Engineered bacteria-enabled biosensing: integration with artificial intelligence for enhanced diagnostic precision

Abstract

Engineered bacteria—microbial strains endowed with bespoke functionalities via genetic or protein engineering—represent a cornerstone of synthetic biology. These living diagnostics have shown immense potential across diverse sensing applications, including disease diagnostics, environmental surveillance, and food safety assessment. Recent advances in artificial intelligence (AI) have further propelled the field by enabling the efficient analysis of complex biological datasets, identification of nuanced signal patterns, and rational optimization of genetic circuits. In this review, we first summarize the fundamental design principles underpinning engineered bacterial biosensors, encompassing chassis selection, genetic circuit construction, and signal transduction modules. We then explore emerging roles of AI in component discovery, regulatory circuit refinement, and predictive modeling of system behavior. Representative case studies highlight the translational potential of these intelligent biosensors in real-world monitoring scenarios. Finally, we discuss key challenges—including biosafety, long-term stability, and regulatory hurdles—and propose future directions for the development and clinical implementation of AI-augmented engineered bacterial sensing platforms.