In-depth analysis of biocatalysts by microfluidics: An emerging source of data for machine learning

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00159816%3A_____%2F23%3A00079599" target="_blank" >RIV/00159816:_____/23:00079599 - isvavai.cz</a>

Alternative codes found

RIV/00216224:14310/23:00131491

Result on the web

<a href="https://www.sciencedirect.com/science/article/abs/pii/S0734975023000782?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/abs/pii/S0734975023000782?via%3Dihub</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.biotechadv.2023.108171" target="_blank" >10.1016/j.biotechadv.2023.108171</a>

Result language

angličtina

Original language name

In-depth analysis of biocatalysts by microfluidics: An emerging source of data for machine learning

Original language description

Nowadays, the vastly increasing demand for novel biotechnological products is supported by the continuous development of biocatalytic applications that provide sustainable green alternatives to chemical processes. The success of a biocatalytic application is critically dependent on how quickly we can identify and characterize enzyme variants fitting the conditions of industrial processes. While miniaturization and parallelization have dramatically increased the throughput of next-generation sequencing systems, the subsequent characterization of the obtained candidates is still a limiting process in identifying the desired biocatalysts. Only a few commercial microfluidic systems for enzyme analysis are currently available, and the transformation of numerous published prototypes into commercial platforms is still to be streamlined. This review presents the state-of-the-art, recent trends, and perspectives in applying microfluidic tools in the functional and structural analysis of biocatalysts. We discuss the advantages and disadvantages of available technologies, their reproducibility and robustness, and readiness for routine laboratory use. We also highlight the unexplored potential of microfluidics to leverage the power of machine learning for biocatalyst development.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

20801 - Environmental biotechnology

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Biotechnology Advances

ISSN

0734-9750

e-ISSN

1873-1899

Volume of the periodical

66

Issue of the periodical within the volume

SEP 2023

Country of publishing house

GB - UNITED KINGDOM

Number of pages

22

Pages from-to

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UT code for WoS article

001009341400001

EID of the result in the Scopus database

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