Literature DB >> 29448095

Advances in analytical tools for high throughput strain engineering.

Esteban Marcellin1, Lars Keld Nielsen2.   

Abstract

The emergence of inexpensive, base-perfect genome editing is revolutionising biology. Modern industrial biotechnology exploits the advances in genome editing in combination with automation, analytics and data integration to build high-throughput automated strain engineering pipelines also known as biofoundries. Biofoundries replace the slow and inconsistent artisanal processes used to build microbial cell factories with an automated design-build-test cycle, considerably reducing the time needed to deliver commercially viable strains. Testing and hence learning remains relatively shallow, but recent advances in analytical chemistry promise to increase the depth of characterization possible. Analytics combined with models of cellular physiology in automated systems biology pipelines should enable deeper learning and hence a steeper pitch of the learning cycle. This review explores the progress, advances and remaining bottlenecks of analytical tools for high throughput strain engineering.
Copyright © 2018 Elsevier Ltd. All rights reserved.

Mesh:

Year:  2018        PMID: 29448095     DOI: 10.1016/j.copbio.2018.01.027

Source DB:  PubMed          Journal:  Curr Opin Biotechnol        ISSN: 0958-1669            Impact factor:   9.740


  7 in total

1.  Usage of Digital Twins Along a Typical Process Development Cycle.

Authors:  Peter Sinner; Sven Daume; Christoph Herwig; Julian Kager
Journal:  Adv Biochem Eng Biotechnol       Date:  2021       Impact factor: 2.635

Review 2.  Blueprints for Biosensors: Design, Limitations, and Applications.

Authors:  Alexander C Carpenter; Ian T Paulsen; Thomas C Williams
Journal:  Genes (Basel)       Date:  2018-07-26       Impact factor: 4.096

3.  Linking genotype and phenotype in an economically viable propionic acid biosynthesis process.

Authors:  Carlos H Luna-Flores; Chris C Stowers; Brad M Cox; Lars K Nielsen; Esteban Marcellin
Journal:  Biotechnol Biofuels       Date:  2018-08-13       Impact factor: 6.040

Review 4.  Modeling population heterogeneity from microbial communities to immune response in cells.

Authors:  Tal Pecht; Anna C Aschenbrenner; Thomas Ulas; Antonella Succurro
Journal:  Cell Mol Life Sci       Date:  2019-11-25       Impact factor: 9.261

5.  Bayesian inference of metabolic kinetics from genome-scale multiomics data.

Authors:  Peter C St John; Jonathan Strutz; Linda J Broadbelt; Keith E J Tyo; Yannick J Bomble
Journal:  PLoS Comput Biol       Date:  2019-11-04       Impact factor: 4.475

6.  Need for speed: evaluation of dilute and shoot-mass spectrometry for accelerated metabolic phenotyping in bioprocess development.

Authors:  Alexander Reiter; Laura Herbst; Wolfgang Wiechert; Marco Oldiges
Journal:  Anal Bioanal Chem       Date:  2021-03-31       Impact factor: 4.142

7.  From Escherichia coli mutant 13C labeling data to a core kinetic model: A kinetic model parameterization pipeline.

Authors:  Charles J Foster; Saratram Gopalakrishnan; Maciek R Antoniewicz; Costas D Maranas
Journal:  PLoS Comput Biol       Date:  2019-09-10       Impact factor: 4.475

  7 in total

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