Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 409345
Title Improving the prediction of Pseudomonas putida mt-2 growth kinetics with the use of a gene expression regulation model of the TOL plasmid
Author(s) Koutinas, M.; Kiparissides, A.; Lam, M.C.; Silva-Rocha, R.; Lorenzo, V. de; Godinho, M.; Martins Dos Santos, V.A.P.; Pistikopoulos, E.N.; Mantalaris, A.
Source Biochemical Engineering Journal 55 (2011)2. - ISSN 1369-703X - p. 108 - 118.
Department(s) Systems and Synthetic Biology
Publication type Refereed Article in a scientific journal
Publication year 2011
Keyword(s) multisubstrate biodegradation kinetics - m-xylene - substrate-inhibition - toluene - phenol - degradation - mixtures - single - system - 4-chlorophenol
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
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