Literature DB >> 25457387

Metabolic variability in bioprocessing: implications of microbial phenotypic heterogeneity.

Frank Delvigne1, Quentin Zune2, Alvaro R Lara3, Waleed Al-Soud4, Søren J Sørensen4.   

Abstract

Phenotypic heterogeneity is a major issue in the context of industrial bioprocessing. Stochasticity of gene expression is usually considered to be the main source of heterogeneity among microbial population, but recent evidence demonstrates that metabolic reactions can also be subject to stochasticity without any intervention of gene expression. Although metabolic heterogeneity can be encountered in laboratory-scale cultivation devices, stochasticity at the level of metabolic reactions is perturbed directly by microenvironmental heterogeneities occurring in large-scale bioreactors. Accordingly, analytical tools are needed for the determination of metabolic variability in bioprocessing conditions and for the efficient design of metabolic engineering strategies. In this context, implementation of single cell technologies for bioprocess monitoring would benefit from knowledge acquired in more fundamental studies.
Copyright © 2014 Elsevier Ltd. All rights reserved.

Keywords:  bioprocess optimization; metabolic engineering; microbial stress; stochasticity

Mesh:

Year:  2014        PMID: 25457387     DOI: 10.1016/j.tibtech.2014.10.002

Source DB:  PubMed          Journal:  Trends Biotechnol        ISSN: 0167-7799            Impact factor:   19.536


  31 in total

1.  Potential of Integrating Model-Based Design of Experiments Approaches and Process Analytical Technologies for Bioprocess Scale-Down.

Authors:  Peter Neubauer; Emmanuel Anane; Stefan Junne; Mariano Nicolas Cruz Bournazou
Journal:  Adv Biochem Eng Biotechnol       Date:  2021       Impact factor: 2.635

Review 2.  Estimation methods for heterogeneous cell population models in systems biology.

Authors:  Steffen Waldherr
Journal:  J R Soc Interface       Date:  2018-10-31       Impact factor: 4.118

3.  Dynamic metabolic control: towards precision engineering of metabolism.

Authors:  Di Liu; Ahmad A Mannan; Yichao Han; Diego A Oyarzún; Fuzhong Zhang
Journal:  J Ind Microbiol Biotechnol       Date:  2018-01-29       Impact factor: 3.346

4.  Accelerating bacterial growth detection and antimicrobial susceptibility assessment in integrated picoliter droplet platform.

Authors:  Aniruddha M Kaushik; Kuangwen Hsieh; Liben Chen; Dong Jin Shin; Joseph C Liao; Tza-Huei Wang
Journal:  Biosens Bioelectron       Date:  2017-11-15       Impact factor: 10.618

5.  Exploiting nongenetic cell-to-cell variation for enhanced biosynthesis.

Authors:  Yi Xiao; Christopher H Bowen; Di Liu; Fuzhong Zhang
Journal:  Nat Chem Biol       Date:  2016-03-21       Impact factor: 15.040

6.  Development of a ribosome profiling protocol to study translation in Kluyveromyces marxianus.

Authors:  Darren A Fenton; Stephen J Kiniry; Martina M Yordanova; Pavel V Baranov; John P Morrissey
Journal:  FEMS Yeast Res       Date:  2022-06-30       Impact factor: 2.923

Review 7.  Microbial metabolic noise.

Authors:  Andreas E Vasdekis; Abhyudai Singh
Journal:  WIREs Mech Dis       Date:  2020-11-23

Review 8.  Fluorescent Reporter Libraries as Useful Tools for Optimizing Microbial Cell Factories: A Review of the Current Methods and Applications.

Authors:  Frank Delvigne; Hélène Pêcheux; Cédric Tarayre
Journal:  Front Bioeng Biotechnol       Date:  2015-09-28

Review 9.  Transcription factor-based biosensors in biotechnology: current state and future prospects.

Authors:  Regina Mahr; Julia Frunzke
Journal:  Appl Microbiol Biotechnol       Date:  2015-10-31       Impact factor: 4.813

10.  Adaptation to sustained nitrogen starvation by Escherichia coli requires the eukaryote-like serine/threonine kinase YeaG.

Authors:  Rita Figueira; Daniel R Brown; Delfim Ferreira; Matthew J G Eldridge; Lynn Burchell; Zhensheng Pan; Sophie Helaine; Sivaramesh Wigneshweraraj
Journal:  Sci Rep       Date:  2015-12-01       Impact factor: 4.379

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.