Unlocking Microbial Potential: AI/ML-Driven Metabolic Engineering for Health and Sustainability

Microbial metabolic engineering stands as a cornerstone of modern synthetic biology, enabling the rational design and manipulation of microbes to produce high-value compounds such as therapeutics, bio-based chemicals, agricultural products, and environmental remediation agents. Despite substantial advancements in genome editing, pathway optimization, and high-throughput methodologies, the inherent complexity of microbial metabolism—governed by nonlinear regulatory networks and emergent cellular behaviors—continues to constrain our capacity for truly predictive design. Recent breakthroughs in artificial intelligence (AI) and machine learning (ML) have unlocked transformative potential for addressing these challenges. By leveraging data-driven modeling, generative algorithms, and predictive analytics, AI/ML tools can decode metabolic pathways, forecast system-level responses, and explore vast biochemical design spaces. With the rapid expansion of multi-omics datasets and the growing adoption of automated experimental platforms, the integration of AI/ML approaches into metabolic engineering represents a crucial frontier for precision biotechnology and sustainable innovation.This Research Topic aims to showcase and advance cutting-edge developments at the intersection of AI/ML and microbial metabolic engineering. The primary goal is to gather pioneering studies demonstrating how AI-powered methods—from predictive modeling and multi-omics integration to generative design—can accelerate the engineering of microbes with enhanced stability, efficiency, and function. By highlighting methodological innovations, applied case studies, and theoretical insights, this Research Topic seeks to elucidate how intelligent computational frameworks can reshape the design-build-test-learn (DBTL) cycle, improve predictability, and foster the creation of sustainable, health-oriented biotechnologies. Ultimately, the goal is to cultivate a multidisciplinary dialogue that bridges synthetic biology, computational science, and systems engineering, thereby defining the roadmap toward intelligent, scalable, and ethically responsible microbial design.The scope of this Research Topic encompasses methodological, computational, and applied research efforts that employ AI/ML approaches to model, design, or optimize microbial metabolism. While the primary focus lies in advancing data-driven innovation, studies addressing the integration of experimental validation, mechanistic modeling, and system-level optimization are equally encouraged.To gather further insights in this rapidly evolving field, we welcome articles addressing, but not limited to, the following themes:-Metabolic network modeling using AI/ML approaches-Predictive and generative frameworks for pathway and strain design-Multi-omics data integration and genome-scale metabolic prediction-Digital twins and automated next-generation DBTL pipelines-AI-assisted microbial community engineering and consortia optimization-Deep learning, probabilistic, or hybrid mechanistic–ML models for metabolic control-Applications of engineered microbes in therapeutics, biomaterials, nutrients, green chemistry, bioremediation, and carbon captureAccepted article types include original research, reviews, perspectives, methods papers, and data papers. Topic editors: Ramesh Jha, Ahsan Islam Source: https://www.frontiersin.org/research-topics/78761/unlocking-microbial-potential-aiml-driven-metabolic-engineering-for-health-and-sustainability