Evidence for dynamically organized modularity in the yeast protein–protein interaction network. Nature 430, 88-93 (1 July 2004)

Connectivity vs essentiality

Number of connections

% o

f ess

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al pro

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Jeong et. al. Nature 2001

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

• Role 1, 2, 3: non-hubs with increasing participation indices

• Role 5, 6: hubs with increasing participation indices

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

Obtaining Data

Distinguish party hubs from date hubs

Red curve – hubsCyan curve – nonhubsBlack curve – randomized• Partners of date hubs are significantly more diverse in spatial distribution

than partners of party hubs

Effect of removal of nodes on average geodesic distance

Green – nonhub nodesBrown – hubsRed – date hubsBlue – party hubsThe ‘breakdown point’ is the threshold after which the main component of the network starts disintegrating.

Original Network

On removal of date hubs

On removal of party hubs

Dynamically organized modularity

Red circles – Date hubsBlue squares - Modules