CSCE555 Bioinformatics

1. CSCE555 Bioinformatics • Lecture 18 Network Biology Meeting: MW 4:00PM-5:15PM SWGN2A21 Instructor: Dr. Jianjun Hu Course page: http://www.scigen.org/csce555 University of South Carolina Department of Computer Science and Engineering 2008 www.cse.sc.edu.

2. Outline • Biological Networks & Databases • Background of graphs and networks • Three types of bio-network analysis • Network statistics • Network based functional annotation • Bio-network reconstruction/inference • Summary

3. Why network analysis: Building models from parts lists Systems Biology view

4. BIOLOGICAL NETWORKS Networks are found in biological systems of varying scales: 1. Evolutionary tree of life 2. Ecological networks 3. Expression networks 4. Regulatory networks - genetic control networks of organisms 5. The protein interaction network in cells 6. The metabolic network in cells … more biological networks

5. Examples of Biological Networks • Metabolic Networks • Signaling Networks • Transcription Regulatory Networks • Protein-Protein Interaction Networks

6. Signaling & Metabolic Pathway Network • A 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

7. A Signaling Pathway Example

8. A Metabolic Pathway Example

9. Regulatory Network

10. Expression 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

11. Example 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).

12. How do we know that proteins interact? (PPI Identification 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

13. Protein Interaction Databases • 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

14. Protein Interaction Databases • 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)

15. Pathway 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

16. KEGG • 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

17. Network Properties

18. Properties of networks • Small world effect • Transitivity/ Clustering • Scale Free Effect • Maximum degree • Network Resilience and robustness • Mixing patterns and assortativity • Community structure • Evolutionary origin • Betweenness centrality of vertices

19. Biological Networks Properties • Power law degree distribution: Rich get richer • Small World: A small average path length • Mean shortest node-to-node path • Robustness: 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

20. Graph Terminology Node Edge Directed/Undirected Degree Shortest Path/Geodesic distance Neighborhood Subgraph Complete Graph Clique Degree Distribution Hubs

21. Graphs • 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

22. Network Measures • Degree ki • Degree distribution P(k) • Mean path length • Network Diameter • Clustering Coefficient

23. Network Analysis Paths: metabolic, signaling pathways Cliques: protein complexes Hubs: regulatory modules Subgraphs: maximally weighted

24. Sparse vsDense Graphs • G(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

25. Connected Components • G(V,E) • |V| = 69 • |E| = 71

26. Connected Components • G(V,E) • |V| = 69 • |E| = 71 • 6 connected components

27. Paths A 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.

28. Shortest-Path between nodes

29. Shortest-Path between nodes

30. Longest Shortest-Path

31. Network Measures: Degree

32. Degree Distribution 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-g Such networks are said to be scale-free

33. Clustering Coefficient The density of the network surrounding node I, characterized as the number of triangles through I. Related to network modularity k: neighbors of I nI: edges between node I’s neighbors The center node has 8 (grey) neighbors There are 4 edges between the neighbors C = 2*4 /(8*(8-1)) = 8/56 = 1/7

34. Interesting Properties of Network Types

35. 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

36. Small world effect • most 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)

37. Scale-Free Networks are Robust • Complex 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.

38. Hierarchical Networks

39. Detecting Hierarchical Organization

40. Other Interesting Features • Cellular 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.

41. Network Models • Random Network • Scale free Network • Hierarchical Network

42. Random 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

43. Random 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.

45. Scale 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 hubs The network is often generated by a growth process called Barabási–Albert model

46. Scale Free Networks II • Scale-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))

47. How Scale-free networks are formed? • PREFERENTIAL ATTACHMENT on Growth: the probability that a new vertex will be connected to vertex i depends on the connectivity of that vertex: In biological network, many such networks are due to gene duplication!

48. Hierarchical 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

49. Hierarchical Networks II • It 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 hubs • Some examples of hierarchical scale free networks.

50. Problems of Network Biology • 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.