About this Research Topic
This research topic titled Artificial Intelligence for Metabolic Engineering aims to showcase the latest advancements and future prospects of employing AI methods, optimisation approaches, and computational modelling in designing, optimizing, and analyzing metabolic pathways. By combining AI techniques such as machine learning, deep learning, and evolutionary algorithms with metabolic engineering, this research topic seeks to enable more efficient and effective design of biochemical pathways and biocatalysts for the production of high-value compounds.
We welcome submissions to this topic in the following areas:
• Machine Learning Approaches for Pathway Design: These approaches leverage large-scale datasets to predict optimal pathway configurations and identify promising targets for metabolic engineering.
• Optimization of Metabolic Networks Using Evolutionary Algorithms: Articles will explore the application of genetic algorithms, particle swarm optimization, and other metaheuristic approaches to optimize pathway fluxes and enhance product yields.
• Integrating Omics Data with AI for Pathway Engineering: This integration enhances the discovery of novel metabolic pathways, identification of key rate-limiting steps, and prediction of metabolic fluxes.
• Deep Learning for Predictive Modelling in Metabolic Engineering: Articles will explore the use of deep learning techniques, such as convolutional neural networks, recurrent neural networks, and generative adversarial networks, for predictive modelling in metabolic engineering.
• AI-assisted Strain Design and Engineering: Articles will explore the prediction of genetic modifications, design of optimal gene expression networks, and identification of novel enzymes or pathways through machine learning and AI algorithms.
• Metabolic Engineering based on Genome-Scale Metabolic Modelling and AI: Articles will explore the possibilities to advance genome-scale metabolic modelling using AI approaches in the context of metabolic engineering applications.
By highlighting artificial intelligence for synthetic biology and metabolic engineering, we aim to promote interdisciplinary collaboration and stimulate the development of AI-based tools and methodologies.
We welcome submissions to this topic in the following areas:
• Machine Learning Approaches for Pathway Design: These approaches leverage large-scale datasets to predict optimal pathway configurations and identify promising targets for metabolic engineering.
• Optimization of Metabolic Networks Using Evolutionary Algorithms: Articles will explore the application of genetic algorithms, particle swarm optimization, and other metaheuristic approaches to optimize pathway fluxes and enhance product yields.
• Integrating Omics Data with AI for Pathway Engineering: This integration enhances the discovery of novel metabolic pathways, identification of key rate-limiting steps, and prediction of metabolic fluxes.
• Deep Learning for Predictive Modelling in Metabolic Engineering: Articles will explore the use of deep learning techniques, such as convolutional neural networks, recurrent neural networks, and generative adversarial networks, for predictive modelling in metabolic engineering.
• AI-assisted Strain Design and Engineering: Articles will explore the prediction of genetic modifications, design of optimal gene expression networks, and identification of novel enzymes or pathways through machine learning and AI algorithms.
• Metabolic Engineering based on Genome-Scale Metabolic Modelling and AI: Articles will explore the possibilities to advance genome-scale metabolic modelling using AI approaches in the context of metabolic engineering applications.
By highlighting artificial intelligence for synthetic biology and metabolic engineering, we aim to promote interdisciplinary collaboration and stimulate the development of AI-based tools and methodologies.
Keywords: metabolic engineering, artificial intelligence, deep learning, strain design, metabolic modelling, metabolic pathways
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
