With the rapid advancement of biotechnology and growing demands for affordable healthcare, particularly in resource limited settings, traditional diagnostic methods reliant on complex instrumentation such as PCR and mass spectrometry face challenges in achieving rapid, sensitive, and portable testing solutions (Law et al., 2015). Bacterial biosensors, which employ engineered bacteria as programmable sensing elements, offer a cost effective and scalable alternative. These devices detect target analytes including pathogens, metabolites and biomolecules, through synthetic genetic circuits, converting biological signals into quantifiable outputs such as electrical currents or fluorescence via electrochemical or optical interfaces (Nakamura et al., 2008). Recent advances in synthetic biology, such as CRISPR-based gene circuits and synthetic circuit, have enabled precise tuning of bacterial sensing pathways. Such innovations expand medical applications from early disease diagnosis to personalized therapeutics (Riglar and Silver, 2018). However, critical barriers such as biosafety concerns and functional stability hinder clinical translation. This article broadly examines recent advances in bacterial biosensor design, highlights their emerging roles in precision medicine, and critically discusses unresolved technical and regulatory hurdles.

Bacteria possess remarkable environmental adaptability, allowing them to detect and react to environmental alterations, such as variations in chemical concentrations, temperature, and pH levels (Andrianantoandro et al., 2006; Park et al., 2013). This unique trait makes bacteria one of the most suitable biological recognition components for biosensors (Soltani Zarrin et al., 2018). Bacterial biosensors achieve specific detection of target molecules or environmental factors by converting biological responses into quantifiable signals. Their functionality relies on the coordinated operation of three core components: the input module (sensing unit), signal transduction module (processing unit), and output module (response unit) (Chen et al., 2023). The input module of bacterial biosensors functions as the sensing element responsible for the specific recognition of and response to target signals, relying on selective molecular interactions between biomolecules (Saltepe and Kehribar et al., 2018). This module employs both naturally occurring components such as transcription factors and membrane receptors and engineered constructs, including aptamers or nucleic acid switches, to directly capture external stimuli (e.g., chemical signals, physical cues, or biomarkers) and convert them into intracellular signals amenable to processing (Ding et al., 2021). Upon target recognition, the sensing elements activate the signal transduction module through distinct triggering mechanisms: conformational changes, induced dimerization, conditional stabilization, or enzymatic reactions (Miller et al., 2022). For example, conformational changes occur when transcription factors or membrane receptors bind target molecules, inducing structural rearrangements that initiate downstream responses (Serganov and Nudler, 2013; Zhong et al., 2016); induced dimerization involves the binding of two monomeric molecules under specific conditions to form a functional dimer, thereby activating signaling pathways (Capra and Laub, 2012; Lazar and Tabor, 2021); conditional stabilization refers to molecular stability regulated by environmental parameters such as temperature (Schuster and Greenberg, 2008); and enzymatic amplification leverages enzyme-catalyzed substrate to product conversions to enhance detection sensitivity (Miller et al., 2022). Acting as the central hub, the signal transduction module bridges the input and output modules by transforming initial detection signals into processable intracellular signals while enabling amplification, integration, or logical operations. Natural bacterial systems predominantly utilize pathways such as two-component systems (TCS), in which histidine kinases (HK) recognize extracellular signals via their sensor domains, undergo autophosphorylation at histidine residues, and transfer phosphate groups to aspartate residues on response regulators (RR), thereby activating RR’s DNA-binding or enzymatic functions to regulate gene expression (Lazar and Tabor, 2021; Salis et al., 2009; Tanna et al., 2021); quorum sensing (QS), a density-dependent communication mechanism mediated by autoinducers (AIs) that accumulate to threshold concentrations to trigger QS-regulated behaviors such as bioluminescence, virulence factor production, or biofilm formation (Kumari et al., 2006; Lee et al., 2013); and chemotaxis systems, which direct bacterial motility toward nutrient-rich environments or away from harmful substances (Karmakar, 2021). The output module translates processed intracellular signals into detectable and quantifiable physical, chemical, or biological responses (Lopreside et al., 2019). Optical outputs such as fluorescence (green fluorescent protein, GFP) or bioluminescence (luciferase) rely on promoter-driven reporter gene expression, where fluorescence intensity correlates linearly with target concentration to achieve high-sensitivity detection (Belkin, 2003; Roda et al., 2011; van der Meer and Belkin, 2010). Chromogenic outputs exploit enzymatic cleavage of substrates, including X-gal hydrolysis by lacZ-encoded β-galactosidase to produce a blue chromogen (Mascher et al., 2004), enabling semi-quantitative visual or spectrophotometric analysis without specialized equipment, a feature particularly advantageous for point-of-care or resource-limited settings. Electrochemical outputs detect target-induced changes in redox reactions, ion concentrations, or charge distributions at electrode surfaces, with signals quantified through current, voltage, or impedance measurements (Johnson et al., 2017; Zhu et al., 2023). Collectively, these modular frameworks enable bacterial biosensors to address diverse biomedical challenges by balancing sensitivity, specificity, and practicality for applications spanning diagnostics, environmental monitoring, and therapeutic evaluation.

Synthetic biology holds significant potential to advance the development of bacterial biosensors. A cornerstone of this advancement lies in the strategic deployment of gene-editing tools, particularly CRISPR-Cas9 technology (Doudna and Charpentier, 2017). By enabling targeted knockout of genes responsible for non-specific responses or background interference, CRISPR-Cas9 enhances sensor specificity through noise reduction (de la Fuente-Núñez and Lu, 2017). Complementarily, gene knock-in techniques integrate functional genetic elements to amplify both sensitivity and specificity, permitting reliable detection even at ultralow target concentrations. The redesign of endogenous signaling circuits constitutes another critical strategy (Jung et al., 2018). Native bacterial signal transduction pathways, composed of receptor proteins, transcription factors, and effector proteins, are systematically engineered to maintain their intrinsic efficiency while enhancing analytical versatility (Jung et al., 2018; Raut et al., 2013; Skjoedt et al., 2016). For example, modifying receptor binding sites to accommodate structurally analogous targets expands the detectable analyte spectrum, thereby addressing diverse diagnostic needs. Synthetic genetic circuit construction further introduces novel functionalities (Sedlmayer et al., 2018; Wang et al., 2013). Modular components, including AND, OR, and NOR logic gates, enable coordinated multi-signal processing, allowing biosensors to function with high precision in complex matrices (Bonnet et al., 2013; Tang et al., 2021; Wang et al., 2011). Memory modules such as transcription factor-based toggle switches and recombinase-mediated memory circuits confer bacteria with programmable “memory storage,” recording prior exposure to specific analytes (Riglar et al., 2017). This capability facilitates accelerated, context-dependent responses upon re-exposure, which is essential for monitoring dynamic fluctuations in environmental conditions or disease biomarkers (Archer et al., 2012; Park et al., 2013). To optimize performance, circuit design integrates signal amplification coupled with feedback control mechanisms. Positive feedback loops serve as biological amplifiers (Wan et al., 2019), heightening sensitivity to low-abundance signals, whereas negative feedback mechanisms act as stabilizers, preventing signal oversaturation and preserving cellular homeostasis (Jia et al., 2019). This dual regulatory framework ensures sustained operational stability across prolonged and variable detection scenarios (Tiwari et al., 2013). Collectively, these innovations position synthetic biology-driven bacterial biosensors as robust tools for applications spanning environmental monitoring to clinical diagnostics Figure 1.

Bacterial biosensors are an innovative biotechnological tool with significant potential across various fields (Plotnikova et al., 2016). In environmental monitoring, they are used to detect organic pollutants, heavy metal ions, and other harmful substances in soil and water (Plotnikova et al., 2016). These sensors employ engineered bacteria to generate specific biological reactions to pollutants, which are then converted into measurable signals (Coelho et al., 2015). For instance, bacterial biosensors can detect organic pollutants like phenolic compounds and polycyclic aromatic hydrocarbons (Plotnikova et al., 2016), as well as heavy metals such as mercury (Chen et al., 2020), arsenic (Hui et al., 2023), and chromium (Francisco et al., 2019). Their high sensitivity and selectivity enable rapid detection and early warning of environmental pollutants. In food safety, bacterial biosensors offer a faster, more cost-effective alternative to traditional methods (Gao et al., 2023), detecting harmful substances like antibiotics, hydrogen peroxide, pesticide residues, and pathogenic bacteria (Poikulainen et al., 2020). These sensors allow for quick screening and early warning, ensuring food safety and reliability (Liao et al., 2006). Additionally, bacterial biosensors are gaining attention in the medical field for their role in diagnosing gastrointestinal diseases, cancer, and other applications Table 1. We will now explore their specific uses and potential value in these areas.

Personalized medicine aims to revolutionize healthcare by tailoring diagnostic and therapeutic strategies to individual patients through real-time, dynamic monitoring of biomarkers. For instance, IBD patients require frequent monitoring of intestinal inflammation to optimize anti-inflammatory therapies, yet current techniques cannot provide continuous, non-invasive insights into biomarker dynamics. Bacterial biosensors, engineered to detect specific molecules in situ, offer a transformative solution (Neurath, 2017). Bhatt (2018) developed ingestible microbial electronic devices (IMBED) for personalized gut monitoring. They engineered E. coli Nissle 1917 to express heme-responsive genetic circuits (Phas promoter) and the luxCDABE operon, enabling bioluminescence upon heme detection. The IMBED encapsulates bacteria in a chamber with a nanoporous membrane (pore size < 50 nm), allowing metabolite influx while preventing bacterial escape (Liu et al., 2021). A silicon photodetector converts bioluminescence into wireless signals transmitted to smartphones, enabling real-time tracking of gut biomarkers (Mimee et al., 2018). In murine models, IMBED achieved a heme detection limit of 1 μM within 30 min, demonstrating potential for personalized management of iron-related disorders (Bhatt, 2018). However, IMBED requires enteric coating to neutralize gastric acid, and its long-term reliability may be compromised by intestinal peristalsis or biofilm formation. Future iterations could integrate pH-resistant circuits and anti-fouling membranes to enhance clinical viability. Recent advances in bacterial biosensors have enabled real-time tracking of transient gastrointestinal molecules for personalized medicine. Inda-Webb et al. (2023) engineered an ingestible electronic capsule (< 1.4 cm³) integrating E. coli Nissle 1917 biosensors with silicon photodiode arrays. The bacteria were modified to express engineered sensing proteins and recombinase-based memory circuits, allowing continuous recording of oxidative stress markers. A low-power module wirelessly transmits bioluminescent signals (triggered by luxCDABE promoter) to smartphones, achieving real-time monitoring of multiple inflammation mediators, including thiosulfate, tetrathionate, hydrogen peroxide (H2O2), and nitric oxide (NO). For instance, the system detects H2O2 with a limit of 10 nM and responds within < 5 min, enabling dynamic tracking of redox imbalance during disease progression. The capsule, validated in porcine colitis models, demonstrated a direct correlation between H2O2 concentration spikes and disease flare severity, enabling data-driven adjustments to antioxidant therapies (Inda-Webb et al., 2023; Liu et al., 2023). This platform represents a closed-loop theranostic system that integrates biomarker detection, therapeutic decision-making, and treatment response monitoring, thereby advancing non-invasive and individualized approaches to gastrointestinal care. MeRALD (Engineered Modularized Receptors Activated by Ligand-induced Dimerization) platform to address critical gaps in liver disease management (Chang et al., 2018). By engineering E. coli Nissle 1917 with programmable receptors sensitive to bile acids, their system detects pathological concentrations of these hepatic biomarkers through ligand-induced dimerization mechanisms (Chang et al., 2021). When bile acids bind to the modularized TcpP18 receptor, conformational changes trigger β-galactosidase expression via a LacZ reporter, generating quantifiable colorimetric signals in fecal samples (Abdelbasset et al., 2023). This innovation demonstrated clinical utility in liver transplant monitoring, while enabling smartphone-based color analysis for home testing. Notably, the platform’s modular design allows rapid adaptation to other biomarkers through receptor reprogramming, as evidenced by its parallel success in detecting gut inflammation markers like thiosulfate in IBD patients (Sicard et al., 2014). By overcoming traditional limitations of centralized laboratory diagnostics, this work exemplifies how synthetic biology can bridge precision medicine with global health accessibility, though challenges persist in ensuring sensor stability across diverse microbiota environments.

Shi et al. (2024) developed a bacterial biosensor leveraging the skin commensal bacterium Staphylococcus epidermidis to diagnose and treat inflammatory diseases such as psoriasis (Cau et al., 2021; Severn and Horswill, 2022). The biosensor integrates S. epidermidis within a dual-network hydrogel matrix composed of gelatin and tapioca starch, mimicking natural biofilm structures to sustain bacterial viability for over 4 days (Xu et al., 2021). Bioinspired double network hydrogels: from covalent double network hydrogels via hybrid double network hydrogels to physical double network hydrogels. This living hydrogel not only adheres conformally to skin but also modulates the immune microenvironment by downregulating pro-inflammatory cytokines (e.g., IL-17, TNF-α) and reducing T-cell infiltration, addressing the root cause of psoriasis. The bacteria’s innate ability to regulate skin homeostasis was enhanced through electrostatic interactions with the conductive polymer, which lowered charge transfer resistance, optimizing electron transfer for real-time monitoring of skin impedance, temperature, and humidity (Zhang P. et al., 2023). A wireless bioelectronic interface enabled on-demand electrical stimulation to control bacterial activity, ensuring biosafety by disinfecting pathogens (e.g., S. aureus) via reactive oxygen species generation. The biosensor demonstrated therapeutic efficacy in a psoriasis mouse model, reducing epidermal hyperplasia by 60% and restoring skin microbiota diversity without genetic modification. By synergizing bacterial immunomodulation with bioelectronic sensing, this platform exemplifies the potential of living bacterial systems in precision medicine, offering a drug-free approach to inflammation management while minimizing biohazard risks (Shi et al., 2024). Wang et al. (2022) developed a biosensor based on the electroactive bacterium Shewanella oneidensis MR-1, which demonstrates significant potential in medical diagnostics. This system utilizes the bacterium’s intrinsic lactate oxidation capability, where electrons generated from metabolic activity are transferred via outer-membrane cytochrome complexes (e.g., MtrCAB) and riboflavin-mediated extracellular electron transport (EET), enabling label-free lactate detection without genetic engineering (Gurdeep et al., 2021). Electrostatic integration of the conductive polymer poly PMNT enhanced biofilm formation and reduced charge transfer resistance from 226 to 12 Ω, achieving a lactate detection limit of 78 μM in physiological fluids (sweat, urine, plasma). By exploiting the Warburg effect—a hallmark of cancer metabolism characterized by excessive lactate secretion—the biosensor indirectly quantified HeLa, MCF-7, and A549 cancer cells with a sensitivity of 2.9 × 10⁴ cells and an error rate < 10% (Wang et al., 2022). Integration with a flexible wearable platform and wireless signal transmission highlights its clinical applicability for real-time monitoring. Future engineering of S. oneidensis to recognize diverse biomarkers (e.g., inflammatory cytokines or pathogens) could expand the utility of living bacterial sensors in precision medicine (Li et al., 2021).

Bacterial biosensors exhibit distinct performance advantages over conventional diagnostic technologies, enabled by synthetic biology-driven detection of ultralow-concentration biomarkers in complex biological matrices (Hicks et al., 2020). These systems achieve real-time/near-real-time monitoring of dynamic analyte fluctuations and support multiplexed detection through engineered genetic circuits (Hicks et al., 2020; Table 2). Notably, advanced prototypes integrate diagnostic and therapeutic capabilities, forming closed-loop theranostic systems with feedback-controlled intervention (Jin et al., 2025). However, their medical application faces three primary challenges: functional stability, biosafety, and clinical translation (Riglar and Silver, 2018). Engineered bacteria withstand extreme host environments that involve immune attacks, resource competition with commensal microbiota, and fluctuating physiological conditions, all of which collectively destabilize sensor functionality (Dana et al., 2012). While synthetic genetic circuits provide novel sensing capabilities, they disrupt native metabolic balance, triggering compensatory mutations such as fluorescence reporter inactivation in engineered E. coli after serial passages and increasing susceptibility to endogenous signal interference (Sleight and Sauro, 2013; Sleight et al., 2010). Biosafety concerns remain unresolved as conventional suicide switches show limited effectiveness in complex human microenvironments, particularly in intestinal hypoxia zones where occasional bacterial escape may occur (Piraner et al., 2017). Physical encapsulation strategies, though effective in restricting microbial spread, reduce detection sensitivity due to molecular permeability barriers caused by suboptimal material interfaces (Hirota et al., 2017). Clinical translation is further challenged by the lack of standardized validation protocols, rigorous requirements for classifying these systems as “live medical devices” necessitating extensive virulence testing, and difficulties in assessing unpredictable long-term ecological risks. Emerging nanotechnology integration addresses existing limitations through bacterial-nanomaterial co-immobilization strategies. Nanostructure fixation enhances sensitivity by extending cellular electron transfer distances while biomimetic nanocapsules with dynamically tunable pores enable immune evasion and selective molecular permeation (Liu et al., 2024). Concurrently, artificial intelligence-driven platforms revolutionize biosensor development as deep learning models deciphering gene sequence-function correlations enable precise prediction of genetic editing outcomes (Angenent-Mari et al., 2020; Kim et al., 2020). These advancements are combined with microfluidic high-throughput screening to establish Design-Build-Test-Learn (DBTL) closed-loop optimization systems (Berlanda et al., 2021; Orsi et al., 2021). Dynamic evolution systems that simulate in vivo pressures accelerate directed bacterial adaptation in microfluidic chips, significantly reducing development timelines. Enhanced biosafety protocols incorporate dual-lock containment mechanisms to reduce escape probability alongside chassis genome minimization strategies for metabolic stability. Regulatory frameworks are evolving through multicenter standardized testing platforms and refined classifications of “live medical device” based on colonization capacity. Researchers are integrating bacterial biosensors with mobile health platforms like smartphones and cloud systems, enabling continuous physiological tracking and remote care delivery via real-time wireless networks (Chang et al., 2017; Raut et al., 2012). While most systems remain experimental, retrospective studies confirm their diagnostic potential in chronic disease management and cancer biomarker detection. Transitioning to clinical practice requires multicenter trials validating sensor stability and specificity in diverse patient populations. Successful validation could translate prototypes into standardized diagnostic modules for healthcare integration. This technological shift promises to enhance detection of complex biomarkers while supporting adaptive treatment protocols through persistent health data streams, ultimately advancing intelligent closed-loop diagnostic-therapeutic systems.