Dynamic control of endogenous metabolism with combinatorial logic circuits

Controlling gene expression during a bioprocess enables real-time metabolic control, coordinated cellular responses, and staging order-of-operations. Achieving this with small molecule inducers is impractical at scale and dynamic circuits are difficult to design. Here, we show that the same set of s...

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Bibliographic Details
Main Authors: Moser, Felix, Espah Borujeni, Amin, Ghodasara, Amar Navin, Cameron, Douglas, Park, YongJin, Voigt, Christopher A.
Other Authors: Massachusetts Institute of Technology. Institute for Medical Engineering & Science
Format: Article
Published: Nature Publishing Group 2019
Online Access:http://hdl.handle.net/1721.1/121120
https://orcid.org/0000-0002-7762-3084
https://orcid.org/0000-0002-3036-7183
https://orcid.org/0000-0001-5409-1831
https://orcid.org/0000-0002-6788-2429
https://orcid.org/0000-0003-0844-4776
Description
Summary:Controlling gene expression during a bioprocess enables real-time metabolic control, coordinated cellular responses, and staging order-of-operations. Achieving this with small molecule inducers is impractical at scale and dynamic circuits are difficult to design. Here, we show that the same set of sensors can be integrated by different combinatorial logic circuits to vary when genes are turned on and off during growth. Three Escherichia coli sensors that respond to the consumption of feedstock (glucose), dissolved oxygen, and by-product accumulation (acetate) are constructed and optimized. By integrating these sensors, logic circuits implement temporal control over an 18-h period. The circuit outputs are used to regulate endogenous enzymes at the transcriptional and post-translational level using CRISPRi and targeted proteolysis, respectively. As a demonstration, two circuits are designed to control acetate production by matching their dynamics to when endogenous genes are expressed (pta or poxB) and respond by turning off the corresponding gene. This work demonstrates how simple circuits can be implemented to enable customizable dynamic gene regulation.