Gabrielle David
Yizhi Cai- Manchester Institute of Biotechnology, University of Manchester, Manchester, United Kingdom
Biosensors harness biological components to detect and report on specific analytes, offering crucial insights across medicine, environmental monitoring, agriculture, and food security. The convergence of synthetic biology and laboratory-on-a-chip (LoC) technologies is enabling a new generation of biosensors that are programmable, modular, and field-deployable. Synthetic biology provides engineered sensing elements, including bespoke proteins, aptamers, and genetic circuits, that expand the range of detectable analytes while offering tunable sensitivity, specificity, and dynamic range. LoC platforms, in turn, miniaturize fluid handling and analytical processes into integrated microfluidic devices, creating controlled environments that enhance biosensor performance, portability, and biocontainment. Together, these approaches address long-standing barriers in biosensing by coupling biological programmability with physical precision. This review surveys the applications of synthetic biology-LoC integration, from healthcare and environmental monitoring to emerging frontiers, including biocomputing and deep sea exploration. With rapidly accelerating innovation, the potential of these devices can be realized, reshaping the way we diagnose disease, safeguard ecosystems, manage food supplies, and explore new frontiers.
Introduction
For millennia, humans have looked toward biological organisms for information about the world around us, advising agricultural practices, indicating waterway health, and alerting to the presence of deadly diseases. By transforming otherwise invisible inputs into perceivable outputs, in the form of increased abundance, altered population dynamics, and, in the case of the canary, behavioral changes, these “sentinel organisms” provide important and potentially lifesaving information to the observer. Scientific advancement has allowed this incredibly useful function of the natural world to be captured and refined in analytical devices known as “biosensors.” Biosensors combine a biological sensing element with a transducer to detect and alert to the presence of a specific biological or chemical analyte. The biological sensing element is often similar to those native to microbial sentinel species, such as enzymes or promoters, which interact with a specific target to produce a measurable signal that is converted into a readable output by the associated transducer. Possible outputs include electrical, optical, and thermal signals, which can often be further analyzed downstream for an in-depth look at analyte concentration.
Since the breakthrough development of the glucose biosensor by Clark and Lyons (1962), biosensors have been continually studied and refined, leading to a current state in which biosensors exist for a myriad of applications. The past decades have shown a shift from predominantly biomedical applications, such as glucose monitoring, toward the inclusion of environmental monitoring of pollution, namely, heavy metals such as arsenic and copper, and a range of other fields, including agriculture, food quality, medical diagnosis, industrial manufacturing, and security, owing to their promised improvements to specificity, speed, cost, and ease of use compared with traditional sensing and monitoring technologies (Nikoleli et al., 2016; Chen et al., 2019; Wang et al., 2022; Tanniche and Behkam, 2023; Fu et al., 2024; Gangopadhyay et al., 2024; Gao et al., 2025; Sajeevan et al., 2025).
The modularity of many biosensing devices strongly aligns with the systematic “Design–Test–Build–Learn” (DTBL) approach of synthetic biology. By applying this DTBL principle to biosensor design, novel biosensors can be designed and standardized in a streamlined pipeline, minimizing the period between target identification and biosensor deployment, a concept especially attractive in biomedical applications. Standardized parts can also be modulated using synthetic biology techniques to customize sensitivity, specificity, and dynamic range (Canton et al., 2008; Arpino et al., 2013). In addition to this refined approach, synthetic biology techniques open a range of potential targets not possible with traditional biosensors by facilitating the design and production of bespoke proteins and aptamers to bind analytes with no known natural sensors (Cai et al., 2023; Cui et al., 2025; Zhou et al., 2025). This improved control over sensing elements also enables the creation of complex genetic circuits, using computer programming concepts to facilitate the detection and even multiplexed recording of analog signals over time (Sheth and Wang, 2018; Wan et al., 2021).
Unlocking the potential of synthetic biology-enabled biosensors often raises safety concerns for field applications, especially for whole-cell biosensors. The risks of horizontal gene transfer and disruption of natural ecosystems are key challenges faced by other genetically modified organisms in the past and are not only a scientific barrier, but also a social and political barrier to more widespread use beyond research applications. Genetic safeguarding circuits using a variety of mechanisms, such as kill switches and non-canonical amino acids, have been developed. However, these methods are still subject to the continuous threat of evolution, which may allow cells to survive beyond their engineered boundaries (Cai et al., 2015; Agmon et al., 2017). Therefore, devices intended for use in the field, or as point-of-care (POC) diagnostic tools, require robust biocontainment mechanisms beyond genetic safeguards. Laboratory-on-a-chip (LoC) technologies provide an excellent solution to this issue, not only providing a controlled physical environment in which biological components can interact predictably and efficiently but also adding a layer of biosecurity to prevent their release.
LoC devices commonly use microfluidics to integrate one or more laboratory processes on a single “chip” device (Gorjikhah et al., 2016). Microfluidics-based systems utilize microscopic channels to handle minute volumes of fluids, which are integrated with a range of modules wherein samples are processed, and a signal is detected (Chircov et al., 2020). Within these modules, biological elements (such as whole cells, cell-free systems, and proteins) immobilized on the surface of microchannels or within chambers detect a signal and produce a measurable response (fluorescence, enzyme activity, or ligand binding). Miniature transducers convert the biological recognition event into a measurable signal, typically optical, electrochemical, or electrical, which can either be read directly (e.g., color change) or amplified to provide a digital output to on-chip microcontrollers or an external reader (Figure 1A) (Nguyen et al., 2018). Technical details, including integration of synthetic biological components, signal transduction modalities, and chip architecture, are discussed thoroughly in recent reviews (Chircov et al., 2020; Baranwal and Maerkl, 2024; Leal-Alves et al., 2024; Sekhwama et al., 2024). Shrinking a range of laboratory analyses that would normally require bulky, expensive equipment into an easily transportable, user-friendly format unlocks an array of applications not accessible to traditional biosensing methods. Biosensors embedded in LoCs can be deployed in the field, in the case of environmental applications, or at the point of care for medical diagnostics (Yang et al., 2022; Aryal et al., 2024).
Figure 1. (A) Conceptual schematic of a synthetic biology-based LoC biosensor showing sample input, processing, biosensing, signal transduction, and output modules. (B) Application areas for synthetic biology–enabled lab-on-a-chip biosensing.
This reduced scale also offers additional advantages, including a minimal sample requirement, rapidity, increased throughput, and the capacity for integrated sample processing (Table 1). LoC functions such as on-chip filtration, selective chemical separation, and microchamber confinement create defined reaction environments that can protect whole-cell and cell-free biosensors from inhibitory compounds, standardize sample conditions, and enhance reliability and sensitivity, even in complex, real-world samples (Xia and Li, 2023; Baranwal and Maerkl, 2024; Siavashy et al., 2024). In addition, this compartmentalized architecture naturally supports multiplexed biosensing. Defined reaction zones can host distinct sensing chemistries (e.g., barcoded aptamers and orthogonal CRISPR guide libraries) on a single device, enabling richer multi-analyte detection while maintaining minimal sample consumption (Avaro and Santiago, 2023; Wu et al., 2025; Zhang et al., 2025). Already-useful tools in biosensing applications, the combination of LoC with synthetic biology is a hugely beneficial prospect, bringing together the strengths of each discipline to generate more specific, efficient, and convenient biosensors that can be deployed in a range of fields (Figure 1B). This review will explore the variety of applications for these intersected specialties and future avenues for advancement and expansion.
Table 1. Comparison of whole-cell biosensors, cell-free biosensors, and Lab-on-a-Chip (LoC)–integrated synthetic biology systems across major challenges in biosensor deployment.
Applications
Healthcare
From the inception of the first biosensors, medical applications have remained at the forefront of biosensor research, owing to their innate biocompatibility with associated target analytes. This focus continues to dominate current biosensor research, incorporating novel tools and techniques to target a wider range of targets with increased specificity.
Engineered cells, cell-free systems, or synthetic gene circuits can sense clinically relevant biomarkers, pathogens, or metabolic states with high specificity. For instance, paper-based toehold switch RNA biosensors embedded on acrylic chips with electronic sensors have been used to detect Zika and SARS-CoV-2 viruses with portable readouts, offering low-cost solutions for use in resource-limited settings (Pardee et al., 2016). Similarly, whole-cell biosensors integrated into microfluidic platforms have been designed to detect quorum-sensing molecules indicative of bacterial infections or to monitor drug toxicity in real time (Tanniche and Behkam, 2023; Zhao et al., 2023). By miniaturizing and multiplexing these synthetic biology systems, LoC devices enable point-of-care testing that is rapid, scalable, and easily adapted to emerging health threats.
MicroRNAs (miRNAs), short (18–25 nucleotide) RNA sequences involved in gene regulation, have been growing in popularity as key biomarkers for multiple diseases, including cancer, dementia, and cardiovascular conditions. Tests to assess these emerging biomarkers must be highly sensitive and selective, which makes current methods cost-, labor-, and equipment-intensive (Adampourezare et al., 2022). To overcome these challenges and improve the accessibility of miRNA analysis, microfluidics can be applied alongside highly selective CRISPR technology (Adampourezare et al., 2022; Razavi et al., 2024). The Cas13 effector is of particular interest for RNA detection and quantification via promiscuous cleavage of nearby non-target RNAs. By incorporating CRISPR/Cas13 in a microfluidic electrochemical biosensor, Bruch et al. (2019) exploited this feature to quantify potential tumor markers, microRNAs miR-19 and miR-20a. Given the relatively simple modifications required to target additional miRNAs, this system offers a highly flexible detection method for a range of diseases and infectious agents. Using a variety of Cas effectors, microfluidic POC devices for the detection of SARS-CoV-2, Ebola, HIV, and other viruses have also been developed, and the scope for additional sensors is wide (Razavi et al., 2024).
Beyond diagnosis, biosensors are an ideal format for continuous monitoring of patient condition. Wearable biosensors, enabled in large part by LoC technology, have garnered significant attention in recent years, ranging from “smart eyeglasses” to biomonitoring tattoos. By integrating live biosensing cells in hydrogel-elastomer hybrid materials, Liu et al. (2017) developed a living fabric that can be integrated into gloves and skin patches to continually monitor and respond to a range of chemicals. Moving beyond whole-cell biosensors, researchers have also engineered pH-responsive wearable biosensors based on engineered proteins, which are integrated in a wearable textile with a fluorescence output (Saldanha et al., 2020). Given the flexibility of these designs, further biosensors could be incorporated into similar materials for a vast range of applications, including personalized healthcare and disease surveillance. For example, “smart” hospital gowns or face masks could monitor the spread of clinically relevant agents and pathogens within hospitals, such as MRSA, in order to minimize risk and guide interventions (Liang et al., 2025).
Environmental monitoring
Environmental, economic, and political changes are amplifying the urgent need for inexpensive and robust methods for detecting environmental hazards, such as water contamination, agricultural runoff, and air pollution, in remote or underserved areas. To this goal, synthetic biology has been used to develop new methods of monitoring a range of environmental pollutants, including heavy metals, phenols, and pesticides (Chong and Ching, 2016; Roy et al., 2021; Somayaji et al., 2022; Wang et al., 2022; Sun et al., 2023). Whole-cell biosensors engineered with pollutant-responsive promoters and transcription factors, such as the arsR system for arsenic detection, have been embedded into microfluidic devices to detect trace contaminants in water with minimal user input and high signal resolution (Buffi et al., 2011a; Buffi et al., 2011b). Airborne biosensors, such as Lu et al.’s UAV-mounted whole-cell biosensing system, can monitor air quality via the natural fluctuations in Escherichia coli cell culture dynamics. Such a system could be readily integrated with engineered WCBs to monitor multiple specific pollutants in tandem (Lu et al., 2015). These systems offer advantages in portability, response time, and cost, enabling broader environmental surveillance without dependence on centralized laboratories.
Polyfluoroalkyl (PFAS) substances have been of particular concern in recent years, as a major contaminant of waterways (Ackerman Grunfeld et al., 2024). Abundant, diverse, and resistant to degradation, these present a major threat that requires fast, sensitive, and reliable methods of detection as even low amounts have been shown to have detrimental environmental and health effects. Current detection methods are highly specialized and time consuming, vastly limiting the availability of these tests in less affluent or rural areas (Zahra et al., 2025). The flexibility of biosensors offers a unique approach to this challenge. Mann et al. (2022) developed an engineered protein biosensor to detect these substances in water samples and report via a fluorescent signal. These proteins can be further engineered to alter their ligand specificity and sensitivity, placing complex analytical tasks on the biosensors, minimizing the skill level required to use such devices (Rottinghaus et al., 2022).
Other environmental biosensors utilize highly regulated transcription factors to detect their target, such as the biosensing system developed by Roy et al. Aromatic hydrocarbons are priority pollutants, according to the Environmental Protection Agency (EPA), due to their toxicity to human and environmental health. However, analysis methods are restricted to laboratory-based analysis. This multiplexed whole-cell biosensing array detects aromatic pollutants at extremely low concentrations using mutated MopR proteins that bind a downstream reporter gene, luciferase (Roy et al., 2021). The MopR protein itself has already been integrated into functional in vitro biosensing chips, removing the need for host cells and increasing shelf stability (Ray et al., 2018).
On-site sensing without the biocontainment and maintenance concerns associated with living cells can also be achieved using cell-free biosensing (Pardee et al., 2016). Systems such as the RNA Output Sensors Activated by Ligand Induction (ROSALIND) system are particularly attractive for their flexibility and multiplexing potential. Deployed within a 3D-printed handheld device, ROSALIND’s cell-free circuitry regulates the synthesis of a fluorescence-activating RNA aptamer in response to a range of water contaminants inaccessible to whole-cell sensing, such as antibiotics and other contaminants incompatible with whole-cell sensing (Jung et al., 2020).
Integration of environmental biosensors into LoC devices may enable wider distribution and more biosecure, practical deployment of these highly specific biosensing tools, at a reduced cost, thanks to the minimized requirement for specialist equipment and training. Portable, multiplexed chips to assess waterways for these chemicals might allow for a broader assessment of worldwide contamination, to guide safe water use and future remediation efforts.
Food security
In the face of increasing populations and an uncertain future for our current food production systems, intensive agriculture is rapidly expanding, and output is becoming the prime focus of the food production chain. This presents an urgent need for sustainable and efficient methods to monitor these outputs and ensure that safety and quality are maintained alongside increased volume. In a similar fashion to those designed for environmental monitoring purposes, synthetic biology has been applied to provide real-time, on-site detection of pathogens, toxins, and contaminants throughout the agricultural supply chain, from soil to consumer (Velusamy et al., 2010; Huang et al., 2021; Wang et al., 2022; Gangopadhyay et al., 2024). These sensors can monitor microbial loads, pesticide residues, nutrient levels, and spoilage indicators, enabling precision agriculture practices and reducing post-harvest losses (Gangopadhyay et al., 2024). For example, cell-free biosensors have been developed to detect atrazine, a commonly used herbicide, in water samples. This and similar systems could be deployed on a multiplexed paper-based system for easy detection of contaminants (Silverman et al., 2020). Integration of biosensing platforms with Internet-of-Things (IoT) technologies also supports smart farming approaches, where automated data collection and response systems enhance yield and resource management (Rajak et al., 2023).
Plant pathogens are a particular threat to global food security, and with increasing global temperatures, disease containment is becoming increasingly challenging. In regions where such diseases are a major challenge, farmers are unable to access the equipment and training required to diagnose and treat these diseases (Singh et al., 2023). Engineered biosensors such as the CRISPR/Cas miRNA biosensors developed for human disease could be applied to provide a rapid, reliable, and easy-to-use diagnostic tool that farmers can use directly to detect disease early and take appropriate action to protect yields and prevent disease spread. Designed specifically for plant disease detection, Plant-Dx is a microfluidic diagnostic platform that integrates isothermal DNA amplification with modular synthetic RNA-based genetic circuits, specifically small transcription-activating RNAs (STARs). These engineered regulators enable programmable detection of pathogen-specific sequences, producing a visible readout in under an hour (Verosloff et al., 2019).
Once on the shelves, there are still unknown variables surrounding food safety, such as the duration food will maintain its freshness and any potential contamination with pathogens. This can lead to excessive food waste as food is disposed of before its time due to overcaution, and in more serious cases, infection of consumers due to undetected contaminants. Continuous monitoring using biosensors could provide a useful method of combating this. Selim et al. (2022) designed a paper-based microfluidic device that uses cell-free protein synthesis to detect putrescine, a biomarker for microbial deterioration, in beef. Portable, affordable, and biodegradable, similar paper-based biosensing devices could be integrated into food packaging to monitor food safety more precisely, eliminating unnecessary food waste and avoiding the sale and consumption of unsafe products. “Smart” packaging could be adapted to detect a range of biomarkers and pathogens, as demonstrated by Yousefi et al. (2018), who developed “Sentinel wraps,” food packaging that produces a fluorescence signal upon detection of a target bacterium. DNAzymes, synthetic, single-stranded DNA molecules with catalytic capabilities, printed on a cyclo-olefin film successfully detected E. coli in samples of meat and apple juice at low concentrations, providing a low-cost, highly sensitive, and shelf-stable method for real-time food safety monitoring (Schlosser and Li, 2009; Yousefi et al., 2018). Additionally, DNAzymes specific to further microbial targets can be swiftly isolated using in vitro selection and directed evolution, allowing for the detection of a wide array of food-borne pathogens (Schlosser and Li, 2009; Rothenbroker et al., 2019; Bhuyan et al., 2024).
Emerging frontiers
Beyond current applications, synthetic biology and LoC technologies are being explored for their potential in many other areas, notably the development of further LoC and biosensing devices, biocomputing, and space research.
Biosensor and microfluidic design
LoC biosensors have also been designed to facilitate the production of improved LoC and biosensing devices. Varma and Voldman (2015) demonstrated the function of a whole-cell biosensor that fluoresces in response to fluid shear stress imposed upon cells when handled in microfluidics systems. These genetically encoded sensors can be used to inform microfluidic system design to minimize adverse effects on cell health, for biosensor design and beyond. This idea was explored further by the researchers, resulting in cell-based biosensors responsive to both shear stress and to DNA damage and heat shock to further optimize cell health in synthetic microenvironments (Varma et al., 2017). While research in this area is limited, these studies highlight an underutilized opportunity: by incorporating sensors to not only track the signal of interest but also the cells themselves, there is a chance to gather information to improve LoC housing and genetic circuit design for improved cell-line stability (Sleight and Sauro, 2013).
Biocomputing
Through advances in synthetic biology, biocomputing uses biological components, including biosensor-like genetic circuits, to perform computer-like logic processes in artificial neural networks capable of pattern recognition and responsive decision making (Solanki et al., 2023; Liu et al., 2024). This capability not only represents a promising alternative to current energy-intensive AI models but also provides a previously inaccessible level of nuance to biosensing applications. Although still in early stages, these systems could lead to biosensors that autonomously interpret complex inputs without specialist analysis, storing information in the extremely efficient medium of DNA (Sheth and Wang, 2018; Doricchi et al., 2022).
Such technologies have already been explored in medical diagnostics. Researchers applied an automated DNA computing-based system to discriminate between bacterial and viral acute respiratory illnesses using mRNA expression patterns in patient blood samples, to inform responsible antibiotic use. This system successfully diagnosed ARI etiology in 4 h, demonstrating rapid diagnostic capabilities; however, the multistep process integrates an RNA extractor, a mechanical sample loading machine, a thermocycler, and a fluorescence reader. While the process has been fully automated, this equipment requirement may be limiting for most POC applications. However, by integrating these processes with existing laboratory-on-a-chip methods, such as accelerated PCR and fluorescence imaging, it may be possible to scale the entire diagnosis to a single chip (Novak et al., 2007; Houssin et al., 2016; Sano et al., 2022). Similar methods have been designed to detect a range of pathological biomarkers, including those related to diabetes and cancer, and integration with microfluidic technology is a promising avenue to inexpensive, accurate, and rapid diagnosis and analysis of complex conditions (Courbet et al., 2015; Farazmand et al., 2019; Jaekel et al., 2021).
Extreme environments
Biosensors are increasingly being adapted for monitoring microbial activity, radiation, chemical contaminants, and environmental conditions relevant to extreme environments, including polar regions, deep sea vents, high-radiation zones, and other resource-constrained settings. Miniaturized and autonomous LoC systems are particularly well-suited for these applications, enabling stable, scalable, and autonomous long-term monitoring where traditional laboratory equipment is impractical.
In the context of space exploration and astrobiological research, synthetic biology offers low-mass, self-replicating, and resource-efficient alternatives for tasks including health monitoring, life support, and environmental sensing (Roda et al., 2018). LoC biosensors such as SporeSat, EcAMSat, and displacement-induced off-on (DIDO) fluorescent biosensors enable the study of microbial activity, metabolism, and antibiotic resistance under microgravity while minimizing power and reagent requirements (Crabbé et al., 2010; Zea et al., 2017; Vashi et al., 2022). These systems support astronaut health by enabling rapid, low-energy detection of radiation damage or contamination and allow for the pre-screening of biological payloads before deployment. Though synthetic biology has yet to be widely applied in deployed space missions, ongoing research suggests strong potential for engineered biosensors to play central roles in future space exploration and habitation.
LoC for deep sea exploration is already an emerging field, with colorimetric analyzers being deployed to measure nitrate and phosphate levels in situ (Beaton et al., 2022). Incorporation of biologically enhanced biosensors could expand the sensing capacity of these tools, for example, using riboswitches targeting eDNA to assess the marine life population in locations of interest. Artificial cells containing PURE cell-free synthesis machinery have already been exhibited to function under deep sea conditions, withstanding the high pressure and low temperatures of these environments (Kuruma et al., 2024). Existing marine biosensors may be adapted to this platform, providing an inexpensive method of exploring this largely unknown and increasingly threatened ecosystem.
Research in other extreme environments has so far been limited. However, there is clear potential for the application of LoC biosensors in a similar capacity to those demonstrated in space and, to a limited degree, deep sea exploration. Natural extremophilic organisms offer a wealth of biological components that are naturally resistant to environmental extremes and might be exploited to develop novel tools for use in otherwise inhospitable and resource-limited environments, including polar regions, high-radiation zones, and deserts (Giordano, 2020).
Next steps
Looking ahead, translating synthetic biology-enabled LoC biosensors into practical devices depends heavily on advances in manufacturing and fabrication techniques. Recent work on paper-based microfluidic platforms (µPADs) suggests that fabrication methods such as hydrophobic patterning and inkjet or wax printing could, with further refinement, offer a route to scalable, cost-effective POC device manufacturing (Anushka et al., 2023). Additive manufacturing (3D printing) is also evolving as a powerful tool for producing complex microfluidic architectures with embedded channels, valves, and integrated electronics, offering rapid prototyping and potentially more reproducible manufacturing workflows. The expanding range of materials available for 3D printing, such as bioinks, conductive polymers, nanocomposites, and stretchable substrates, provides increasing versatility and enables greater functional integration (Paul et al., 2024; Saha et al., 2024). Importantly, these fabrication advances also allow more precise control of microchannel geometry and surface chemistry, which in turn supports improved on-chip handling of real-world samples without shifting complexity onto the user (Tang et al., 2022; Ashok et al., 2025).
The construction of synthetic biological circuits also requires further advancement. Despite increasing interest in modular genetic engineering, standardization of biological parts remains limited, and laboratory workflows are often slow and labor-intensive (Buecherl and Myers, 2022). Recent progress is beginning to alleviate these bottlenecks; standards such as the Synthetic Biology Open Language (SBOL) enable consistent, machine-readable descriptions of part behavior; machine-learning-driven automation can optimize DBTL workflows in real time; and advances in enzymatic DNA synthesis are lowering turnaround times while improving the reliability of larger, more complex constructs (McLaughlin et al., 2020; Buecherl and Myers, 2022; Hoose et al., 2023; Kim et al., 2025). Together, these developments indicate a gradual shift toward more predictable, scalable, and accessible circuit-engineering pipelines.
Regulatory uncertainty presents an additional major barrier to deployment. While LoC presents an attractive solution to prevent the uncontrolled escape of whole-cell biosensors, current regulatory frameworks (such as the EU’s contained-use and deliberate-release directives (European Union, 2001; European Union, 2009) and the US EPA’s Toxic Substances Control Act (TSCA) biotechnology rules (40 CFR Part 725; EPA, 1997)) are based on conventional GMO use and do not encompass physically contained, field-deployable devices with multilayer biocontainment strategies (e.g., kill switches and nutritional dependencies). Cell-free systems are a particular blind spot. Having only emerged into prominence in recent years, there are little to no regulatory guidelines associated with cell-free systems, leading to fragmented and unclear approval processes (Sundaram et al., 2023; Robinson and Nadal, 2025). Together, these issues underscore the need for updated and flexible regulation that accounts for continuing innovation in biocontainment, in addition to improved consistency across regulatory bodies, both within regions and internationally. Despite challenges, progress is being made. International bodies (e.g., Organization for Economic Co-operation and Development (OECD) and National Academies of Sciences, Engineering, and Medicine (NASEM)) are encouraging risk-proportionate, application-specific frameworks that recognize recent advances in biocontainment as legitimate safety measures, and several regions are already adopting more flexible, risk-based biotechnology oversight models, particularly in agriculture (National Academies of Sciences, Engineering, and Medicine, 2017; European Commission, 2024; Fernández Ríos et al., 2025; Keiper and Atanassova, 2025; Robinson and Nadal, 2025). Collectively, these trends offer promising signs of more coherent, adaptive approval pathways. Further shifts will be necessary to fully account for continuing innovation in the synthetic biology sector.
Conclusion
The convergence of synthetic biology and LoC technologies holds substantial promise for next-generation biosensing. By merging the programmability and modularity of synthetic biology with the precision and portability of microfluidics, researchers can build biosensing platforms that are not only more sensitive and selective but also field deployable, user friendly, and cost effective.
Widespread deployment will depend on overcoming technical and regulatory barriers. Future work should prioritize improving circuit predictability and scaling chip fabrication. Emerging AI- and data-science approaches could play an important role in these goals by optimizing circuit design and synthesis, guiding microfluidic control, and supporting the interpretation of multiplexed outputs. Paired with stronger evidence from real-world matrices and field trials, these advances could, in time, support regulatory modernization.
Looking ahead, the fusion of synthetic biology and LoC technologies appears well positioned to enable autonomous decision making, adaptive diagnostics, and real-time responses. With continued innovation, these hybrid technologies may reshape how we diagnose disease, protect our environment, and sustain life on—and potentially beyond—Earth.
Author contributions
GD: Conceptualization, Investigation, Visualization, Writing – original draft, Writing – review and editing. YC: Conceptualization, Funding acquisition, Resources, Supervision, Writing – original draft, Writing – review and editing.
Funding
The author(s) declared that financial support was received for this work and/or its publication. GD was supported by a Biotechnology and Biosciences Research Council (BBSRC) Doctoral Training Partnership studentship [grant number DTP3 2020-2025 entry—BB/T008725/1]. This work was also supported by an Engineering and Physical Sciences Research Council (EPSRC) Fellowship EP/V05967X/1 and a European Research Council (ERC) Consolidator Award EP/Y024753/1 to YC.
Conflict of interest
The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Generative AI statement
The author(s) declared that generative AI was not used in the creation of this manuscript.
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References
Ackerman Grunfeld, D., Gilbert, D., Hou, J., Jones, A. M., Lee, M. J., Kibbey, T. C. G., et al. (2024). Underestimated burden of per- and polyfluoroalkyl substances in global surface waters and groundwaters. Nat. Geosci. 17, 340–346. doi:10.1038/s41561-024-01402-8
Adampourezare, M., Hasanzadeh, M., and Seidi, F. (2022). Microfluidic assisted recognition of miRNAs towards point-of-care diagnosis: technical and analytical overview towards biosensing of short stranded single non-coding oligonucleotides. Biomed. Pharmacother. 153, 113365. doi:10.1016/j.biopha.2022.113365
Agmon, N., Tang, Z., Yang, K., Sutter, B., Ikushima, S., Cai, Y., et al. (2017). Low escape-rate genome safeguards with minimal molecular perturbation of Saccharomyces cerevisiae. Proc. Natl. Acad. Sci. U. S. A. 114, E1470–E1479. doi:10.1073/pnas.1621250114
Anushka, A., Bandopadhyay, A., and Das, P. K. (2023). Paper based microfluidic devices: a review of fabrication techniques and applications. Eur. Phys. J. Spec. Top. 232, 781–815. doi:10.1140/epjs/s11734-022-00727-y
Arpino, J. A. J., Hancock, E. J., Anderson, J., Barahona, M., Stan, G.-B. V., Papachristodoulou, A., et al. (2013). Tuning the dials of synthetic biology. Microbiology 159, 1236–1253. doi:10.1099/mic.0.067975-0
Aryal, P., Hefner, C., Martinez, B., and Henry, C. S. (2024). Microfluidics in environmental analysis: advancements, challenges, and future prospects for rapid and efficient monitoring. Lab. Chip 24, 1175–1206. doi:10.1039/D3LC00871A
Ashok, D., Singh, J., Howard, H. R., Cottam, S., Waterhouse, A., and Bilek, M. M. M. (2025). Interfacial engineering for biomolecule immobilisation in microfluidic devices. Biomaterials 316, 123014. doi:10.1016/j.biomaterials.2024.123014
Avaro, A. S., and Santiago, J. G. (2023). A critical review of microfluidic systems for CRISPR assays. Lab. Chip 23, 938–963. doi:10.1039/D2LC00852A
Baranwal, A. K., and Maerkl, S. J. (2024). A comprehensive review of microfluidic approaches in cell-free synthetic biology. Front. Synth. Biol. 2, 1397533. doi:10.3389/fsybi.2024.1397533
Beaton, A. D., Schaap, A. M., Pascal, R., Hanz, R., Martincic, U., Cardwell, C. L., et al. (2022). Lab-on-Chip for in situ analysis of nutrients in the deep sea. ACS Sens. 7, 89–98. doi:10.1021/acssensors.1c01685
Bhuyan, S. K., He, W., Cui, J., and Tanner, J. A. (2024). Directed evolution of peroxidase DNAzymes by a function-based approach. Biol. Methods Protoc. 10, bpae088. doi:10.1093/biomethods/bpae088
Bruch, R., Baaske, J., Chatelle, C., Meirich, M., Madlener, S., Weber, W., et al. (2019). CRISPR/Cas13a-Powered electrochemical microfluidic biosensor for nucleic acid amplification-free miRNA diagnostics. Adv. Mater. 31, 1905311. doi:10.1002/adma.201905311
Buecherl, L., and Myers, C. J. (2022). Engineering genetic circuits: advancements in genetic design automation tools and standards for synthetic biology. Curr. Opin. Microbiol. 68, 102155. doi:10.1016/j.mib.2022.102155
Buffi, N., Merulla, D., Beutier, J., Barbaud, F., Beggah, S., Van Lintel, H., et al. (2011a). Development of a microfluidics biosensor for agarose-bead immobilized Escherichia coli bioreporter cells for arsenite detection in aqueous samples. Lab. Chip 11, 2369–2377. doi:10.1039/c1lc20274j
Buffi, N., Merulla, D., Beutier, J., Barbaud, F., Beggah, S., Van Lintel, H., et al. (2011b). Miniaturized bacterial biosensor system for arsenic detection holds great promise for making integrated measurement device. Bioeng. Bugs 2, 296–298. doi:10.4161/bbug.2.5.17236
Cai, Y., Agmon, N., Choi, W. J., Ubide, A., Stracquadanio, G., Caravelli, K., et al. (2015). Intrinsic biocontainment: multiplex genome safeguards combine transcriptional and recombinational control of essential yeast genes. Proc. Natl. Acad. Sci. U. S. A. 112, 1803–1808. doi:10.1073/pnas.1424704112
Cai, R., Chen, X., Zhang, Y., Wang, X., and Zhou, N. (2023). Systematic bio-fabrication of aptamers and their applications in engineering biology. Syst. Microbiol. Biomanufacturing 3, 223–245. doi:10.1007/s43393-022-00140-5
Canton, B., Labno, A., and Endy, D. (2008). Refinement and standardization of synthetic biological parts and devices. Nat. Biotechnol. 26, 787–793. doi:10.1038/nbt1413
Chen, S.-Y., Wei, W., Yin, B.-C., Tong, Y., Lu, J., and Ye, B.-C. (2019). Development of a highly sensitive whole-cell biosensor for arsenite detection through engineered promoter modifications. ACS Synth. Biol. 8, 2295–2302. doi:10.1021/acssynbio.9b00093
Chircov, C., Bîrcă, A. C., Grumezescu, A. M., and Andronescu, E. (2020). Biosensors-on-Chip: an up-to-date review. Molecules 25, 6013. doi:10.3390/molecules25246013
Chong, H., and Ching, C. B. (2016). Development of colorimetric-based whole-cell biosensor for organophosphorus compounds by engineering transcription regulator DmpR. ACS Synth. Biol. 5, 1290–1298. doi:10.1021/acssynbio.6b00061
Clark, L. C., and Lyons, C. (1962). Electrode systems for continuous monitoring in cardiovascular surgery. Ann. N. Y. Acad. Sci. 102, 29–45. doi:10.1111/j.1749-6632.1962.tb13623.x
Courbet, A., Endy, D., Renard, E., Molina, F., and Bonnet, J. (2015). Detection of pathological biomarkers in human clinical samples via amplifying genetic switches and logic gates. Sci. Transl. Med. 7, 289ra83. doi:10.1126/scitranslmed.aaa3601
Crabbé, A., Pycke, B., Van Houdt, R., Monsieurs, P., Nickerson, C., Leys, N., et al. (2010). Response of Pseudomonas aeruginosa PAO1 to low shear modelled microgravity involves AlgU regulation. Environ. Microbiol. 12, 1545–1564. doi:10.1111/j.1462-2920.2010.02184.x
Cui, Y., Chen, Y., Zhang, Y., and Zhang, L. (2025). Advances in aptamer technology for target-based drug discovery. J. Pharm. Anal., 101369. doi:10.1016/j.jpha.2025.101369
Doricchi, A., Platnich, C. M., Gimpel, A., Horn, F., Earle, M., Lanzavecchia, G., et al. (2022). Emerging approaches to DNA data storage: challenges and prospects. ACS Nano 16, 17552–17571. doi:10.1021/acsnano.2c06748
Environmental Protection Agency (1997). 40 CFR part 725—Requirements for microorganisms under TSCA. Available online at: https://www.ecfr.gov/current/title-40/part-725.
European Commission (2024). Building the future with nature: boosting biotechnology and biomanufacturing in the EU, (brussels). Available online at: https://eur-lex.europa.eu/legalcontent/EN/TXT/HTML/?uri=CELEX%3A52024DC0137R%2801%29 (Accessed November 20, 2025).
European Union (2001). Directive 2001/18/EC on the deliberate release into the environment of genetically modified organisms. Official J. Eur. Union L106, 1–39. Available online at: https://eur-lex.europa.eu/eli/dir/2001/18/oj.
European Union (2009). Directive 2009/41/EC on the contained use of genetically modified micro-organisms. Official J. Eur. Union L125, 75–97. Available online at: https://eur-lex.europa.eu/eli/dir/2009/41/oj.
Farazmand, M. H., Rodrigues, R., Gardner, J. W., and Charmet, J. (2019). “Design and development of a disposable Lab-on-a-Chip for prostate cancer detection,” in 2019 41st annual international conference of the IEEE engineering in medicine and biology society (EMBC) (Berlin, Germany: IEEE), 1579–1583. doi:10.1109/EMBC.2019.8857072
Fernández Ríos, D., Quintana, S. A., Gómez Paniagua, P., Arrúa, A. A., Brozón, G. R., Bertoni Hicar, M. S., et al. (2025). Regulatory challenges and global trade implications of genome editing in agriculture. Front. Bioeng. Biotechnol. 13, 1609110. doi:10.3389/fbioe.2025.1609110
Fu, Y., Li, J., Wang, J., Wang, E., and Fang, X. (2024). Development of a two component system based biosensor with high sensitivity for the detection of copper ions. Commun. Biol. 7, 1407. doi:10.1038/s42003-024-07112-6
Gangopadhyay, M., Chakravarti, S., Sanyamat, S., and Dewanjee, S. (2024). “Biosensors in agriculture: pioneering the future of precision farming and sustainability,” in Biosensors: developments, challenges and perspectives. Editors R. Mathew, and J. Ajayan (Singapore: Springer Nature Singapore), 259–301. doi:10.1007/978-981-97-3048-3_13
Gao, W., Wang, Q., Gong, W., Zheng, L., Liu, Q., Zhang, L., et al. (2025). Recent advances in biosensors: structure, principles, classification, and application in bio-manufacturing. Methods 242, 123–142. doi:10.1016/j.ymeth.2025.07.011
Giordano, D. (2020). Bioactive molecules from extreme environments. Mar. Drugs 18, 640. doi:10.3390/md18120640
Gorjikhah, F., Davaran, S., Salehi, R., Bakhtiari, M., Hasanzadeh, A., Panahi, Y., et al. (2016). Improving “lab-on-a-chip” techniques using biomedical nanotechnology: a review. Artif. Cells Nanomedicine Biotechnol. 44, 1609–1614. doi:10.3109/21691401.2015.1129619
Hoose, A., Vellacott, R., Storch, M., Freemont, P. S., and Ryadnov, M. G. (2023). DNA synthesis technologies to close the gene writing gap. Nat. Rev. Chem. 7, 144–161. doi:10.1038/s41570-022-00456-9
Houssin, T., Cramer, J., Grojsman, R., Bellahsene, L., Colas, G., Moulet, H., et al. (2016). Ultrafast, sensitive and large-volume on-chip real-time PCR for the molecular diagnosis of bacterial and viral infections. Lab. Chip 16, 1401–1411. doi:10.1039/C5LC01459J
Huang, F., Zhang, Y., Lin, J., and Liu, Y. (2021). Biosensors coupled with signal amplification technology for the detection of pathogenic bacteria: a review. Biosensors 11, 190. doi:10.3390/bios11060190
Jaekel, J. J., Schweingruber, D., Cheras, V., Doshi, J., and Benenson, Y. (2021). Multi-input biocomputer gene circuits for therapeutic application. Curr. Opin. Syst. Biol. 28, 100371. doi:10.1016/j.coisb.2021.100371
Jung, J. K., Alam, K. K., Verosloff, M. S., Capdevila, D. A., Desmau, M., Clauer, P. R., et al. (2020). Cell-free biosensors for rapid detection of water contaminants. Nat. Biotechnol. 38, 1451–1459. doi:10.1038/s41587-020-0571-7
Keiper, F., and Atanassova, A. (2025). International synthetic biology policy developments and implications for global biodiversity goals. Front. Synth. Biol. 3, 1585337. doi:10.3389/fsybi.2025.1585337
Kim, J. H., Kim, J. H., Kim, S. D., Kim, H., and Cho, B.-K. (2025). Recent advances in AI-enabled automation of DNA assembly in biofoundries. Curr. Opin. Biotechnol. 96, 103386. doi:10.1016/j.copbio.2025.103386
Kuruma, Y., Nomaki, H., Isobe, N., Matsuoka, D., and Shimane, Y. (2024). The potential of artificial cells functioning under in situ deep-sea conditions. ACS Synth. Biol. 13, 3144–3149. doi:10.1021/acssynbio.4c00441
Leal-Alves, C., Deng, Z., Kermeci, N., and Shih, S. C. C. (2024). Integrating microfluidics and synthetic biology: advancements and diverse applications across organisms. Lab. Chip 24, 2834–2860. doi:10.1039/D3LC01090B
Liang, J., Liu, C., and Xu, T. (2025). Innovative biosensing smart masks: unveiling the future of respiratory monitoring. Mater. Horiz. 12, 5975–5993. doi:10.1039/D5MH00279F
Liu, X., Tang, T.-C., Tham, E., Yuk, H., Lin, S., Lu, T. K., et al. (2017). Stretchable living materials and devices with hydrogel–elastomer hybrids hosting programmed cells. Proc. Natl. Acad. Sci. 114, 2200–2205. doi:10.1073/pnas.1618307114
Liu, R., Liu, T., Liu, W., Luo, B., Li, Y., Fan, X., et al. (2024). SemiSynBio: a new era for neuromorphic computing. Synth. Syst. Biotechnol. 9, 594–599. doi:10.1016/j.synbio.2024.04.013
Lu, Y., Macias, D., Dean, Z. S., Kreger, N. R., and Wong, P. K. (2015). A UAV-mounted whole cell biosensor system for environmental monitoring applications. IEEE Trans. NanoBioscience 14, 811–817. doi:10.1109/TNB.2015.2478481
Mann, M. M., Tang, J. D., and Berger, B. W. (2022). Engineering human liver fatty acid binding protein for detection of poly- and perfluoroalkyl substances. Biotechnol. Bioeng. 119, 513–522. doi:10.1002/bit.27981
McLaughlin, J. A., Beal, J., Mısırlı, G., Grünberg, R., Bartley, B. A., Scott-Brown, J., et al. (2020). The synthetic biology open language (SBOL) version 3: simplified data exchange for bioengineering. Front. Bioeng. Biotechnol. 8, 1009. doi:10.3389/fbioe.2020.01009
National Academies of Sciences, Engineering, and Medicine (2017). Preparing for future products of biotechnology. Washington, D.C.: National Academies Press. doi:10.17226/24605
Nguyen, T., Zoëga Andreasen, S., Wolff, A., and Duong Bang, D. (2018). From lab on a chip to point of care devices: the role of open source microcontrollers. Micromachines 9, 403. doi:10.3390/mi9080403
Nikoleli, G.-P., Karapetis, S., Bratakou, S., Nikolelis, D. P., Tzamtzis, N., Psychoyios, V. N., et al. (2016). “Biosensors for security and bioterrorism: definitions, history, types of agents, new trends and applications,” in Biosensors for security and bioterrorism applications. Editors D. P. Nikolelis, and G.-P. Nikoleli (Cham: Springer International Publishing), 1–13. doi:10.1007/978-3-319-28926-7_1
Novak, L., Neuzil, P., Pipper, J., Zhang, Y., and Lee, S. (2007). An integrated fluorescence detection system for lab-on-a-chip applications. Lab. Chip 7, 27–29. doi:10.1039/B611745G
Pardee, K., Green, A. A., Takahashi, M. K., Braff, D., Lambert, G., Lee, J. W., et al. (2016). Rapid, low-cost detection of zika virus using programmable biomolecular components. Cell 165, 1255–1266. doi:10.1016/j.cell.2016.04.059
Paul, A. A., Aladese, A. D., and Marks, R. S. (2024). Additive manufacturing applications in biosensors technologies. Biosensors 14, 60. doi:10.3390/bios14020060
Rajak, P., Ganguly, A., Adhikary, S., and Bhattacharya, S. (2023). Internet of things and smart sensors in agriculture: scopes and challenges. J. Agric. Food Res. 14, 100776. doi:10.1016/j.jafr.2023.100776
Ray, S., Senapati, T., Sahu, S., Bandyopadhyaya, R., and Anand, R. (2018). Design of ultrasensitive protein biosensor strips for selective detection of aromatic contaminants in environmental wastewater. Anal. Chem. 90, 8960–8968. doi:10.1021/acs.analchem.8b01130
Razavi, Z., Soltani, M., Pazoki-Toroudi, H., and Chen, P. (2024). CRISPR-microfluidics nexus: advancing biomedical applications for understanding and detection. Sens. Actuators Phys. 376, 115625. doi:10.1016/j.sna.2024.115625
Robinson, D. K. R., and Nadal, D. (2025). Synthetic biology in focus: policy issues and opportunities in engineering life. doi:10.1787/3e6510cf-en
Roda, A., Mirasoli, M., Guardigli, M., Zangheri, M., Caliceti, C., Calabria, D., et al. (2018). Advanced biosensors for monitoring astronauts’ health during long-duration space missions. Biosens. Bioelectron. 111, 18–26. doi:10.1016/j.bios.2018.03.062
Rothenbroker, M., Zakaria, S., Morrison, D., and Li, Y. (2019). in DNAzymes: synthetic enzymes made of DNA. Editors Q. Husain, and M. F. Ullah (Cham: Springer International Publishing), 1–16. doi:10.1007/978-3-030-25023-2_1
Rottinghaus, A. G., Xi, C., Amrofell, M. B., Yi, H., and Moon, T. S. (2022). Engineering ligand-specific biosensors for aromatic amino acids and neurochemicals. Cell Syst. 13, 204–214.e4. doi:10.1016/j.cels.2021.10.006
Roy, R., Ray, S., Chowdhury, A., and Anand, R. (2021). Tunable multiplexed whole-cell biosensors as environmental diagnostics for ppb-level detection of aromatic pollutants. ACS Sens. 6, 1933–1939. doi:10.1021/acssensors.1c00329
Saha, R., Sarkar, M., Choudhury, S. S., Kumar, H., Bhatt, G., and Bhattacharya, S. (2024). Evolution of 3d printing technology in fabrication of microfluidic devices and biological applications: a comprehensive review. J. Micromanufacturing 7, 110–140. doi:10.1177/25165984241237357
Sajeevan, A., Sukumaran, R. A., Panicker, L. R., and Kotagiri, Y. G. (2025). Trends in ready-to-use portable electrochemical sensing devices for healthcare diagnosis. Microchim. Acta 192, 80. doi:10.1007/s00604-024-06916-x
Saldanha, D. J., Abdali, Z., Modafferi, D., Janfeshan, B., and Dorval Courchesne, N.-M. (2020). Fabrication of fluorescent pH-responsive protein–textile composites. Sci. Rep. 10, 13052. doi:10.1038/s41598-020-70079-x
Sano, T., Zhang, H., Losakul, R., and Schmidt, H. (2022). All-in-One optofluidic chip for molecular biosensing assays. Biosensors 12, 501. doi:10.3390/bios12070501
Schlosser, K., and Li, Y. (2009). Biologically inspired synthetic enzymes made from DNA. Chem. Biol. 16, 311–322. doi:10.1016/j.chembiol.2009.01.008
Sekhwama, M., Mpofu, K., Sudesh, S., and Mthunzi-Kufa, P. (2024). Integration of microfluidic chips with biosensors. Discov. Appl. Sci. 6, 458. doi:10.1007/s42452-024-06103-w
Selim, A. S., Perry, J. M., Nasr, M. A., Pimprikar, J. M., and Shih, S. C. C. (2022). A synthetic biosensor for detecting putrescine in beef samples. ACS Appl. Bio Mater. 5, 5487–5496. doi:10.1021/acsabm.2c00824
Sheth, R. U., and Wang, H. H. (2018). DNA-based memory devices for recording cellular events. Nat. Rev. Genet. 19, 718–732. doi:10.1038/s41576-018-0052-8
Siavashy, S., Soltani, M., Rahimi, S., Hosseinali, M., Guilandokht, Z., and Raahemifar, K. (2024). Recent advancements in microfluidic-based biosensors for detection of genes and proteins: applications and techniques. Biosens. Bioelectron. X 19, 100489. doi:10.1016/j.biosx.2024.100489
Silverman, A. D., Akova, U., Alam, K. K., Jewett, M. C., and Lucks, J. B. (2020). Design and optimization of a cell-free atrazine biosensor. ACS Synth. Biol. 9, 671–677. doi:10.1021/acssynbio.9b00388
Singh, B. K., Delgado-Baquerizo, M., Egidi, E., Guirado, E., Leach, J. E., Liu, H., et al. (2023). Climate change impacts on plant pathogens, food security and paths forward. Nat. Rev. Microbiol. 21, 640–656. doi:10.1038/s41579-023-00900-7
Sleight, S. C., and Sauro, H. M. (2013). Visualization of evolutionary stability dynamics and competitive fitness of Escherichia coli engineered with randomized multigene circuits. ACS Synth. Biol. 2, 519–528. doi:10.1021/sb400055h
Solanki, A., Griffin, Z., Sutradhar, P. R., Pradhan, K., Merritt, C., Ganguly, A., et al. (2023). Neural network execution using nicked DNA and microfluidics. PLOS ONE 18, e0292228. doi:10.1371/journal.pone.0292228
Somayaji, A., Sarkar, S., Balasubramaniam, S., and Raval, R. (2022). Synthetic biology techniques to tackle heavy metal pollution and poisoning. Synth. Syst. Biotechnol. 7, 841–846. doi:10.1016/j.synbio.2022.04.007
Sun, S., Peng, K., Sun, S., Wang, M., Shao, Y., Li, L., et al. (2023). Engineering modular and highly sensitive cell-based biosensors for aromatic contaminant monitoring and high-throughput enzyme screening. ACS Synth. Biol. 12, 877–891. doi:10.1021/acssynbio.3c00036
Sundaram, L. S., Ajioka, J. W., and Molloy, J. C. (2023). Synthetic biology regulation in Europe: containment, release and beyond. Synth. Biol. 8, ysad009. doi:10.1093/synbio/ysad009
Tang, H., Niu, J., Jin, H., Lin, S., and Cui, D. (2022). Geometric structure design of passive label-free microfluidic systems for biological micro-object separation. Microsyst. Nanoeng. 8, 62. doi:10.1038/s41378-022-00386-y
Tanniche, I., and Behkam, B. (2023). Engineered live bacteria as disease detection and diagnosis tools. J. Biol. Eng. 17, 65. doi:10.1186/s13036-023-00379-z
Varma, S., and Voldman, J. (2015). A cell-based sensor of fluid shear stress for microfluidics. Lab. Chip 15, 1563–1573. doi:10.1039/C4LC01369G
Varma, S., Fendyur, A., Box, A., and Voldman, J. (2017). Multiplexed cell-based sensors for assessing the impact of engineered systems and methods on cell health. Anal. Chem. 89, 4663–4670. doi:10.1021/acs.analchem.7b00256
Vashi, A., Sreejith, K. R., and Nguyen, N.-T. (2022). Lab-on-a-Chip technologies for microgravity simulation and space applications. Micromachines 14, 116. doi:10.3390/mi14010116
Velusamy, V., Arshak, K., Korostynska, O., Oliwa, K., and Adley, C. (2010). An overview of foodborne pathogen detection: in the perspective of biosensors. Biotechnol. Adv. 28, 232–254. doi:10.1016/j.biotechadv.2009.12.004
Verosloff, M., Chappell, J., Perry, K. L., Thompson, J. R., and Lucks, J. B. (2019). PLANT-Dx: a molecular diagnostic for point-of-use detection of plant pathogens. ACS Synth. Biol. 8, 902–905. doi:10.1021/acssynbio.8b00526
Wan, X., Saltepe, B., Yu, L., and Wang, B. (2021). Programming living sensors for environment, health and biomanufacturing. Microb. Biotechnol. 14, 2334–2342. doi:10.1111/1751-7915.13820
Wang, X., Luo, Y., Huang, K., and Cheng, N. (2022). Biosensor for agriculture and food safety: recent advances and future perspectives. Adv. Agrochem. 1, 3–6. doi:10.1016/j.aac.2022.08.002
Wu, Y., Xu, X., Zhu, Y., Wan, J., Wang, X., Zhou, X., et al. (2025). Research progress on multiplexed pathogen detection using optical biosensors. Biosensors 15, 378. doi:10.3390/bios15060378
Xia, L., and Li, G. (2023). Recent progress of microfluidic sample preparation techniques. J. Sep. Sci. 46, 2300327. doi:10.1002/jssc.202300327
Yang, S.-M., Lv, S., Zhang, W., and Cui, Y. (2022). Microfluidic point-of-care (POC) devices in early diagnosis: a review of opportunities and challenges. Sensors 22, 1620. doi:10.3390/s22041620
Yousefi, H., Ali, M. M., Su, H.-M., Filipe, C. D. M., and Didar, T. F. (2018). Sentinel wraps: real-time monitoring of food contamination by printing DNAzyme probes on food packaging. ACS Nano 12, 3287–3294. doi:10.1021/acsnano.7b08010
Zahra, Z., Song, M., Habib, Z., and Ikram, S. (2025). Advances in per- and polyfluoroalkyl substances (PFAS) detection and removal techniques from drinking water, their limitations, and future outlooks. Emerg. Contam. 11, 100434. doi:10.1016/j.emcon.2024.100434
Zea, L., Larsen, M., Estante, F., Qvortrup, K., Moeller, R., Dias De Oliveira, S., et al. (2017). Phenotypic changes exhibited by E. coli cultured in space. Front. Microbiol. 8, 1598. doi:10.3389/fmicb.2017.01598
Zhang, J., Zhang, X., Zhang, Y., Yang, X., Guo, L., Man, C., et al. (2025). Emerging biosensors integrated with microfluidic devices: a promising analytical tool for on-site detection of mycotoxins. Npj Sci. Food 9, 84. doi:10.1038/s41538-025-00444-5
Zhao, N., Song, Y., Xie, X., Zhu, Z., Duan, C., Nong, C., et al. (2023). Synthetic biology-inspired cell engineering in diagnosis, treatment and drug development. Signal Transduct. Target. Ther. 8, 112. doi:10.1038/s41392-023-01375-x
Keywords: synthetic biology, biosensors, microfluidics, point-of-care diagnostics, biocontainment, environmental monitoring, food safety and security
Citation: David G and Cai Y (2026) Integrating synthetic biology and laboratory-on-a-chip technologies for next-generation biosensors. Front. Lab Chip Technol. 4:1716737. doi: 10.3389/frlct.2025.1716737
Received: 30 September 2025; Accepted: 18 December 2025;
Published: 06 February 2026.
Edited by:
Mohammad Nasr Esfahani, University of York, United KingdomReviewed by:
Lorenzo Pasotti, University of Pavia, ItalyMehdi Tayybi Azar, Yeditepe University, Türkiye
Dagwin Wachholz Junior, State University of Campinas, Brazil
Copyright © 2026 David and Cai. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Yizhi Cai, eWl6aGkuY2FpQG1hbmNoZXN0ZXIuYWMudWs=