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Updated: Aug 30, 2026

FIBS-enabled Noninvasive Metabolic Profiling
Published on: February 3, 2014
Fluorescent biosensing of glucose and ATP in biological systems: Photophysical mechanisms and AI-assisted probe
Abrar Hussain1, Khurram Shahzad1, Shahzaib Akhter2
1Advanced Radiation Technology Institute, Korea Atomic Energy Research Institute (KAERI), Jeongeup 56212, Republic of Korea; Department of Radiation Science, University of Science and Technology (UST), Daejeon 34113, Republic of Korea.
Abstract:
Glucose and adenosine triphosphate (ATP) are essential metabolic biomarkers involved in cellular energy regulation, physiological homeostasis, and disease progression. Accurate and real-time detection of these molecules is therefore critically important in biological and biomedical research. Although conventional analytical techniques provide excellent sensitivity and accuracy, their dependence on sophisticated instrumentation and extensive sample preparation limits their application in continuous and point-of-care monitoring. Fluorescence spectroscopy has emerged as a powerful analytical approach due to its high sensitivity, rapid response, tunable photophysical properties, and compatibility with biological imaging and real-time monitoring. This review critically examines recent advances in fluorescent probes and optical biosensing platforms for glucose and ATP detection. It places particular emphasis on the relationship between molecular probe design, photophysical behavior, and sensing performance. Major fluorescence transduction mechanisms, including photoinduced electron transfer (PET), intramolecular charge transfer (ICT), Förster resonance energy transfer (FRET), and aggregation-induced emission (AIE), are discussed in terms of their excited-state dynamics and spectroscopic characteristics. Representative sensing platforms are comparatively evaluated based on detection limit, linear dynamic range, selectivity, response time, and performance in complex biological matrices. Current challenges associated with signal interference, probe photostability, biocompatibility, and limited in vivo applicability are highlighted. Furthermore, emerging computational and data-driven approaches, including TD-DFT calculations, HOMO-LUMO analysis, machine learning (ML), and artificial intelligence (AI)-assisted photophysical prediction, are discussed as promising tools for rational probe optimization and mechanistic understanding. Overall, this review provides mechanistic and computational insights into the design of fluorescent probes and biosensing platforms, offering a critical framework for advancing next-generation optical biosensing systems with enhanced sensitivity, selectivity, and in vivo applicability.
