Literature DB >> 30921678

Custom-made transcriptional biosensors for metabolic engineering.

Mathilde Koch1, Amir Pandi1, Olivier Borkowski2, A C Batista1, Jean-Loup Faulon3.   

Abstract

Transcriptional biosensors allow screening, selection, or dynamic regulation of metabolic pathways, and are, therefore, an enabling technology for faster prototyping of metabolic engineering and sustainable chemistry. Recent advances have been made, allowing for routine use of heterologous transcription factors, and new strategies such as chimeric protein design allow engineers to tap into the reservoir of metabolite-binding proteins. However, extending the sensing scope of biosensors is only the first step, and computational models can help in fine-tuning properties of biosensors for custom-made behavior. Moreover, metabolic engineering is bound to benefit from advances in cell-free expression systems, either for faster prototyping of biosensors or for whole-pathway optimization, making it both a means and an end in biosensor design.
Copyright © 2019 Elsevier Ltd. All rights reserved.

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Year:  2019        PMID: 30921678     DOI: 10.1016/j.copbio.2019.02.016

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


  12 in total

1.  Directed Evolution of Transcription Factor-Based Biosensors for Altered Effector Specificity.

Authors:  Leopoldo Ferreira Marques Machado; Neil Dixon
Journal:  Methods Mol Biol       Date:  2022

2.  Cell-Free Biosensors and AI Integration.

Authors:  Paul Soudier; Léon Faure; Manish Kushwaha; Jean-Loup Faulon
Journal:  Methods Mol Biol       Date:  2022

Review 3.  Biosensor-enabled pathway optimization in metabolic engineering.

Authors:  Yuxi Teng; Jianli Zhang; Tian Jiang; Yusong Zou; Xinyu Gong; Yajun Yan
Journal:  Curr Opin Biotechnol       Date:  2022-02-11       Impact factor: 10.279

4.  Design of a programmable biosensor-CRISPRi genetic circuits for dynamic and autonomous dual-control of metabolic flux in Bacillus subtilis.

Authors:  Yaokang Wu; Taichi Chen; Yanfeng Liu; Rongzhen Tian; Xueqin Lv; Jianghua Li; Guocheng Du; Jian Chen; Rodrigo Ledesma-Amaro; Long Liu
Journal:  Nucleic Acids Res       Date:  2020-01-24       Impact factor: 16.971

5.  Metabolic perceptrons for neural computing in biological systems.

Authors:  Amir Pandi; Mathilde Koch; Peter L Voyvodic; Paul Soudier; Jerome Bonnet; Manish Kushwaha; Jean-Loup Faulon
Journal:  Nat Commun       Date:  2019-08-28       Impact factor: 14.919

Review 6.  Genetically encoded biosensors for lignocellulose valorization.

Authors:  Guadalupe Alvarez-Gonzalez; Neil Dixon
Journal:  Biotechnol Biofuels       Date:  2019-10-15       Impact factor: 6.040

7.  Development of High-Performance Whole Cell Biosensors Aided by Statistical Modeling.

Authors:  Adokiye Berepiki; Ross Kent; Leopoldo F M Machado; Neil Dixon
Journal:  ACS Synth Biol       Date:  2020-02-17       Impact factor: 5.110

8.  Biosensor for branched-chain amino acid metabolism in yeast and applications in isobutanol and isopentanol production.

Authors:  Jeremy D Cortez; Sarah K Hammer; Yanfei Zhang; César Carrasco-López; Sergio Á García Echauri; Jessica B Wiggins; Wei Wang; José L Avalos
Journal:  Nat Commun       Date:  2022-01-12       Impact factor: 14.919

Review 9.  Effective use of biosensors for high-throughput library screening for metabolite production.

Authors:  Jennifer A Kaczmarek; Kristala L J Prather
Journal:  J Ind Microbiol Biotechnol       Date:  2021-12-23       Impact factor: 4.258

10.  Directed evolution of the PcaV allosteric transcription factor to generate a biosensor for aromatic aldehydes.

Authors:  Leopoldo F M Machado; Andrew Currin; Neil Dixon
Journal:  J Biol Eng       Date:  2019-11-27       Impact factor: 4.355

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