Engineering microbes for bioproduct manufacturing represents an increasingly essential approach in biotechnology, promising sustainable alternatives to petroleum-derived resources. Recent advances have shown substantial progress in synthetic biology, metabolic engineering, and systems biology, notably enhancing microbial strain developments, optimizing metabolic pathways, and reducing reliance on fossil-based raw materials. These innovations have facilitated significant breakthroughs in biofuel production, pharmaceuticals, fine chemicals, and bioplastics synthesis and upcycling. Yet, the industrial implementation of microbial engineering still faces persistent hurdles, including strain stability concerns, metabolic pathway efficiency bottlenecks, and scalability challenges, calling for further advances to bridge the existing gaps between laboratory research and industrial application.

This Research Topic aims to present and explore the latest innovative strategies utilizing metabolic engineering and artificial intelligence (AI) tools to address current challenges in microbial bioproduction processes. It will emphasize studies aimed at optimizing microbial strain performance, developing novel metabolic pathways, enhancing enzyme activity, and integrating AI methodologies to accelerate strain design and process optimization. By illustrating these cutting-edge endeavors, the goal is to contribute toward overcoming current industrial bottlenecks and to facilitate efficient, scalable, and sustainable bioprocesses aligned with global sustainability objectives, particularly United Nations SDGs 7 and 9.

To gather further insights within the intersection of microbial engineering and AI-driven optimization, we welcome articles addressing, but not limited to, the following themes:

o Novel pathways, enzyme and metabolic engineering, and synthetic biology approaches for dynamic pathway regulation.
o Production strategies for biofuels, bioplastics, pharmaceuticals, and specialty chemicals using engineered microbial strains.
o Strain engineering and adaptive evolution to enhance robustness, productivity, and industrial viability.
o Multi-omics technologies and analyses enabling robust microbial pathway understanding and strain optimization.
o Machine learning applications for accurate predictions of metabolic fluxes and enzyme functions.
o AI-aided analysis and integration of comprehensive multi-omics datasets.
o Automated bioprocess control and scale-up strategies facilitated by AI and machine learning tools.
o Development of predictive metabolic and fermentation process models utilizing AI-driven methodologies.

Are you interested in contributing a manuscript to this Research Topic?

Engineering Microbes for Sustainable Bioproducts: Innovations in Metabolic Engineering and AI-Driven Optimization