Dynamic Control of Metabolism

Abstract

Metabolic engineering reprograms cells to synthesize value-added products. In doing so, endogenous genes are altered and heterologous genes can be introduced to achieve the necessary enzymatic reactions. Dynamic regulation of metabolic flux is a powerful control scheme to alleviate and overcome the competing cellular objectives that arise from the introduction of these production pathways. This review explores dynamic regulation strategies that have demonstrated significant production benefits by targeting the metabolic node corresponding to a specific challenge. We summarize the stimulus-responsive control circuits employed in these strategies that determine the criterion for actuating a dynamic response and then examine the points of control that couple the stimulus-responsive circuit to a shift in metabolic flux. Expected final online publication date for the Annual Review of Chemical and Biomolecular Engineering, Volume 12 is June 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Strategies for Enhancing Microbial Production of 2′-Fucosyllactose, the Most Abundant Human Milk Oligosaccharide

“…Recently, multicopy chromosomal integration using transposon-encoded CRISPR–Cas systems, which directs RNA-guided DNA integration independent of homologous recombination, can be efficiently achieved by designing a specific crRNA to construct a stable chassis cell. , On the other hand, metabolic engineering-based microbial production of 2′-FL might produce imbalanced cellular metabolic networks due to the imbalance in the supply of precursors and cofactors or between cell growth and 2′-FL synthesis. Thus, dynamic regulation of key gene expression via a metabolite-responsive biosensor or pathway-independent genetic control tool, such as quorum sensing (QS), has become an effective method to dynamically regulate metabolic fluxes, which could be applied in 2′-FL production to maximize its productivity and yield. , In addition, enhancement of lactose utilization and 2′-FL export would be also beneficial for cellular metabolism and viability to improve 2′-FL titer.…”

“…Thus, dynamic regulation of key gene expression via a metabolite-responsive biosensor or pathway-independent genetic control tool, such as quorum sensing (QS), has become an effective method to dynamically regulate metabolic fluxes, which could be applied in 2′-FL production to maximize its productivity and yield. 147,148 In addition, enhancement of lactose utilization and 2′-FL export would be also beneficial for cellular metabolism and viability to improve 2′-FL titer.…”

In vitro reconstituted quorum sensing pathways enable rapid evaluation of quorum sensing inhibitors

“…With an increasing number of microbial quorum sensing systems being reported, synthetic biologists are keen to excavate, optimize and standardize QS components, e.g., enzymes responsible for AI synthesis, receptors that capture AIs sensitively, and AI-responsive promoters for expressing gene of interest with broad dynamic range, and to design artificial quorum sensing circuitry with high orthogonality. Notably, the synthetic biology community has reprogrammed quorum sensing as a toolkit to rationally regulate cellular group activities, facilitating myriad applications including population density control [48,49], biological pattern formation [50,51] and edge detection [52], biosensing [53], biocomputing [54,55], growth-coupled metabolic engineering [56], social interaction programming [57], and solid tumor therapy mediated by quorum oscillators [58,59].…”

Platforms for Optogenetic Stimulation and Feedback Control

“…One of the major goals of metabolic engineering research today is to maximize the yield of a given bioproduction process. To achieve this goal, there is an ever-growing interest in the synthetic biology community to build genetic constructs allowing for dynamic control of metabolism ( Venayak et al, 2015 ; Lynch, 2016 ; Ni et al, 2021 ). The idea is to introduce externally inducible systems in metabolic pathways with different induction strategies ( Lalwani et al, 2018 ; Bertaux et al, 2021b ).…”