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Discrimination of dynamical system models for biological and chemical processes
Author(s) -
Lorenz Sönke,
Diederichs Elmar,
Telgmann Regina,
Schütte Christof
Publication year - 2007
Publication title -
journal of computational chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.907
H-Index - 188
eISSN - 1096-987X
pISSN - 0192-8651
DOI - 10.1002/jcc.20674
Subject(s) - computer science , process (computing) , selection (genetic algorithm) , development (topology) , statistical model , machine learning , model selection , biochemical engineering , management science , artificial intelligence , industrial engineering , mathematics , engineering , mathematical analysis , operating system
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. © 2007 Wiley Periodicals, Inc. J Comput Chem, 2007