Literature DB >> 21315172

Linking genes to microbial growth kinetics: an integrated biochemical systems engineering approach.

Michalis Koutinas1, Alexandros Kiparissides, Rafael Silva-Rocha, Ming-Chi Lam, Vitor A P Martins Dos Santos, Victor de Lorenzo, Efstratios N Pistikopoulos, Athanasios Mantalaris.   

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.
Copyright © 2011 Elsevier Inc. All rights reserved.

Entities:  

Mesh:

Substances:

Year:  2011        PMID: 21315172     DOI: 10.1016/j.ymben.2011.02.001

Source DB:  PubMed          Journal:  Metab Eng        ISSN: 1096-7176            Impact factor:   9.783


  4 in total

Review 1.  Bioprocess systems engineering: transferring traditional process engineering principles to industrial biotechnology.

Authors:  Michalis Koutinas; Alexandros Kiparissides; Efstratios N Pistikopoulos; Athanasios Mantalaris
Journal:  Comput Struct Biotechnol J       Date:  2013-03-10       Impact factor: 7.271

2.  Does It Pay Off to Explicitly Link Functional Gene Expression to Denitrification Rates in Reaction Models?

Authors:  Anna Störiko; Holger Pagel; Adrian Mellage; Olaf A Cirpka
Journal:  Front Microbiol       Date:  2021-06-18       Impact factor: 5.640

3.  Modeling of scale-dependent bacterial growth by chemical kinetics approach.

Authors:  Haydee Martínez; Joaquín Sánchez; José-Manuel Cruz; Guadalupe Ayala; Marco Rivera; Thomas Buhse
Journal:  ScientificWorldJournal       Date:  2014-07-03

4.  DRUM: a new framework for metabolic modeling under non-balanced growth. Application to the carbon metabolism of unicellular microalgae.

Authors:  Caroline Baroukh; Rafael Muñoz-Tamayo; Jean-Philippe Steyer; Olivier Bernard
Journal:  PLoS One       Date:  2014-08-08       Impact factor: 3.240

  4 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.