Please use this identifier to cite or link to this item: https://ktisis.cut.ac.cy/handle/10488/14869
Title: Linking genes to microbial growth kinetics: an integrated biochemical systems engineering approach
Authors: Koutinas, Michalis 
Kiparissides, Alexandros 
Silva-Rocha, Rafael 
Lam, Ming Chi 
Martins dos Santos, Vitor A.P. 
de Lorenzo, Victor 
Pistikopoulos, Efstratios N. 
Mantalaris, Athanasios 
Keywords: Metabolic engineering;Genetic circuit;pWW0 (TOL) plasmid;Pseudomonas putida;m-xylene;Dynamic model
Category: Biological Sciences
Field: Natural Sciences
Issue Date: Jul-2011
Journal: Metabolic engineering 
Abstract: The majority of models describing the kinetic properties of a microorganism for a given substrate are unstructured and empirical. They are formulated in this manner so that the complex mechanism of cell growth is simplified. Herein, a novel approach for modelling microbial growth kinetics is proposed, linking biomass growth and substrate consumption rates to the gene regulatory programmes that control these processes. A dynamic model of the TOL (pWW0) plasmid of Pseudomonas putida mt-2 has been developed, describing the molecular interactions that lead to the transcription of the upper and meta operons, known to produce the enzymes for the oxidative catabolism of m-xylene. The genetic circuit model was combined with a growth kinetic model decoupling biomass growth and substrate consumption rates, which are expressed as independent functions of the rate-limiting enzymes produced by the operons. Estimation of model parameters and validation of the model's predictive capability were successfully performed in batch cultures of mt-2 fed with different concentrations of m-xylene, as confirmed by relative mRNA concentration measurements of the promoters encoded in TOL. The growth formation and substrate utilisation patterns could not be accurately described by traditional Monod-type models for a wide range of conditions, demonstrating the critical importance of gene regulation for the development of advanced models closely predicting complex bioprocesses. In contrast, the proposed strategy, which utilises quantitative information pertaining to upstream molecular events that control the production of rate-limiting enzymes, predicts the catabolism of a substrate and biomass formation and could be of central importance for the design of optimal bioprocesses.
URI: https://ktisis.cut.ac.cy/handle/10488/14869
ISSN: 1096-7176
DOI: 10.1016/j.ymben.2011.02.001
Rights: © 2011 Elsevier Inc. All rights reserved.
Type: Article
Appears in Collections:Άρθρα/Articles

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