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Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow
Published on: October 17, 2025
Synthetic biology-driven biosensors for healthcare applications: A roadmap toward programmable and intelligent
Begüm Sarac1, Seydanur Yücer1, Fatih Ciftci1
1Faculty of Engineering, Department of Biomedical Engineering, Fatih Sultan Mehmet Vakıf University, Zeytinburnu, Istanbul, 34015, Turkey; BioriginAI Research Group, Department of Biomedical Engineering, Fatih Sultan Mehmet Vakıf University, Zeytinburnu, Istanbul, 34015, Turkey; Biomedical Electronic Design Application and Research Center (BETAM), Fatih Sultan Mehmet Vakıf University, Istanbul, Turkey.
Abstract:
Synthetic biology has revolutionized biosensor design by enabling programmable, modular systems that integrate biological components with engineered logic. This review explores recent innovations in synthetic biology-driven biosensors, highlighting applications in healthcare, environmental monitoring, and point-of-care diagnostics. Central to these advancements are synthetic gene circuits, CRISPR-based control systems, RNA regulators, and logic gate architectures enabling high specificity, multiplexed detection, and memory-enabled response. Both whole-cell and cell-free platforms have been developed for detecting pathogens, cancer biomarkers, metabolic imbalances, and environmental contaminants. Notably, wearable and paper-based devices now offer real-time monitoring with minimal infrastructure. In medical applications, engineered biosensors show promise for early diagnosis, personalized treatment monitoring, and integrated theranostics, including systems that trigger therapeutic responses upon biomarker detection. Environmental applications include the detection of heavy metals like arsenic and cadmium via genetically modified microbial platforms. Despite significant advances, challenges remain in circuit stability, biosafety, and large-scale deployment. Future directions involve integrating artificial intelligence, feedback-regulated systems, and hybrid materials to enhance sensor adaptability and performance. This review provides a comprehensive framework for developing next-generation biosensors that merge biological intelligence with engineering precision to meet the growing demand for smart, responsive, and scalable diagnostic tools.
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