Literature DB >> 29131522

When Do Two-Stage Processes Outperform One-Stage Processes?

Steffen Klamt1, Radhakrishnan Mahadevan2, Oliver Hädicke1.   

Abstract

Apart from product yield and titer, volumetric productivity is a key performance indicator for many biotechnological processes. Due to the inherent trade-off between the production of biomass as catalyst and of the actual target product, yield and volumetric productivity cannot be optimized simultaneously. Therefore, in combination with genetic techniques for dynamic regulation of metabolic fluxes, two-stage fermentations (TSFs) with separated growth and production phase have recently gained much interest because of their potential to improve the productivity of bioprocesses while still allowing high product yields. However, despite some successful case studies, so far it has not been discussed and analyzed systematically whether or under which conditions a TSF guarantees superior productivity compared to one-stage fermentation (OSF). In this study, we use mathematical models to demonstrate that the volumetric productivity of a TSF is not automatically better than of a corresponding OSF. Our analysis reveals that the sharp decrease of the specific substrate uptake rate usually observed in (non-growth) production phases severely impacts the volumetric productivity and thus raises a big challenge for designing competitive TSF processes. We discuss possible approaches such as enforced ATP wasting to improve substrate utilization rates in the production phase by which TSF processes can become superior to OSF. We also analyze additional factors influencing the relative performance of OSF and TSF and show that OSF processes can be more appropriate if a high product yield is an economic constraint. In conclusion, a careful assessment of the trade-offs between substrate uptake rates, yields, and productivity is necessary when deciding for OSF vs. TSF processes.
© 2017 The Authors. Biotechnology Journal Published by Wiley-VCH Verlag GmbH & Co. KGaA.

Entities:  

Keywords:  Escherichia coli; dynamic metabolic control; dynamic process strategies; metabolic engineering; two-stage fermentation

Mesh:

Substances:

Year:  2017        PMID: 29131522     DOI: 10.1002/biot.201700539

Source DB:  PubMed          Journal:  Biotechnol J        ISSN: 1860-6768            Impact factor:   4.677


  13 in total

1.  Overcoming glutamate auxotrophy in Escherichia coli itaconate overproducer by the Weimberg pathway.

Authors:  Ken W Lu; Chris T Wang; Hengray Chang; Ryan S Wang; Claire R Shen
Journal:  Metab Eng Commun       Date:  2021-12-02

Review 2.  Metabolic Engineering Strategies for Improved Lipid Production and Cellular Physiological Responses in Yeast Saccharomyces cerevisiae.

Authors:  Wei Jiang; Chao Li; Yanjun Li; Huadong Peng
Journal:  J Fungi (Basel)       Date:  2022-04-21

3.  Growth-uncoupled isoprenoid synthesis in Rhodobacter sphaeroides.

Authors:  Enrico Orsi; Ioannis Mougiakos; Wilbert Post; Jules Beekwilder; Marco Dompè; Gerrit Eggink; John van der Oost; Servé W M Kengen; Ruud A Weusthuis
Journal:  Biotechnol Biofuels       Date:  2020-07-13       Impact factor: 6.040

4.  Designing an irreversible metabolic switch for scalable induction of microbial chemical production.

Authors:  Ahmad A Mannan; Declan G Bates
Journal:  Nat Commun       Date:  2021-06-08       Impact factor: 14.919

5.  An extended and generalized framework for the calculation of metabolic intervention strategies based on minimal cut sets.

Authors:  Philipp Schneider; Axel von Kamp; Steffen Klamt
Journal:  PLoS Comput Biol       Date:  2020-07-27       Impact factor: 4.475

6.  CO2 to succinic acid - Estimating the potential of biocatalytic routes.

Authors:  Ulf W Liebal; Lars M Blank; Birgitta E Ebert
Journal:  Metab Eng Commun       Date:  2018-06-28

7.  MoVE identifies metabolic valves to switch between phenotypic states.

Authors:  Naveen Venayak; Axel von Kamp; Steffen Klamt; Radhakrishnan Mahadevan
Journal:  Nat Commun       Date:  2018-12-14       Impact factor: 14.919

8.  High-throughput enrichment of temperature-sensitive argininosuccinate synthetase for two-stage citrulline production in E. coli.

Authors:  Thorben Schramm; Martin Lempp; Dominik Beuter; Silvia González Sierra; Timo Glatter; Hannes Link
Journal:  Metab Eng       Date:  2020-03-13       Impact factor: 9.783

9.  Characterizing and ranking computed metabolic engineering strategies.

Authors:  Philipp Schneider; Steffen Klamt
Journal:  Bioinformatics       Date:  2019-09-01       Impact factor: 6.937

Review 10.  Dynamic control in metabolic engineering: Theories, tools, and applications.

Authors:  Christopher J Hartline; Alexander C Schmitz; Yichao Han; Fuzhong Zhang
Journal:  Metab Eng       Date:  2020-09-11       Impact factor: 9.783

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.