Introduction to Bioinformatics
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1 Introduction to Bioinformatics Biological Networks Department of Computing Imperial College London Spring 2010
2 1. Motivation Large Networks model many real-world phenomena technological: www, internet, electric circuits,... social: friendship, collaboration, disease spread,... biological: protein structure, transcriptional regulation, metabolic, protein-protein interaction (PPI), 2
3 1. Motivation Large Networks model many real-world phenomena technological: www, internet, electric circuits,... social: friendship, collaboration, disease spread,... biological: protein structure, transcriptional regulation, metabolic, protein-protein interaction (PPI), 3
4 1. Motivation Large Networks model many real-world phenomena technological: www, internet, electric circuits,... social: friendship, collaboration, disease spread,... biological: protein structure, transcriptional regulation, metabolic, protein-protein interaction (PPI), 4
5 1. Motivation Large-scale networks in bioinformatics: Technological advances in experimental biology data Important computational problems Algorithmic and modeling advances contribute: biological understanding (function, disease, pathogens, ) therapeutics Booming research area 5
6 1. Motivation Why model biological networks? Concise summary, unexpected properties Understand laws predictions/reproduction E.g. Johannes Kepler ( )» Observed planetary motion Sir Isaac Newton ( )» Universal gravitation, laws of motion Explained planetary motion 6
7 1. Motivation Problems: 1. Noise revise models as data sets evolve 2. Hardness of graph theoretic problems E.g. NP-completeness of subgraph isomorphism Cannot exactly compare/align networks heuristics (approximate solutions) Exact comparison inappropriate in biology due to biological variation 7
8 1. Motivation Properties of Large Networks (heuristic comparisons) Global Degree distribution Diameter Clustering coefficient/spectrum Local: network motifs and subgraphs (U. Alon s group, 02-04, Przulj 2004) 8
9 1. Motivation Examples of different model networks: 9
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15 Assumed Knowledge of Graph Theory and Algorithms: 15
16 Assumed Knowledge of Graph Theory and Algorithms: 16
17 Assumed Knowledge of Graph Theory and Algorithms: 17
18 Assumed Knowledge of Graph Theory and Algorithms: 18
19 Assumed Knowledge of Graph Theory and Algorithms: 19
20 Assumed Knowledge of Graph Theory and Algorithms: 20
21 Assumed Knowledge of Graph Theory and Algorithms: 21
22 Assumed Knowledge of Graph Theory and Algorithms: 22
23 Assumed Knowledge of Graph Theory and Algorithms: 23
24 Assumed Knowledge of Graph Theory and Algorithms: 24
25 Assumed Knowledge of Graph Theory and Algorithms: 25
26 Assumed Knowledge of Graph Theory and Algorithms: A program for testing isomorphism and automorphism of graphs: Brendan McKay s nauty : 26
27 Assumed Knowledge of Graph Theory and Algorithms: Example: a b c Node a can be mapped to c by an automorphism, and b can only be mapped to itself. Thus: Orb(a)={a,c}, Orb(b)={b}. 27
28 Assumed Knowledge of Graph Theory and Algorithms: by Goodrich and Tamassia - Pseudocode (Chpt 1.1) - Growth Rate of Running Time 28
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