About this Research Topic
In recent years, the field of metabolic engineering has made great progress, leading to the development of microbial cell factories for the synthesis of value-added compounds. Metabolic engineering, which has been aided by advances in computational tools, synthetic biology tools, and analytical tools, provides a sustainable alternative to conventional petroleum-based manufacturing systems. Selecting a target product with high demand and prospective uses, as well as a host strain that can be designed to produce the desired product at high titer, rate, and yield, is part of the process. After that, the design, build, test, and learn (DBTL) cycle is used to build native or non-native biochemical pathways and optimize metabolic fluxes toward the desired product. Despite these advances, obstacles and barriers continue to exist due to a lack of fundamental knowledge. The goal of this research topic is to explore future directions in metabolic engineering, such as substrate and microbe selection, the DBTL cycle for converting organisms into microbial cell factories, and the hurdles to and potential solutions for efficient cell factory development. Furthermore, the topic will emphasize strategies for increasing strain robustness through adaptive laboratory evolution (ALE) and metabolic engineering techniques, as well as the use of various carbon sources and the applications of microbial cell factories in the production of biofuels and bioproducts.
The focus of this research topic is to address the challenges and constraints in metabolic engineering, as well as to investigate current breakthroughs that can be used to create efficient and sustainable microbial cell factories for the production of chemicals and fuels. To broaden the spectrum of possible feedstocks for microbial cell factories, one critical issue to be addressed is the selection and utilization of varied carbon sources, including both sugar and non-sugar substrates. This includes investigating the pretreatment of lignocellulosic biomass to produce fermentable sugars as well as experimenting with other substrates such as pentose sugars, 3-carbon and 2-carbon substrates, and even carbon dioxide (CO2) and other C1 compounds such as methanol and formate. Recent advances in substrate utilization and co-utilization will be investigated in order to improve product conversion efficiency and sustainability. Another issue to be addressed is the optimization of the DBTL cycle for strain design, construction, testing, and learning. The use of machine learning-based technologies in enzyme and metabolic engineering offers promise in terms of speeding up bioengineering procedures. Machine learning (ML) will be used to improve many stages of the metabolic engineering development cycle, such as gene annotation, route design, optimization, constructing, testing, and scale-up. Furthermore, the purpose of this study topic is to look at the use of microbial cell factories in the manufacture of biofuels and bioproducts. The goal is to increase the synthesis of bulk chemicals, lipids, oleochemicals, alcohols, organic acids, natural products, and other valuable compounds by applying the DBTL cycle and employing metabolic engineering strategies. Furthermore, the topic will investigate methods for enhancing strain robustness through adaptive laboratory evolution (ALE) and metabolic engineering techniques. This research topic aims to contribute to the development of efficient and sustainable microbial cell factories for the synthesis of biofuels and bioproducts by addressing these challenges and leveraging recent advances, ultimately driving the adoption of metabolic engineering as a viable alternative to conventional production processes.
Contributions that investigate various elements of metabolic engineering and its application in the development of efficient microbial cell factories for the production of chemicals and fuels are invited. Among the specific topics of interest are:
- Substrate usage: Investigation into the usage and co-utilization of various carbon sources, such as sugar and non-sugar substrates, in order to broaden the spectrum of feedstocks for microbial cell factories.
- Optimization of the DBTL Cycle: Advances in the strain engineering design, build, test, and learn (DBTL) cycle, including the use of machine learning-based tools in enzyme and metabolic engineering, CRISPR/Cas9-based genome editing tools, omics-based tools like genomics, transcriptomics, proteomics, metabolomics, and fluxomics, and computational tools like genome-scale models (GEMs), kinetic models, and machine learning models.
- Applications in Biofuels and Bioproducts: Studies demonstrating the implementation of the DBTL cycle to convert organisms into microbial cell factories for the synthesis of biofuels, bulk chemicals, lipids, oleochemicals, alcohols, organic acids, natural products, and other valuable compounds.
- Strain Robustness: Techniques for improving strain robustness using adaptive laboratory evolution (ALE) and metabolic engineering.
Original research articles, method articles, technology reports, reviews, mini-reviews, and perspective and opinion articles that address these issues and contribute to the understanding and advancement of metabolic engineering are encouraged to be submitted. Manuscripts that use experimental, computational, or theoretical approaches are all encouraged. The purpose is to promote interdisciplinary dialogue and to communicate recent advances in the field.
The focus of this research topic is to address the challenges and constraints in metabolic engineering, as well as to investigate current breakthroughs that can be used to create efficient and sustainable microbial cell factories for the production of chemicals and fuels. To broaden the spectrum of possible feedstocks for microbial cell factories, one critical issue to be addressed is the selection and utilization of varied carbon sources, including both sugar and non-sugar substrates. This includes investigating the pretreatment of lignocellulosic biomass to produce fermentable sugars as well as experimenting with other substrates such as pentose sugars, 3-carbon and 2-carbon substrates, and even carbon dioxide (CO2) and other C1 compounds such as methanol and formate. Recent advances in substrate utilization and co-utilization will be investigated in order to improve product conversion efficiency and sustainability. Another issue to be addressed is the optimization of the DBTL cycle for strain design, construction, testing, and learning. The use of machine learning-based technologies in enzyme and metabolic engineering offers promise in terms of speeding up bioengineering procedures. Machine learning (ML) will be used to improve many stages of the metabolic engineering development cycle, such as gene annotation, route design, optimization, constructing, testing, and scale-up. Furthermore, the purpose of this study topic is to look at the use of microbial cell factories in the manufacture of biofuels and bioproducts. The goal is to increase the synthesis of bulk chemicals, lipids, oleochemicals, alcohols, organic acids, natural products, and other valuable compounds by applying the DBTL cycle and employing metabolic engineering strategies. Furthermore, the topic will investigate methods for enhancing strain robustness through adaptive laboratory evolution (ALE) and metabolic engineering techniques. This research topic aims to contribute to the development of efficient and sustainable microbial cell factories for the synthesis of biofuels and bioproducts by addressing these challenges and leveraging recent advances, ultimately driving the adoption of metabolic engineering as a viable alternative to conventional production processes.
Contributions that investigate various elements of metabolic engineering and its application in the development of efficient microbial cell factories for the production of chemicals and fuels are invited. Among the specific topics of interest are:
- Substrate usage: Investigation into the usage and co-utilization of various carbon sources, such as sugar and non-sugar substrates, in order to broaden the spectrum of feedstocks for microbial cell factories.
- Optimization of the DBTL Cycle: Advances in the strain engineering design, build, test, and learn (DBTL) cycle, including the use of machine learning-based tools in enzyme and metabolic engineering, CRISPR/Cas9-based genome editing tools, omics-based tools like genomics, transcriptomics, proteomics, metabolomics, and fluxomics, and computational tools like genome-scale models (GEMs), kinetic models, and machine learning models.
- Applications in Biofuels and Bioproducts: Studies demonstrating the implementation of the DBTL cycle to convert organisms into microbial cell factories for the synthesis of biofuels, bulk chemicals, lipids, oleochemicals, alcohols, organic acids, natural products, and other valuable compounds.
- Strain Robustness: Techniques for improving strain robustness using adaptive laboratory evolution (ALE) and metabolic engineering.
Original research articles, method articles, technology reports, reviews, mini-reviews, and perspective and opinion articles that address these issues and contribute to the understanding and advancement of metabolic engineering are encouraged to be submitted. Manuscripts that use experimental, computational, or theoretical approaches are all encouraged. The purpose is to promote interdisciplinary dialogue and to communicate recent advances in the field.
Keywords: Metabolic engineering, microbial cell factories, substrate utilization, DBTL cycle, strain engineering, carbon sources, synthetic biology, computational tools, machine learning, genome editing, omics-based tools, biofuels, bioproducts, strain robustness
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.
