AI Medical Compendium Journal:
Metabolic engineering

Showing 1 to 10 of 12 articles

Deep learning for NAD/NADP cofactor prediction and engineering using transformer attention analysis in enzymes.

Metabolic engineering
Understanding and manipulating the cofactor preferences of NAD(P)-dependent oxidoreductases, the most widely distributed enzyme group in nature, is increasingly crucial in bioengineering. However, large-scale identification of the cofactor preference...

AI-based automated construction of high-precision Geobacillus thermoglucosidasius enzyme constraint model.

Metabolic engineering
Geobacillus thermoglucosidasius NCIMB 11955 possesses advantages, such as high-temperature tolerance, rapid growth rate, and low contamination risk. Additionally, it features efficient gene editing tools, making it one of the most promising next-gene...

A machine learning framework for extracting information from biological pathway images in the literature.

Metabolic engineering
There have been significant advances in literature mining, allowing for the extraction of target information from the literature. However, biological literature often includes biological pathway images that are difficult to extract in an easily edita...

Cell factory design with advanced metabolic modelling empowered by artificial intelligence.

Metabolic engineering
Advances in synthetic biology and artificial intelligence (AI) have provided new opportunities for modern biotechnology. High-performance cell factories, the backbone of industrial biotechnology, are ultimately responsible for determining whether a b...

Enabling pathway design by multiplex experimentation and machine learning.

Metabolic engineering
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...

Deep learning for metabolic pathway design.

Metabolic engineering
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...

Rank-ordering of known enzymes as starting points for re-engineering novel substrate activity using a convolutional neural network.

Metabolic engineering
Retro-biosynthetic approaches have made significant advances in predicting synthesis routes of target biofuel, bio-renewable or bio-active molecules. The use of only cataloged enzymatic activities limits the discovery of new production routes. Recent...

Active and machine learning-based approaches to rapidly enhance microbial chemical production.

Metabolic engineering
In order to make renewable fuels and chemicals from microbes, new methods are required to engineer microbes more intelligently. Computational approaches, to engineer strains for enhanced chemical production typically rely on detailed mechanistic mode...

Recent advances in constraint and machine learning-based metabolic modeling by leveraging stoichiometric balances, thermodynamic feasibility and kinetic law formalisms.

Metabolic engineering
Understanding the governing principles behind organisms' metabolism and growth underpins their effective deployment as bioproduction chassis. A central objective of metabolic modeling is predicting how metabolism and growth are affected by both exter...

Machine learning for metabolic engineering: A review.

Metabolic engineering
Machine learning provides researchers a unique opportunity to make metabolic engineering more predictable. In this review, we offer an introduction to this discipline in terms that are relatable to metabolic engineers, as well as providing in-depth i...