Document Detail


Metabolomics, machine learning and modelling: towards an understanding of the language of cells.
MedLine Citation:
PMID:  15916555     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
In answering the question 'Systems Biology--will it work?' (which it self-evidently has already), it is appropriate to highlight advances in philosophy, in new technique development and in novel findings. In terms of philosophy, we see that systems biology involves an iterative interplay between linked activities--instance, between theory and experiment, between induction and deduction and between measurements of parameters and variables--with more emphasis than has perhaps been common now being focused on the first in each of these pairs. In technique development, we highlight closed loop machine learning and its use in the optimization of scientific instrumentation, and the ability to effect high-quality and quasi-continuous optical images of cells. This leads to many important and novel findings. In the first case, these may involve new biomarkers for disease, whereas in the second case, we have determined that many biological signals may be frequency-rather than amplitude-encoded. This leads to a very different view of how signalling 'works' (equations such as that of Michaelis and Menten which use only amplitudes, i.e. concentrations, are inadequate descriptors), lays emphasis on the signal processing network elements that lie 'downstream' of what are traditionally considered the signals, and allows one simply to understand how cross-talk may be avoided between pathways which nevertheless use common signalling elements. The language of cells is much richer than we had supposed, and we are now well placed to decode it.
Authors:
D B Kell
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Publication Detail:
Type:  Journal Article; Research Support, Non-U.S. Gov't    
Journal Detail:
Title:  Biochemical Society transactions     Volume:  33     ISSN:  0300-5127     ISO Abbreviation:  Biochem. Soc. Trans.     Publication Date:  2005 Jun 
Date Detail:
Created Date:  2005-05-26     Completed Date:  2005-08-16     Revised Date:  2008-11-21    
Medline Journal Info:
Nlm Unique ID:  7506897     Medline TA:  Biochem Soc Trans     Country:  England    
Other Details:
Languages:  eng     Pagination:  520-4     Citation Subset:  IM    
Affiliation:
School of Chemistry, The University of Manchester, Faraday Building, Sackville Street, P.O. Box 88, Manchester M60 1QD, UK. dbk@manchester.ac.uk
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MeSH Terms
Descriptor/Qualifier:
Cell Physiological Phenomena*
Computer Simulation*
Genomics
Metabolism*
Models, Biological*
Signal Transduction
Systems Biology*

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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