AIMC Topic: Metabolic Engineering
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Enabling pathway design by multiplex experimentation and machine learning.
Metabolic engineering
Nov 30, 2023
The remarkable metabolic diversity observed in nature has provided a foundation for sustainable production of a wide array of valuable molecules. However, transferring the biosynthetic pathway to the desired host often runs into inherent failures tha...
Design, Evaluation, and Implementation of Synthetic Isopentyldiol Pathways in .
ACS synthetic biology
Oct 23, 2023
Isopentyldiol (IPDO) is an important raw material in the cosmetic industry. So far, IPDO is exclusively produced through chemical synthesis. Growing interest in natural personal care products has inspired the quest to develop a biobased process. We p...
Deep learning for metabolic pathway design.
Metabolic engineering
Sep 19, 2023
The establishment of a bio-based circular economy is imperative in tackling the climate crisis and advancing sustainable development. In this realm, the creation of microbial cell factories is central to generating a variety of chemicals and material...
Generative Artificial Intelligence GPT-4 Accelerates Knowledge Mining and Machine Learning for Synthetic Biology.
ACS synthetic biology
Sep 8, 2023
Knowledge mining from synthetic biology journal articles for machine learning (ML) applications is a labor-intensive process. The development of natural language processing (NLP) tools, such as GPT-4, can accelerate the extraction of published inform...
Metabolic engineering for sustainability and health.
Trends in biotechnology
Jan 10, 2023
Bio-based production of chemicals and materials has attracted much attention due to the urgent need to establish sustainability and enhance human health. Metabolic engineering (ME) allows purposeful modification of cellular metabolic, regulatory, and...
Microbial chassis engineering drives heterologous production of complex secondary metabolites.
Biotechnology advances
Apr 26, 2022
The cryptic secondary metabolite biosynthetic gene clusters (BGCs) far outnumber currently known secondary metabolites. Heterologous production of secondary metabolite BGCs in suitable chassis facilitates yield improvement and discovery of new-to-nat...
Artificial intelligence: a solution to involution of design-build-test-learn cycle.
Current opinion in biotechnology
Apr 7, 2022
Iterative design-build-test-learn (DBTL) cycles are routinely performed during microbial strain development. This useful approach integrates computational strain design, genetic engineering, fermentation testing, and omics analysis to reveal and reso...
Machine learning-informed and synthetic biology-enabled semi-continuous algal cultivation to unleash renewable fuel productivity.
Nature communications
Jan 27, 2022
Algal biofuel is regarded as one of the ultimate solutions for renewable energy, but its commercialization is hindered by growth limitations caused by mutual shading and high harvest costs. We overcome these challenges by advancing machine learning t...
Machine learning-guided acyl-ACP reductase engineering for improved in vivo fatty alcohol production.
Nature communications
Oct 5, 2021
Alcohol-forming fatty acyl reductases (FARs) catalyze the reduction of thioesters to alcohols and are key enzymes for microbial production of fatty alcohols. Many metabolic engineering strategies utilize FARs to produce fatty alcohols from intracellu...
Applications of artificial intelligence to enzyme and pathway design for metabolic engineering.
Current opinion in biotechnology
Aug 3, 2021
Metabolic engineering for developing industrial strains capable of overproducing bioproducts requires good understanding of cellular metabolism, including metabolic reactions and enzymes. However, metabolic pathways and enzymes involved are still unk...
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