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Abstracts


Biochemical and molecular foundations of bioinformatics

Chris Weise
(Freie Universität Berlin)


In this introductory lecture I will present some basic aspects of biochemistry. I will explain the relation between nucleic acids as an information store and proteins as the executors of diverse cellular functions. I will then focus on proteins, discussing the relation between structure and function and going through the different structural levels and the fundamental chemical interactions which determine their structure. I will set forth the importance of proteomics research in the "post-genome era" and try to define where, from a biochemist's point of view, there are possible interfaces between biochemistry and informatics.


Algorithmic aspects of genome assembly

Daniel Huson
(Celera Genomics Corp., Rockville, Maryland)


In modern biology, knowledge of the complete genome of a species is seen as a fundamental step towards it's full understanding. The unraveling of the human genome, in particular, is of great scientific importance, and publication of a first draft of the human genome is expected by the end of the year. We will first review a number of different sequencing and assembly strategies and then focus on some of the associated algorithmic problems.


Analysis of Completely Sequenced Genomes

Jens Stoye
(Deutsches Krebsforschungszentrum, Heidelberg)


The emergence of entire genomes in recent and coming years allows to study the function and evolution of organisms on a completely new basis. Mandatory for such studies, however, is the existence of computational tools that can handle and analyse large amounts of sequence data like the human genome which is expected to consist of up to 3.5 billion bases (letters). We will review data structures and algorithms for the analysis of large genomic sequences. In particular, we will discuss suffix trees and efficient algorithms using this data structure.


Bioinformatics of gene expression data

Lorenz Wernisch
(Birkbeck College, London)


Over the last few years new measurement techniques have been developed allowing to simultaneously collect data on the expression levels of virtually all the thousands of genes of an organism. These techniques have been used to obtain gene expression information for organisms during their cell cycles or to observe alterations in expression levels under varying conditions or in mutants. Such experiments result in large matrices of data that need to be analyzed using interesting mathematical and statistical methods. A first step involves grouping (coregulated) genes according to similarities in their expression profile. These classifications may soon play an indispensable role, for example, in the diagnosis of diseases like cancer. The search for common patterns in sequences surrounding coregulated genes or the analysis of metabolic networks connecting these genes is an important step towards an improved understanding of gene regulation. We will discuss established as well as some of the more recent methods applied in gene expression analysis. They come from a variety of areas such as statistics, combinatorial optimization, and graph theory.


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