Biosensor-Based Detection and Quantification of Arsenic in Drinking Water.

Authors

Oskar S Zeballos Huarachi, Andrea A Tambo Santos, Jonatan J Aliaga Rodriguez, Andy K Porcel Velasquez, Karen Cabero, Cesar Perez-Fernandez, Yadira Boada, Noemi Tirado, Mauricio Ormachea, Maria Eugenia García, Cecilia Gonzalez, Maria Teresa Alvarez-Aliaga, Tania Pozzo

Published in

ACS synthetic biology. Jul 17, 2026. Epub Jul 17, 2026.

Abstract

Arsenic contamination of drinking water remains a persistent global health burden and an environmental justice challenge, particularly for low-resource communities that lack access to reliable monitoring tools. Synthetic-biology-driven biosensors offer a promising complement to conventional analytical methods by coupling arsenic-responsive genetic circuits with portable, low-cost readouts suitable for field deployment. This review traces the evolution from the early ArsR-based Escherichia coli biosensor to modern whole-cell and cell-free platforms that approach World Health Organization-relevant detection limits for arsenic in water under controlled conditions, emphasizing how signal amplification strategies intersect with shelf life, biosafety, and regulatory simplicity. The operational principles of ars operon-derived modules are examined across detection, processing, and host-engineering layers that collectively tune sensitivity, dynamic range, and robustness. Immobilization formats, microfluidic architectures, and transduction mechanisms─including colorimetric, fluorescent, bioluminescent, and electrochemical outputs─are analyzed for their ability to integrate biological sensing with commodity optics and electronics in portable devices. Building on this engineering landscape, the review highlights how biodesign automation, high-throughput Design-Build-Test-Learn workflows, and emerging AI tools such as supervised learning and Bayesian optimization are accelerating the construction and optimization of arsenic-responsive genetic circuits. Biosafety and regulatory considerations, including biocontainment, standardized stress-testing, and community codesign, are discussed to position arsenic biosensors as candidates for integration into distributed water-quality monitoring networks that combine synthetic biology, low-cost hardware, automation, and AI under robust governance regimes.

PMID:
42469591
Bibliographic data and abstract were imported from PubMed on 18 Jul 2026.

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