AIMC Topic: Metabolic Engineering

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

Metabolic engineering Jul 3, 2021
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...
Phenotype Escherichia coli Metabolic Engineering Machine Learning
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A deep learning approach to evaluate the feasibility of enzymatic reactions generated by retrobiosynthesis.

Biotechnology journal Jan 18, 2021
Retrobiosynthesis allows the designing of novel biosynthetic pathways for the production of chemicals and materials through metabolic engineering, but generates a large number of reactions beyond the experimental feasibility. Thus, an effective metho...
Feasibility Studies Biosynthetic Pathways Neural Networks, Computer Deep Learning Metabolic Engineering
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Machine learning for metabolic engineering: A review.

Metabolic engineering Nov 19, 2020
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...
Machine Learning Gene Editing Metabolic Engineering Algorithms
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A machine learning Automated Recommendation Tool for synthetic biology.

Nature communications Sep 25, 2020
Synthetic biology allows us to bioengineer cells to synthesize novel valuable molecules such as renewable biofuels or anticancer drugs. However, traditional synthetic biology approaches involve ad-hoc engineering practices, which lead to long develop...
Beer Dodecanol Synthetic Biology Biofuels Saccharomyces cerevisiae Metabolic Engineering Bayes Theorem Fatty Acids Machine Learning Escherichia coli
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Combining mechanistic and machine learning models for predictive engineering and optimization of tryptophan metabolism.

Nature communications Sep 25, 2020
Through advanced mechanistic modeling and the generation of large high-quality datasets, machine learning is becoming an integral part of understanding and engineering living systems. Here we show that mechanistic and machine learning models can be c...
Machine Learning Algorithms Genotype Metabolic Networks and Pathways Amino Acids Phenotype Biosensing Techniques Metabolic Engineering Saccharomyces cerevisiae Biochemical Phenomena Models, Biological Tryptophan
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Biosystems Design by Machine Learning.

ACS synthetic biology Jun 29, 2020
Biosystems such as enzymes, pathways, and whole cells have been increasingly explored for biotechnological applications. However, the intricate connectivity and resulting complexity of biosystems poses a major hurdle in designing biosystems with desi...
Proteins Linear Models Machine Learning Biotechnology Metabolic Engineering Gene Regulatory Networks Gene Editing
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Reinforcement Learning for Bioretrosynthesis.

ACS synthetic biology Dec 30, 2019
Metabolic engineering aims to produce chemicals of interest from living organisms, to advance toward greener chemistry. Despite efforts, the research and development process is still long and costly, and efficient computational design tools are requi...
Markov Chains Synthetic Biology Monte Carlo Method Reinforcement, Psychology Metabolic Networks and Pathways Models, Biological Software Artificial Intelligence Algorithms Enzymes Metabolic Engineering
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Metabolic perceptrons for neural computing in biological systems.

Nature communications Aug 28, 2019
Synthetic biological circuits are promising tools for developing sophisticated systems for medical, industrial, and environmental applications. So far, circuit implementations commonly rely on gene expression regulation for information processing usi...
Neural Networks, Computer Synthetic Biology Computer Simulation Metabolic Engineering Escherichia coli
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Lessons from Two Design-Build-Test-Learn Cycles of Dodecanol Production in Escherichia coli Aided by Machine Learning.

ACS synthetic biology May 24, 2019
The Design-Build-Test-Learn (DBTL) cycle, facilitated by exponentially improving capabilities in synthetic biology, is an increasingly adopted metabolic engineering framework that represents a more systematic and efficient approach to strain developm...
Synthetic Biology Dodecanol Machine Learning Escherichia coli Metabolic Engineering Metabolic Networks and Pathways Algorithms
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Systems Metabolic Engineering Meets Machine Learning: A New Era for Data-Driven Metabolic Engineering.

Biotechnology journal May 2, 2019
The recent increase in high-throughput capacity of 'omics datasets combined with advances and interest in machine learning (ML) have created great opportunities for systems metabolic engineering. In this regard, data-driven modeling methods have beco...
Metabolic Engineering Systems Biology Biotechnology Machine Learning
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