Document Detail


Modeling and simulation of Streptomyces peucetius var. caesius N47 cultivation and epsilon-rhodomycinone production with kinetic equations and neural networks.
MedLine Citation:
PMID:  16797766     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
This study focuses on comparing different kinetic growth models and the use of neural networks in the batch cultivation of Streptomyces peucetius var. caesius producing epsilon-rhodomycinone. Contois, Monod and Teissier microbial growth models were used as well as the logistic growth modeling approach, which was found best in the simulations of growth and glucose consumption in the batch growth phase. The lag phase was included in the kinetic model with a CO2 trigger and a delay factor. Substrate consumption and product formation were included as Luedeking-Piret and logistic type equations, respectively. Biomass formation was modeled successfully with a 6-8-2 network, and the network was capable of biomass prediction with an R2-value of 0.983. Epsilon-rhodomycinone production was successfully modeled with a recursive 8-3-1 network capable of epsilon-rhodomycinone prediction with an R2-value of 0.903. The predictive power of the neural networks was superior to the kinetic models, which could not be used in predictive modeling of arbitrary batch cultivations.
Authors:
Kristiina Kiviharju; Kalle Salonen; Matti Leisola; Tero Eerikäinen
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Publication Detail:
Type:  Journal Article; Research Support, Non-U.S. Gov't     Date:  2006-05-06
Journal Detail:
Title:  Journal of biotechnology     Volume:  126     ISSN:  0168-1656     ISO Abbreviation:  J. Biotechnol.     Publication Date:  2006 Nov 
Date Detail:
Created Date:  2006-10-18     Completed Date:  2006-12-21     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  8411927     Medline TA:  J Biotechnol     Country:  Netherlands    
Other Details:
Languages:  eng     Pagination:  365-73     Citation Subset:  IM    
Affiliation:
Helsinki University of Technology, Laboratory of Bioprocess Engineering, P.O. Box 6100, FIN-02015 TKK, Finland. kristiina.kiviharju@tkk.fi
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MeSH Terms
Descriptor/Qualifier:
Anthracyclines / metabolism
Bioreactors / microbiology*
Cell Culture Techniques / methods*
Cell Proliferation
Computer Simulation
Kinetics
Models, Biological*
Neural Networks (Computer)
Streptomyces / classification,  physiology*
Chemical
Reg. No./Substance:
0/Anthracyclines; 21288-60-8/rhodomycinone

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


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