Difference between revisions of "Lecture 01"

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<small>[[Lecture_02|(Next lecture)]]</small>
 
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Revision as of 15:24, 1 September 2007

Update Warning! This page has not been revised yet for the 2007 Fall term. Some of the slides may be reused, but please consider the page as a whole out of date as long as this warning appears here.

 

 


(Next lecture)


Organisation and Orientation

Add/expand:

  • Mission: Analysis must be followed by interpretation, course is hands-on, interactive and goal oriented.
  • Technology: Google group, Wiki, supporting material on the Web
  • Contact information
  • Technical details of course organisation

Lecture Slides

Slide 001
Lecture 01, Slide 001
Bioinformatics is not only required to master the quantitative aspects of the post-genomic era in molecular biology, it is a qualitative change in our approach to biology as well.
Slide 002
Lecture 01, Slide 002
Bioinformatics can be viewed as the science that develops between the two poles of data management and computational modeling of life.
Slide 003
Lecture 01, Slide 003
From its beginning, it was recognized that molecular biology is an information science, just as much as a molecular science. The abstractions and models that focus on the essence of this information, rather than on the details of its representation, have proven to be remarkably powerful in explaining the basic features of life, such as inheritance, self-organization and the process of evolution.
Slide 004
Lecture 01, Slide 004
The promises of genome analysis include harnessing the power of self assembly towards a bio-nanotechnologic revolution: growth, rather than manufacturing. This includes the vision of regenerative molecular medicine, essentially relegating disease to the dark past ages of ignorance. But while the information for a complete specification of life is undoubtedly present in the genome, life realizes itself in complex interactions between context-dependent components. This makes life essentially unpredictable, at least to our current approaches. The sheer volume of data is a comparatively minor obstacle.
Slide 005
Lecture 01, Slide 005
Slide 006
Lecture 01, Slide 006
Slide 007
Lecture 01, Slide 007 [1]
Slide 008
Lecture 01, Slide 008
Slide 009
Lecture 01, Slide 009
Slide 010
Lecture 01, Slide 010
Slide 011
Lecture 01, Slide 011
Slide 012
Lecture 01, Slide 012
Slide 013
Lecture 01, Slide 013
Slide 014
Lecture 01, Slide 014
Slide 015
Lecture 01, Slide 015
Slide 016
Lecture 01, Slide 016
Slide 017
Lecture 01, Slide 017
Slide 018
Lecture 01, Slide 018
Slide 019
Lecture 01, Slide 019
Slide 020
Lecture 01, Slide 020
Slide 021
Lecture 01, Slide 021
Slide 022
Lecture 01, Slide 022
Slide 023
Lecture 01, Slide 023
Slide 024
Lecture 01, Slide 024
Slide 025
Lecture 01, Slide 025
Slide 026
Lecture 01, Slide 026
Slide 027
Lecture 01, Slide 027
Slide 028
Lecture 01, Slide 028
Slide 029
Lecture 01, Slide 029