Document Detail


Discrimination of dynamical system models for biological and chemical processes.
MedLine Citation:
PMID:  17330886     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
In technical chemistry, systems biology and biotechnology, the construction of predictive models has become an essential step in process design and product optimization. Accurate modelling of the reactions requires detailed knowledge about the processes involved. However, when concerned with the development of new products and production techniques for example, this knowledge often is not available due to the lack of experimental data. Thus, when one has to work with a selection of proposed models, the main tasks of early development is to discriminate these models. In this article, a new statistical approach to model discrimination is described that ranks models wrt. the probability with which they reproduce the given data. The article introduces the new approach, discusses its statistical background, presents numerical techniques for its implementation and illustrates the application to examples from biokinetics.
Authors:
Sönke Lorenz; Elmar Diederichs; Regina Telgmann; Christof Schütte
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Publication Detail:
Type:  Journal Article; Research Support, Non-U.S. Gov't    
Journal Detail:
Title:  Journal of computational chemistry     Volume:  28     ISSN:  0192-8651     ISO Abbreviation:  J Comput Chem     Publication Date:  2007 Jun 
Date Detail:
Created Date:  2007-04-17     Completed Date:  2007-06-01     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  9878362     Medline TA:  J Comput Chem     Country:  United States    
Other Details:
Languages:  eng     Pagination:  1384-99     Citation Subset:  IM    
Affiliation:
Institute for Mathematics II, Free University of Berlin Arnimallee 2-6, 14195 Berlin, Germany.
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MeSH Terms
Descriptor/Qualifier:
Algorithms
Kinetics
Models, Biological*
Models, Chemical*
Nonlinear Dynamics
Reproducibility of Results

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


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