Please use this identifier to cite or link to this item: https://ktisis.cut.ac.cy/handle/10488/11844
Title: Optimal bioprocess design through a gene regulatory network – growth kinetic hybrid model: towards replacing Monod kinetics
Authors: Tsipa, Argyro 
Koutinas, Michalis 
Usaku, Chonlatep 
Mantalaris, Athanasios 
Keywords: P. putida;Bioprocess optimisation;Gene regulatory network;Mixed-substrate;Fed-batch;Monod Kinetics
Category: Biological Sciences
Field: Natural Sciences
Issue Date: Jul-2018
Publisher: Academic Press Inc.
Source: Metabolic Engineering, 2018, Volume 48, Pages 129-137
DOI: https://doi.org/10.1016/j.ymben.2018.04.023
Abstract: 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.
URI: http://ktisis.cut.ac.cy/handle/10488/11844
ISSN: 10967176
Rights: © Elsevier Inc.
Type: Article
Appears in Collections:Άρθρα/Articles

Show full item record

Page view(s) 50

45
Last Week
1
Last month
4
checked on Jul 17, 2019

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.