SYNBIOCHEM Design-Build-Test-Learn pipeline

Name: SYNBIOCHEM Design-Build-Test-Learn pipeline
Creator: Pablo Carbonell
Published: 2018-05-02T08:39:27.901Z
Keywords: Synthetic Biology, Metabolic Engineering

Carbonell, Pablo; Jervis, Adrian J; Robinson, Christopher J; Yan, Cunyu; Dunstan, Mark; Swainston, Neil; Vinaixa, Maria; Hollywood, Katherine A; Currin, Andrew; Rattray, Nicholas J W; Taylor, Sandra; Spiess, Reynar; Sung, Rehana; Williams, Alan R; Fellows, Donal; Stanford, Natalie J; Mulherin, Paul; Le Feuvre, Rosalind; Barran, Perdita; Goodacre, Royston; Turner, Nicholas J; Goble, Carole; Chen, George G; Kell, Douglas B; Micklefield, Jason; Breitling, Rainer; Takano, Eriko; Faulon, Jean-Loup; Scrutton, Nigel S

doi:10.17632/8g4wfwtd43.1

SYNBIOCHEM Design-Build-Test-Learn pipeline

Published: 2 May 2018| Version 1 | DOI: 10.17632/8g4wfwtd43.1

Contributors:

Pablo Carbonell,

,

Description

Quantified titers and peak values measured through different rounds of the SYNBIOCHEM Design-Build-Test-Learn (DBTL) pipeline for microbial production of fine chemicals. The pipeline was applied for the production of flavonoids and alkaloids in Escherichia coli. The flavonoids data contains cinnamate and (2S)-pinocembrin measurements for combinatorial libraries for: a) DBT round 1; b) DBT round 2; c) Chassis selection; d) Media screening; e) Optimization. The alkaloids data provides (S)-reticuline and (S)-scoulerine measurements for a combinatorial library corresponding to one DBTL round.

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The University of Manchester

SYNBIOCHEM Design-Build-Test-Learn pipeline

Description

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Institutions

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