CSCE555 BioinformaticsCSCE555 BioinformaticsLecture 18 Network Biology

Meeting: MW 4:00PM-5:15PM SWGN2A21

Instructor: Dr. Jianjun Hu

Course page: http://www.scigen.org/csce555

University of South CarolinaDepartment of Computer Science and Engineering

2008 www.cse.sc.edu.

OutlineOutline

Biological Networks & DatabasesBackground of graphs and

networksThree types of bio-network

analysis◦Network statistics◦Network based functional annotation◦Bio-network reconstruction/inference

Summary

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Why network analysis: Why network analysis: Building models from parts Building models from parts listslists

Systems Biology view

BIOLOGICAL NETWORKSBIOLOGICAL NETWORKS

Networks are found in biological systems of varying scales:

1. Evolutionary tree of life

2. Ecological networks 3. Expression networks4. Regulatory networks

- genetic control networks of organisms

5. The protein interaction network in cells6. The metabolic network in cells… more biological networks

Examples of Biological Examples of Biological NetworksNetworks

Metabolic NetworksSignaling NetworksTranscription Regulatory

NetworksProtein-Protein Interaction

Networks

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Signaling & Metabolic Pathway Signaling & Metabolic Pathway NetworkNetworkA Pathway can be defined as a modular

unit of interacting molecules to fulfill a cellular function.

Signaling Pathway Networks◦ In biology a signal or biopotential is an electric

quantity (voltage or current or field strength), caused by chemical reactions of charged ions.

◦ refer to any process by which a cell converts one kind of signal or stimulus into another.

◦ Another use of the term lies in describing the transfer of information between and within cells, as in signal transduction.

Metabolic Pathway Networks◦ a series of chemical reactions occurring within a cell,

catalyzed by enzymes, resulting in either the formation of a metabolic product to be used or stored by the cell, or the initiation of another metabolic pathway

A Signaling Pathway ExampleA Signaling Pathway Example

A Metabolic Pathway ExampleA Metabolic Pathway Example

Regulatory NetworkRegulatory Network

Expression NetworkExpression Network A network representation of genomic data. Inferred from genomic data, i.e. microarray.

Gene co-expression network. Each node is a gene. Edge: co-expression relationship

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Example of a PPI NetworkExample of a PPI Network Yeast PPI network Nodes – proteins Edges – interactions

The color of a node indicates the phenotypic effect of removing the corresponding protein (red = lethal, green = non-lethal, orange = slow growth, yellow = unknown).

How do we know that proteins How do we know that proteins interact? (PPI Identification interact? (PPI Identification methods)methods) Data

◦Yeast 2 hybrid assay◦Mass spectrometry◦Correlated m-RNA expression◦Genetic interactions

Analysis

◦Phylogenetic analysis◦Gene neighbors◦Co-evolution◦Gene clusters

Also see: Comparative assessment of large-scale data sets of protein-protein interactions – von Mering

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Protein Interaction Protein Interaction DatabasesDatabases Species-specific

◦ FlyNets - Gene networks in the fruit fly ◦ MIPS - Yeast Genome Database ◦ RegulonDB - A DataBase On Transcriptional Regulation in

E. Coli ◦ SoyBase ◦ PIMdb - Drosophila Protein Interaction Map database

Function-specific◦ Biocatalysis/Biodegradation Database ◦ BRITE - Biomolecular Relations in Information

Transmission and Expression ◦ COPE - Cytokines Online Pathfinder Encyclopaedia ◦ Dynamic Signaling Maps ◦ EMP - The Enzymology Database ◦ FIMM - A Database of Functional Molecular Immunology ◦ CSNDB - Cell Signaling Networks Database

Protein Interaction Protein Interaction DatabasesDatabases Interaction type-specific

◦ DIP - Database of Interacting Proteins ◦ DPInteract - DNA-protein interactions ◦ Inter-Chain Beta-Sheets (ICBS) - A database of protein-

protein interactions mediated by interchain beta-sheet formation

◦ Interact - A Protein-Protein Interaction database ◦ GeneNet (Gene networks)

General◦ BIND - Biomolecular Interaction Network Database ◦ BindingDB - The Binding Database ◦ MINT - a database of Molecular INTeractions ◦ PATIKA - Pathway Analysis Tool for Integration and

Knowledge Acquisition ◦ PFBP - Protein Function and Biochemical Pathways

Project ◦ PIM (Protein Interaction Map)

Pathway DatabasesPathway Databases KEGG (Kyoto Encyclopedia of Genes and Genomes)

http://www.genome.ad.jp/kegg/ Institute for Chemical Research, Kyoto University

PathDB http://www.ncgr.org/pathdb/index.html National Center for Genomic Resources

SPAD: Signaling PAthway Database Graduate School of Genetic Resources Technology.

Kyushu University. Cytokine Signaling Pathway DB.

Dept. of Biochemistry. Kumamoto Univ. EcoCyc and MetaCyc

Stanford Research Institute BIND (Biomolecular Interaction Network Database)

UBC, Univ. of Toronto

KEGGKEGG Pathway Database: Computerize current knowledge

of molecular and cellular biology in terms of the pathway of interacting molecules or genes.

Genes Database: Maintain gene catalogs of all sequenced organisms and link each gene product to a pathway component

Ligand Database: Organize a database of all chemical compounds in living cells and link each compound to a pathway component

Pathway Tools: Develop new bioinformatics technologies for functional genomics, such as pathway comparison, pathway reconstruction, and pathway design

Network Properties

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Properties of networksProperties of networks Small world effectTransitivity/ ClusteringScale Free EffectMaximum degreeNetwork Resilience and robustnessMixing patterns and assortativityCommunity structureEvolutionary originBetweenness centrality of vertices

Biological Networks Biological Networks PropertiesProperties Power law degree distribution: Rich get

richerSmall World: A small average path length

◦Mean shortest node-to-node pathRobustness: Resilient and have strong

resistance to failure on random attacks and vulnerable to targeted attacks

Hierarchical Modularity: A large clustering coefficient◦How many of a node’s neighbors are

connected to each other

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Graph TerminologyGraph TerminologyNode Edge

Directed/UndirectedDegree

Shortest Path/Geodesic distanceNeighborhood

SubgraphComplete Graph

CliqueDegree Distribution

Hubs

GraphsGraphs Graph G=(V,E) is a set of vertices V and edges E

A subgraph G’ of G is induced by some V’ V and E’ E

Graph properties:◦ Connectivity (node degree, paths)◦ Cyclic vs. acyclic◦ Directed vs. undirected

Network MeasuresNetwork Measures

Degree ki

Degree distribution P(k)

Mean path length

Network Diameter

Clustering Coefficient

Paths:metabolic, signaling pathways

Cliques:protein complexes

Hubs:regulatory modules

Subgraphs:maximally weighted

Network AnalysisNetwork Analysis

Sparse Sparse vsvs Dense Graphs Dense GraphsG(V, E) where |V|=n, |E|=m the

number of vertices and edges

Graph is sparse if m~n

Graph is dense if m~n2

Complete graph when m=n2

Connected ComponentsConnected Components

G(V,E)|V| = 69|E| = 71

Connected ComponentsConnected Components

G(V,E)|V| = 69|E| = 716

connected components

PathsPathsA path is a sequence {x1, x2,…, xn} such that (x1,x2), (x2,x3), …, (xn-1,xn) are edges of the graph.

A closed path xn=x1 on a graph is called a graph cycle or circuit.

Shortest-Path between Shortest-Path between nodesnodes

Shortest-Path between Shortest-Path between nodesnodes

Longest Shortest-PathLongest Shortest-Path

Network Measures: Network Measures: DegreeDegree

P(k) is probability of each degree k, i.e fraction of nodes having that degree.

For random networks, P(k) is normally distributed.

For real networks the distribution is often a power-law:

P(k) ~ k

Such networks are said to be scale-free

Degree DistributionDegree Distribution

1

2

2

kk

nkn

C III

k: neighbors of I

nI: edges between

node I’s neighbors

The density of the network surrounding node I, characterized as the number of triangles through I. Related to network modularity

The center node has 8 (grey) neighbors

There are 4 edges between the neighbors

C = 2*4 /(8*(8-1)) = 8/56 = 1/7

Clustering CoefficientClustering Coefficient

Interesting Properties of Network Types

Small-world Network Small-world Network Every node can be reached from every

other by a small number of hops or steps

High clustering coefficient and low mean-shortest path length◦ Random graphs don’t necessarily have high

clustering coefficients

Social networks, the Internet, and biological networks all exhibit small-world network characteristics

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Small world effectSmall world effectmost pairs of vertices in the network seem to

be connected by a short path

l is mean geodesic distance dij is the geodesic distance between vertex i and vertex j

l ~ log(N)

Scale-Free Networks are Scale-Free Networks are RobustRobustComplex systems (cell, internet, social

networks), are resilient to component failure

Network topology plays an important role in this robustness◦ Even if ~80% of nodes fail, the remaining ~20% still

maintain network connectivity

Attack vulnerability if hubs are selectively targeted

In yeast, only ~20% of proteins are lethal when deleted, and are 5 times more likely to have degree k>15 than k<5.

Hierarchical NetworksHierarchical Networks

Detecting Hierarchical Detecting Hierarchical OrganizationOrganization

Other Interesting FeaturesOther Interesting FeaturesCellular networks are assortative, hubs tend

not to interact directly with other hubs.

Hubs tend to be “older” proteins (so far claimed for protein-protein interaction networks only)

Hubs also seem to have more evolutionary pressure—their protein sequences are more conserved than average between species (shown in yeast vs. worm)

Experimentally determined protein complexes tend to contain solely essential or non-essential proteins—further evidence for modularity.

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Network ModelsNetwork Models

Random NetworkScale free NetworkHierarchical Network

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Random Network IRandom Network I

The Erdös–Rényi (ER) model of a random network starts with N nodes and connects each pair of nodes with probability p, which creates a graph with approximately pN(N–1)/2 randomly placed links

The node degrees follow a Poisson distribution

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Random Network IIRandom Network II

Mean shortest path l ~ log N, which indicates that it is characterized by the small-world property. Random graphs have served as idealized models of certain gene networks, ecosystems and the spread of infectious diseases and computer viruses.

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Scale Free Networks IScale Free Networks I

Power-law degree distribution: P(k) ~ k –γ, where γ is the degree exponent. Usually 2-3 The network’s properties are determined by hubsThe network is often generated by a growth process called Barabási–Albert model

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Scale Free Networks IIScale Free Networks IIScale-free networks with degree exponents 2<γ<3, a range that is observed in most biological and non-biological networks like the Internet backbone, the World Wide Web, metabolic reaction network and telephone call graphs.

The mean shortest path length is proportional to log(n)/log(log(n))

PREFERENTIAL ATTACHMENT on Growth: the probability that a new vertex will be connected to vertex i depends on the connectivity of that vertex:

How Scale-free networks are How Scale-free networks are formed? formed?

( ) ii

jj

kk

k

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Hierarchical Networks IHierarchical Networks I To account for the coexistence of

modularity, local clustering and scale-free topology in many real systems it has to be assumed that clusters combine in an iterative manner, generating a hierarchical network

The hierarchical network model seamlessly integrates a scale-free

topology with an inherent modular structure by generating a network that has a power-law degree distribution with degree exponent γ = 1 + ln4/ln3 = 2.26

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Hierarchical Networks IIHierarchical Networks IIIt has a large system-size independent

average clustering coefficient <C> ~ 0.6. The most important signature of hierarchical modularity is the scaling of the clustering coefficient, which follows C(k) ~ k –1 a straight line of slope –1 on a log–log plot

A hierarchical architecture implies that sparsely connected nodes are part of highly clustered areas, with communication between the different highly clustered neighborhoods being maintained by a few hubsSome examples of hierarchical scale free networks.

Problems of Network Problems of Network BiologyBiology Network Inference

Micro Array, Protein Chips, other high throughput assay methods

Function prediction

The function of 40-50% of the new proteins is unknown

Understanding biological function is important for: Study of fundamental biological processes Drug design Genetic engineering

Functional module detection Cluster analysis

Topological Analysis Descriptive and Structural Locality Analysis Essential Component Analysis

Dynamics Analysis Signal Flow Analysis Metabolic Flux Analysis Steady State, Response, Fluctuation Analysis

Evolution Analysis Biological Networks are very rich networks with very limited,

noisy, and incomplete information. Discovering underlying principles is very challenging.

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SummarySummaryThe problem: Identify

Differentially expressed genes from Microarray data

How to identify: t-test and Rank product

How to evaluate significance of identified genes

Reference & Reference & AcknowledgementsAcknowledgements Albert Barabasi et al

◦ Network Biology: understanding the cell’s functional organization

Jing-Dong et al◦ Evidence for dynamically organized

modularity in the yeast protein–protein interaction network

Woochang Hwang