Introduction to Bioinformatics - UCL Computer Science · Introduction to Bioinformatics Biological...

Introduction to Bioinformatics

Biological Networks

Department of ComputingImperial College London

Spring 2010

Nataša Pržuljnatasha@imperial.ac.uk

• 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),

• …

1. Motivation

Nataša Pržuljnatasha@imperial.ac.uk

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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),

• …

Nataša Pržuljnatasha@imperial.ac.uk

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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),

• …

Nataša Pržuljnatasha@imperial.ac.uk

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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

Nataša Pržuljnatasha@imperial.ac.uk

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• Why model biological networks?– Concise summary, unexpected properties– Understand laws predictions/reproduction

• E.g. – Johannes Kepler (1571-1630)

» Observed planetary motion– Sir Isaac Newton (1643-1727)

» Universal gravitation, laws of motion Explained planetary motion

1. Motivation

Nataša Pržuljnatasha@imperial.ac.uk

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Problems:

1. Noise revise models as data sets evolve2. “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

1. Motivation

Nataša Pržuljnatasha@imperial.ac.uk

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1. MotivationProperties 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)

Nataša Pržuljnatasha@imperial.ac.uk

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1. MotivationExamples of different model networks:

Nataša Pržuljnatasha@imperial.ac.uk

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http://www.doc.ic.ac.uk/~natasha/course_outline_bio_nets_2010.pdf

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Assumed Knowledge of Graph Theory and Algorithms:

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Assumed Knowledge of Graph Theory and Algorithms:

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Assumed Knowledge of Graph Theory and Algorithms:

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Assumed Knowledge of Graph Theory and Algorithms:

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Assumed Knowledge of Graph Theory and Algorithms:

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Assumed Knowledge of Graph Theory and Algorithms:

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Assumed Knowledge of Graph Theory and Algorithms:

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Assumed Knowledge of Graph Theory and Algorithms:

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Assumed Knowledge of Graph Theory and Algorithms:

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Assumed Knowledge of Graph Theory and Algorithms:

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Assumed Knowledge of Graph Theory and Algorithms:

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A program for testing isomorphism and automorphism of graphs:Brendan McKay’s “nauty”: http://cs.anu.edu.au/~bdm/nauty/

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Assumed Knowledge of Graph Theory and Algorithms:

Nataša Pržuljnatasha@imperial.ac.uk

Example:

abc

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}.

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Assumed Knowledge of Graph Theory and Algorithms:

Nataša Pržuljnatasha@imperial.ac.uk

- Pseudocode (Chpt 1.1)- Growth Rate of Running Time

by Goodrich and Tamassia