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

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 % of essential proteins Number of connections 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 – hubs Cyan curve – nonhubs Black 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 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.

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