Relieving metabolic burden to improve robustness and bioproduction by industrial microorganisms

Biotechnology Advances, Journal Year: 2024, Volume and Issue: 74, P. 108401 - 108401

Published: June 27, 2024

Metabolic burden is defined by the influence of genetic manipulation and environmental perturbations on distribution cellular resources. The rewiring microbial metabolism for bio-based chemical production often leads to a metabolic burden, followed adverse physiological effects, such as impaired cell growth low product yields. Alleviating imposed undesirable changes has become an increasingly attractive approach constructing robust factories. In this review, we provide brief overview engineering, focusing specifically recent developments strategies diminishing while improving robustness yield. A variety examples are presented showcase promise engineering in facilitating design construction Finally, challenges limitations encountered discussed.

Language: Английский

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Genetically encoded biosensors for microbial synthetic biology: From conceptual frameworks to practical applications

Biotechnology Advances, Journal Year: 2022, Volume and Issue: 62, P. 108077 - 108077

Published: Dec. 9, 2022

Language: Английский

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CRISPR–dCas12a-mediated genetic circuit cascades for multiplexed pathway optimization

Nature Chemical Biology, Journal Year: 2023, Volume and Issue: 19(3), P. 367 - 377

Published: Jan. 16, 2023

Language: Английский

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Transcription factor-based biosensors for screening and dynamic regulation

Frontiers in Bioengineering and Biotechnology, Journal Year: 2023, Volume and Issue: 11

Published: Feb. 6, 2023

Advances in synthetic biology and genetic engineering are bringing into the spotlight a wide range of bio-based applications that demand better sensing control biological behaviours. Transcription factor (TF)-based biosensors promising tools can be used to detect several types chemical compounds elicit response according desired application. However, wider use this type device is still hindered by challenges, which addressed increasing current metabolite-activated transcription knowledge base, developing methods identify new factors, improving overall workflow for design novel biosensor circuits. These improvements particularly important bioproduction field, where researchers need biosensor-based approaches screening production-strains precise dynamic regulation strategies. In work, we summarize what currently known about factor-based biosensors, discuss recent experimental computational targeted at their modification improvement, suggest possible future research directions based on two applications:

Language: Английский

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A review on emerging technologies and machine learning approaches for sustainable production of biofuel from biomass waste

Biomass and Bioenergy, Journal Year: 2023, Volume and Issue: 180, P. 106997 - 106997

Published: Nov. 23, 2023

Language: Английский

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Biological valorization of lignin to flavonoids

Biotechnology Advances, Journal Year: 2023, Volume and Issue: 64, P. 108107 - 108107

Published: Feb. 7, 2023

Language: Английский

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Transcription factor-based biosensor: A molecular-guided approach for advanced biofuel synthesis

Biotechnology Advances, Journal Year: 2024, Volume and Issue: 72, P. 108339 - 108339

Published: March 18, 2024

Language: Английский

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The expanded CRISPR toolbox for constructing microbial cell factories

Trends in biotechnology, Journal Year: 2023, Volume and Issue: 42(1), P. 104 - 118

Published: July 26, 2023

Language: Английский

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Improving Microbial Cell Factory Performance by Engineering SAM Availability

Journal of Agricultural and Food Chemistry, Journal Year: 2024, Volume and Issue: 72(8), P. 3846 - 3871

Published: Feb. 19, 2024

Methylated natural products are widely spread in nature.

Language: Английский

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TFBMiner: A User-Friendly Command Line Tool for the Rapid Mining of Transcription Factor-Based Biosensors

ACS Synthetic Biology, Journal Year: 2023, Volume and Issue: 12(5), P. 1497 - 1507

Published: April 13, 2023

Transcription factors responsive to small molecules are essential elements in synthetic biology designs. They often used as genetically encoded biosensors with applications ranging from the detection of environmental contaminants and biomarkers microbial strain engineering. Despite our efforts expand space compounds that can be detected using biosensors, identification characterization transcription their corresponding inducer remain labor- time-intensive tasks. Here, we introduce TFBMiner, a new data mining analysis pipeline enables automated rapid putative metabolite-responsive factor-based (TFBs). This user-friendly command line tool harnesses heuristic rule-based model gene organization identify both clusters involved catabolism user-defined associated transcriptional regulators. Ultimately, scored based on how well they fit model, providing wet-lab scientists ranked list candidates experimentally tested. We validated set for which TFBs have been reported previously, including sensors responding sugars, amino acids, aromatic compounds, among others. further demonstrated utility TFBMiner by identifying biosensor S-mandelic acid, an compound factor had not found previously. Using combinatorial library mandelate-producing strains, newly identified was able distinguish between low- high-producing candidates. work will aid unraveling regulatory networks toolbox allow construction more sophisticated self-regulating biosynthetic pathways.

Language: Английский

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