About this Research Topic
The application of Artificial Intelligence (AI) in industrial microbiology is ushering in a new era of innovation and efficiency. AI technologies, particularly machine learning and deep learning, are proving instrumental in deciphering complex biological data, optimizing microbial processes, and predicting outcomes with unprecedented accuracy. Recent advances have shown how AI can significantly enhance strain development, refine metabolic pathway predictions, improve fermentation process efficiencies, and maintain stringent quality controls. These innovations are not just boosting productivity but are also accelerating the pace of discovery and application in industrial microbiology, making it a focal point for future research and development.
This Research Topic aims to gather pioneering research focused on the utilization of AI in various aspects of industrial microbiology. We invite original research articles, comprehensive reviews, and short communications that address a wide range of topics including AI-driven microbial strain engineering, modelling of metabolic networks, optimization of bioprocesses, predictive analytics in fermentation, and quality control systems. We are particularly interested in works that explore data-driven methodologies for studying microbial ecosystems, AI-based diagnostic and monitoring tools, and applications of AI in scaling up bioprocesses. Contributions that highlight the synergy between AI and other emerging technologies such as synthetic biology and industrial biotechnology are also highly encouraged.
The main goal of this Research Topic is to compile an authoritative and forward-looking set of research articles that emphasize the innovative applications of AI in industrial microbiology. By attracting submissions from leading scientists and researchers, we aim to create a comprehensive platform for disseminating cutting-edge research and technological advancements. This Research Topic seeks to promote interdisciplinary dialogue, inspire collaborative research efforts, and address the current challenges and opportunities within the field. Ultimately, our aim is to support the integration of AI into industrial microbiology, leading to enhanced efficiency, sustainability, and innovation across the industry.
This Research Topic aims to gather pioneering research focused on the utilization of AI in various aspects of industrial microbiology. We invite original research articles, comprehensive reviews, and short communications that address a wide range of topics including AI-driven microbial strain engineering, modelling of metabolic networks, optimization of bioprocesses, predictive analytics in fermentation, and quality control systems. We are particularly interested in works that explore data-driven methodologies for studying microbial ecosystems, AI-based diagnostic and monitoring tools, and applications of AI in scaling up bioprocesses. Contributions that highlight the synergy between AI and other emerging technologies such as synthetic biology and industrial biotechnology are also highly encouraged.
The main goal of this Research Topic is to compile an authoritative and forward-looking set of research articles that emphasize the innovative applications of AI in industrial microbiology. By attracting submissions from leading scientists and researchers, we aim to create a comprehensive platform for disseminating cutting-edge research and technological advancements. This Research Topic seeks to promote interdisciplinary dialogue, inspire collaborative research efforts, and address the current challenges and opportunities within the field. Ultimately, our aim is to support the integration of AI into industrial microbiology, leading to enhanced efficiency, sustainability, and innovation across the industry.
Keywords: artificial intelligence, AI in Industrial Microbiology, Metabolic Pathway Optimization, Fermentation Process Efficiency, Quality Control Systems, Machine Learning, Deep Learning, Industrial Biotechnology Innovations
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.
