Literature DB >> 27768949

Biofuel metabolic engineering with biosensors.

Stacy-Anne Morgan1, Dana C Nadler1, Rayka Yokoo1, David F Savage2.   

Abstract

Metabolic engineering offers the potential to renewably produce important classes of chemicals, particularly biofuels, at an industrial scale. DNA synthesis and editing techniques can generate large pathway libraries, yet identifying the best variants is slow and cumbersome. Traditionally, analytical methods like chromatography and mass spectrometry have been used to evaluate pathway variants, but such techniques cannot be performed with high throughput. Biosensors - genetically encoded components that actuate a cellular output in response to a change in metabolite concentration - are therefore a promising tool for rapid and high-throughput evaluation of candidate pathway variants. Applying biosensors can also dynamically tune pathways in response to metabolic changes, improving balance and productivity. Here, we describe the major classes of biosensors and briefly highlight recent progress in applying them to biofuel-related metabolic pathway engineering.
Copyright © 2016 Elsevier Ltd. All rights reserved.

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Year:  2016        PMID: 27768949      PMCID: PMC5161612          DOI: 10.1016/j.cbpa.2016.09.020

Source DB:  PubMed          Journal:  Curr Opin Chem Biol        ISSN: 1367-5931            Impact factor:   8.822


  71 in total

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Journal:  Science       Date:  2010-12-03       Impact factor: 47.728

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5.  Computational design of ligand-binding proteins with high affinity and selectivity.

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Journal:  Nature       Date:  2013-09-04       Impact factor: 49.962

6.  Hot spots for allosteric regulation on protein surfaces.

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Journal:  Cell       Date:  2011-12-23       Impact factor: 41.582

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Journal:  Antioxid Redox Signal       Date:  2016-03-18       Impact factor: 8.401

8.  Visualization of ATP levels inside single living cells with fluorescence resonance energy transfer-based genetically encoded indicators.

Authors:  Hiromi Imamura; Kim P Huynh Nhat; Hiroko Togawa; Kenta Saito; Ryota Iino; Yasuyuki Kato-Yamada; Takeharu Nagai; Hiroyuki Noji
Journal:  Proc Natl Acad Sci U S A       Date:  2009-08-31       Impact factor: 11.205

9.  Engineering an allosteric transcription factor to respond to new ligands.

Authors:  Noah D Taylor; Alexander S Garruss; Rocco Moretti; Sum Chan; Mark A Arbing; Duilio Cascio; Jameson K Rogers; Farren J Isaacs; Sriram Kosuri; David Baker; Stanley Fields; George M Church; Srivatsan Raman
Journal:  Nat Methods       Date:  2015-12-21       Impact factor: 28.547

10.  Rapid construction of metabolite biosensors using domain-insertion profiling.

Authors:  Dana C Nadler; Stacy-Anne Morgan; Avi Flamholz; Kaitlyn E Kortright; David F Savage
Journal:  Nat Commun       Date:  2016-07-29       Impact factor: 14.919

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Journal:  MRS Commun       Date:  2019-04-08       Impact factor: 2.566

3.  Development of a Metabolite Sensor for High-Throughput Detection of Aldehydes in Escherichia Coli.

Authors:  Cláudio R Frazão; Victor Maton; Jean M François; Thomas Walther
Journal:  Front Bioeng Biotechnol       Date:  2018-08-23

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Journal:  Microb Cell Fact       Date:  2020-11-02       Impact factor: 5.328

Review 5.  Approaches to genetic tool development for rapid domestication of non-model microorganisms.

Authors:  Lauren A Riley; Adam M Guss
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Review 6.  Recent advances in metabolic engineering of microorganisms for advancing lignocellulose-derived biofuels.

Authors:  Abhishek Joshi; Krishan K Verma; Vishnu D Rajput; Tatiana Minkina; Jaya Arora
Journal:  Bioengineered       Date:  2022-04       Impact factor: 6.832

7.  Dynamic control of endogenous metabolism with combinatorial logic circuits.

Authors:  Felix Moser; Amin Espah Borujeni; Amar N Ghodasara; Ewen Cameron; Yongjin Park; Christopher A Voigt
Journal:  Mol Syst Biol       Date:  2018-11-27       Impact factor: 11.429

  7 in total

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