Workflows for optimization of enzyme cascades and whole cell catalysis based on enzyme kinetic characterization and pathway modelling

Laura Kuschmierz, Lu Shen, Christopher Bräsen, Jacky Snoep, Bettina Siebers

Research output: Contribution to JournalReview articleAcademicpeer-review

Abstract

To move towards a circular bioeconomy, sustainable strategies for the utilization of renewable, non-food biomass wastes such as lignocellulose, are needed. To this end, an efficient bioconversion of D-xylose – after D-glucose the most abundant sugar in lignocellulose – is highly desirable. Most standard organisms used in biotechnology are limited in metabolising D-xylose, and also in vitro enzymatic strategies for its conversion have not been very successful. We herein discuss that bioconversion of D-xylose is mostly hampered by missing knowledge on the kinetic properties of the enzymes involved in its metabolism. We propose a combination of classical enzyme characterizations and mathematical modelling approaches as a workflow for rational, model-based design to optimize enzyme cascades and/or whole cell biocatalysts for efficient D-xylose metabolism.

Original languageEnglish
Pages (from-to)55-60
Number of pages6
JournalCurrent Opinion in Biotechnology
Volume74
Early online date15 Nov 2021
DOIs
Publication statusPublished - Apr 2022

Bibliographical note

Funding Information:
L.S. C.B. and B.S. acknowledge funding by MERCURPr-2013-0010. L.S. C.B. J.S. and B.S. acknowledge funding by the Federal Ministry of Education and Research (BMBF) grant HotSysAPP, 031L0078A within the eBio (2) funding initiative. J.S. acknowledges funding from the DST/NRF, particularly for funding the SARCHI initiative (NRF-SARCHI-82813).

Funding Information:
L.S., C.B. and B.S. acknowledge funding by MERCUR Pr-2013-0010 . L.S., C.B., J.S. and B.S. acknowledge funding by the Federal Ministry of Education and Research (BMBF) grant HotSysAPP , 031L0078A within the eBio (2) funding initiative. J.S. acknowledges funding from the DST / NRF , particularly for funding the SARCHI initiative ( NRF-SARCHI-82813 ).

Publisher Copyright:
© 2021 Elsevier Ltd

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