Extended Metabolic Biosensor Design for Dynamic Pathway Regulation of Cell Factories.

Yadira Boada,Alejandro Vignoni,Jesús Picó,Pablo Carbonell

doi:10.1016/j.isci.2020.101305

Abstract

SummaryTranscription factor-based biosensors naturally occur in metabolic pathways to maintain cell growth and to provide a robust response to environmental fluctuations. Extended metabolic biosensors, i.e., the cascading of a bio-conversion pathway and a transcription factor (TF) responsive to the downstream effector metabolite, provide sensing capabilities beyond natural effectors for implementing context-aware synthetic genetic circuits and bio-observers. However, the engineering of such multi-step circuits is challenged by stability and robustness issues. In order to streamline the design of TF-based biosensors in metabolic pathways, here we investigate the response of a genetic circuit combining a TF-based extended metabolic biosensor with an antithetic integral circuit, a feedback controller that achieves robustness against environmental fluctuations. The dynamic response of an extended biosensor-based regulated flavonoid pathway is analyzed in order to address the issues of biosensor tuning of the regulated pathway under industrial biomanufacturing operating constraints.

Highlights

Natural cells maintain robust growth and withstand environmental fluctuations by dynamically adjusting cellular metabolism through complex regulatory networks (Liu et al, 2018)
Transcription factor-based biosensors naturally occur in metabolic pathways to maintain cell growth and to provide a robust response to environmental fluctuations
In order to streamline the design of transcription factor (TF)-based biosensors in metabolic pathways, here we investigate the response of a genetic circuit combining a TFbased extended metabolic biosensor with an antithetic integral circuit, a feedback controller that achieves robustness against environmental fluctuations

Summary

Introduction

Natural cells maintain robust growth and withstand environmental fluctuations by dynamically adjusting cellular metabolism through complex regulatory networks (Liu et al, 2018). There exist several metabolic pathway-balancing approaches that optimize gene expression and flux distribution based on in silico predictions provided by static constraint-based metabolic genome-scale models (Purdy and Reed, 2017), using regulatory elements (DNA copy number, promoter and ribosome binding site [RBS] engineering) (Nielsen et al, 2016), synthetic scaffolds, compartmentalization, and flux diversion (silencing, knockouts, alternative carbon sources) (Chae et al, 2017) These pathway regulation strategies optimizing for a particular condition are static, so they are unable to respond to growth and environmental changes that occur in a bioreactor setup (Shi et al, 2018; Wehrs et al, 2019). These static control systems may not be suitable when piecing together a complicated pathway with biosynthetic modules with mismatched input/output levels or when there is a need to minimize the accumulation of potentially toxic intermediates (Shi et al, 2018)

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