 |
| Research article summary (published 30 Dec 2001): |
Progress in bioinformatics and the importance of being earnest.
Full Abstract
In silico biology has gathered momentum as, worldwide, scientists have united in a common quest to sequence, store and analyse complete genomes. This year, a pivotal achievement of this cooperative endeavour was realised in the release of a public draft of the human genome, and with it the promises to improve our understanding of diverse aspects of biology and to yield a healthier future with safe personalized medicines. Key to these goals will be the need to elucidate and characterise the genes and gene products encoded not just in the human genome, but in many genomes. These tasks are underpinned by the concepts and processes of genome and gene/protein evolution, regulation of gene expression, mechanisms of protein folding, the manifestation of protein function, and so on, all of which must be understood in the context of complex, dynamic biological systems. Our use of computers to model such concepts and systems must be placed in the context of the current limits of our understanding of them:- it is important to recognise, for example, that we don't have a common understanding either of what constitutes a gene or a protein function; we can't invariably say that a particular sequence or fold has arisen via divergent or convergent evolution; and we don't fully understand the rules of protein folding. Accepting what we can't do in silico is essential in appreciating what we can do. Without this understanding, it is easy to be misled, as notions of what particular computational approaches can achieve are sometimes rather optimistic. There are valuable lessons to be learned here from the field of Artificial Intelligence, principal among which is the realisation that capturing and representing complex knowledge is time consuming, expensive and hard. Thus, we argue here that if bioinformatics is to tackle biological complexity in earnest, it would be wise to absorb the experience distilled from decades of artificial intelligence research, and to approach the road ahead with caution, rigour and pragmatism.
Learn Faster Today Improve your study skills
Author information
Author/s: Attwood, T K (TK); Miller, C J (CJ);
Affiliation: School of Biological Sciences, Department of Computer Science, University of Manchester, Oxford Road, Manchester M13 9PT, UK. attwood(-atsign-)bioinf.man.ac.uk
Journal and publication information
Publication Type: Journal Article; Research Support, Non-U.S. Gov't; Review
Journal: Biotechnology annual review (Biotechnol Annu Rev), published in Netherlands. (Language: eng)
Reference: 2002-; vol 8 (issue ) : pp 1-54
Dates: Created 2002/11/19; Completed 2003/04/30; Revised 2006/11/15;
PMID: 12436914, status: MEDLINE (last retrieval date: 11/6/2008)
Sourced from the National Library of Medicine. Abstract text and other information may be subject to copyright.
External Links for this article (including full text providers, if available):
Click Electronic Full-text Provider Links to see options for finding the electronic full text links to this article. Note there may be a subscription or fee required for access to the full text. See our FAQ for information on finding FREE full text articles.
This article may also be located in paper journal collections available in many libraries. Use the Journal and Publication Information above to find the full article.
MeSH headings (categories)
This article was linked to the MESH Headings shown below.
|
|
Related articles
This article has not been indexed for related articles as yet, however you can still use the live related article search links below.
See a large map of 100+ related articles.