Please use this identifier to cite or link to this item: https://ktisis.cut.ac.cy/handle/10488/14864
Title: Improving the prediction of Pseudomonas putida mt-2 growth kinetics with the use of a gene expression regulation model of the TOL plasmid
Authors: Koutinas, Michalis 
Kiparissides, Alexandros 
Lam, Ming Chi 
Silva-Rocha, Rafael 
Godinho, Miguel 
de Lorenzo, Victor 
Martins dos Santos, Vitor A.P. 
Pistikopoulos, Efstratios N. 
Mantalaris, Athanasios A. 
Keywords: Dynamic modelling;pWW0 (TOL) plasmid;Genetic circuit;Pseudomonas putida;m-Xylene
Category: Biological Sciences
Field: Natural Sciences
Issue Date: 15-Jul-2011
Source: Biochemical Engineering Journal, 2011, Volume 55, Issue 2, Pages 108-118
Journal: Biochemical Engineering Journal 
Abstract: The molecular and genetic events responsible for the growth kinetics of a microorganism can be extensively influenced by the presence of mixtures of substrates leading to unusual growth patterns, which cannot be accurately predicted by mathematical models developed using analogies to enzyme kinetics. Towards this end, we have combined a dynamic mathematical model of the Ps/. Pr promoters of the TOL (pWW0) plasmid of Pseudomonas putida mt-2, involved in the metabolism of m-xylene, with the growth kinetics of the microorganism to predict the biodegradation of m-xylene and succinate in batch cultures. The substrate interactions observed in mixed-substrate experiments could not be accurately described by models without directly specifying the type of interaction even when accounting for enzymatic interactions. The structure of the genetic circuit-growth kinetic model was validated with batch cultures of mt-2 fed with m-xylene and succinate and its predictive capability was confirmed by successfully predicting independent sets of experimental data. Our combined genetic circuit-growth kinetic modelling approach exemplifies the critical importance of the molecular interactions of key genetic circuits in predicting unusual growth patterns. Such strategy is more suitable in describing bioprocess performance, which current models fail to predict.
URI: https://ktisis.cut.ac.cy/handle/10488/14864
ISSN: 1369-703X
DOI: 10.1016/j.bej.2011.03.012
Rights: © 2011 Elsevier B.V. All rights reserved.
Type: Article
Appears in Collections:Άρθρα/Articles

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