Complexity Issues in Bioinformatics

4/27/02


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Table of Contents

Complexity Issues in Bioinformatics

Biology’s dilemma: There is too much to know about living things

Some important model organisms

Let’s find out everything about some species

Some genome sizes

Sequencing Genomes

Where to store all these data?

What’s in the databases?

What’s in the databases?

What’s in the databases?

Time-complexity of algorithms

Consequences of database growth

How to overcome the problem?

You have sequenced your genome - what do you do with it?

From genes to proteins

What did the gene finding algorithms find?

So we know the genes - do we know everything?

Prediction of gene function

How to detect similarity of genes?

Example of an alignment

How to score an alignment

Prediction of gene function: homology searches

Prediction of important sites in proteins

How to predict binding sites from sequence data:

Example of a multiple alignment

How to score multiple sequence alignments?

Computational complexity of multiple sequence alignment

The class P

The class NP

The P = NP problem

Computational complexity of multiple sequence alignment

Polynomial-time approximation schemes

Computational complexity of multiple sequence alignment

A few words about the proof

An open question

An partial answer

The practice of multiple sequence alignment

Using genomic data for reconstruction of phylogenies

Reconstructing phylogenies: How to get started

Methods for reconstruction of phylogenies

Methods for reconstruction of phylogenies

The computational challenge for reconstruction of phylogenies

Reconstruction of phylogenies: A success story

Gene interactions: Collecting gene expression data

Gene interactions: Interpeting gene expression data

Interpeting gene expression data: A mathematical challenge

Gene expression profiles: A success story

Author: Winfried Just

Email: just@math.ohiou.edu

Home Page: http://www.math.ohiou.edu/~just

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