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Τίτλος: Optimal bioprocess design through a gene regulatory network – growth kinetic hybrid model: towards replacing Monod kinetics
Συγγραφείς: Tsipa, Argyro 
Koutinas, Michalis 
Usaku, Chonlatep 
Mantalaris, Athanasios A. 
Major Field of Science: Natural Sciences
Field Category: Biological Sciences
Λέξεις-κλειδιά: Bioprocess optimisation;Gene regulatory network;Mixed-substrate;Fed-batch;Monod Kinetics
Ημερομηνία Έκδοσης: Ιου-2018
Πηγή: Metabolic Engineering, 2018, vol.48, pp. 129-137
Volume: 48
Start page: 129
End page: 137
Περιοδικό: Metabolic engineering 
Περίληψη: Currently, design and optimisation of biotechnological bioprocesses is performed either through exhaustive experimentation and/or with the use of empirical, unstructured growth kinetics models. Whereas, elaborate systems biology approaches have been recently explored, mixed-substrate utilisation is predominantly ignored despite its significance in enhancing bioprocess performance. Herein, bioprocess optimisation for an industrially-relevant bioremediation process involving a mixture of highly toxic substrates, m-xylene and toluene, was achieved through application of a novel experimental-modelling gene regulatory network – growth kinetic (GRN-GK) hybrid framework. The GRN model described the TOL and ortho-cleavage pathways in Pseudomonas putida mt-2 and captured the transcriptional kinetics expression patterns of the promoters. The GRN model informed the formulation of the growth kinetics model replacing the empirical and unstructured Monod kinetics. The GRN-GK framework's predictive capability and potential as a systematic optimal bioprocess design tool, was demonstrated by effectively predicting bioprocess performance, which was in agreement with experimental values, when compared to four commonly used models that deviated significantly from the experimental values. Significantly, a fed-batch biodegradation process was designed and optimised through the model-based control of TOL Pr promoter expression resulting in 61% and 60% enhanced pollutant removal and biomass formation, respectively, compared to the batch process. This provides strong evidence of model-based bioprocess optimisation at the gene level, rendering the GRN-GK framework as a novel and applicable approach to optimal bioprocess design. Finally, model analysis using global sensitivity analysis (GSA) suggests an alternative, systematic approach for model-driven strain modification for synthetic biology and metabolic engineering applications.
ISSN: 10967176
DOI: 10.1016/j.ymben.2018.04.023
Rights: © Elsevier Inc.
Type: Article
Affiliation: Imperial College London 
Cyprus University of Technology 
Silpakorn University 
Publication Type: Peer Reviewed
Εμφανίζεται στις συλλογές:Άρθρα/Articles

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