Real-time adaptive intelligent microbial systems for dynamic biosynthesis in unpredictable environments - Molavi, Roya; Ghasemi , Reza; Salaseh Dehkhargani , Hossein; Shafiei, Zahra (Archives of Advances in Biosciences, 2025)
Context: Intelligent microbial systems capable of real-time adaptation to environmental perturbations represent a transformative innovation in synthetic biology and bioprocess engineering. These systems dynamically regulate gene expression and metabolic activity through context-responsive genetic circuits, enabling stable and efficient biosynthesis in variable and unpredictable environments. By integrating sensor technologies, control theory, and artificial intelligence, such platforms emulate cognitive biological functions such as perception, decision-making, and response at the microbial level. Evidence Acquisition: This review conducts a systematic examination of the literature published between 2015 and 2025, sourced from PubMed, Web of Science, and ScienceDirect. Inclusion criteria focused on experimental and computational advancements in adaptive microbial systems, particularly studies combining synthetic biology, real-time biosensing, AI-based feedback control, and optimization algorithms. Studies on static or non-intelligent microbial systems were excluded. Results: Emerging research highlights the convergence of biosensors, machine learning models, and modular genetic networks that enable microbes to sense and interpret environmental cues with high temporal resolution. Real-time feedback systems facilitate metabolic flux reprogramming, enhancing yield and process stability. Notable applications span biopharmaceutical production, environmental remediation, precision agriculture, and renewable bioenergy. Case studies demonstrate improvements in ethanol, astaxanthin, and lycopene biosynthesis through dynamic control mechanisms, adaptive laboratory evolution, and in situ optimization strategies. Conclusion: Real-time adaptive microbial systems embody the next generation of programmable biological platforms. Their potential to autonomously adjust to environmental variability positions them as critical enablers of scalable, sustainable, and intelligent biomanufacturing. Advancements in biosensor miniaturization, genome editing, and AI-driven regulation will be essential for their industrial translation. This review outlines a framework for future interdisciplinary research that bridges biology, computation, and engineering to advance autonomous bio-production systems.