Design, Implementation and Evaluation of a Cell-Free Biosensor Platform for Real-Time Analyte Detection

Thesis Abstract

The rapid advancement of cell-free synthetic biology offers new avenues for real-time, field-deployable analyte detection, yet scalable, robust biosensor platforms that deliver quantitative readouts under diverse environmental conditions remain scarce. This study addresses the gap by designing, implementing, and rigorously evaluating a cell-free biosensor platform capable of real-time analyte detection with quantitative output, portability, and minimal reliance on cold-chain logistics. The aim is to develop a modular, paper-based cell-free system integrated with a smartphone-compatible optical reader to enable on-site monitoring of selected environmental and clinical analytes. Specific objectives are (1) to engineer programmable cell-free reaction circuits responsive to target analytes (including heavy metals, ground-level ozone proxies, and clinically relevant metabolites) using standardized TX-TL master mixes; (2) to optimize a paper-based encapsulation and stabilization protocol that preserves activity for up to 21 days at ambient temperatures; (3) to implement a dual-readout format combining colorimetric and fluorescent signals to improve dynamic range and limit of detection; (4) to validate analytical performance against reference methods (ICP-MS for metals, GC-MS for volatile compounds, and HPLC for metabolites) across matrices such as water, soil extract, and simulated physiological fluids; and (5) to evaluate usability, robustness, and data analytics in a field-deployed scenario using a smartphone spectrometer app. The methodological framework adopts a Design, Build, Test, and Learn cycle embedded within an action research paradigm to iteratively refine sensor components. The research design comprises experimental development followed by field validation. The population includes engineered cell-free reactions and commercially available TX-TL kits, wastewater-impacted water samples, soil eluates, and synthetic physiological matrices. A purposive sample of 60 environmental and 40 clinical-quality control samples will be analyzed to establish calibration curves, interference tolerance, and matrix effects. Data collection instruments include calibrated spectrophotometers for colorimetric signals, fluorescence microplate readers for fluorescent readouts, a smartphone-based optical reader app, and standardized questionnaires assessing user experience during field trials. Analytical methods include regression analysis to determine calibration models, Bland-Altman analysis to compare with reference methods, ANOVA to assess matrix effects and signal stability, and multivariate PCA to identify dominant factors influencing sensor performance. A mixed-methods approach will integrate qualitative usability data with quantitative analytical results to illuminate practical deployment challenges. Expected findings indicate that the optimized cell-free platform can achieve a dynamic range suitable for trace to moderate analyte concentrations (0.5–500 µM for metals, 1–100 ppb for volatile proxies, and 1–100 µM for metabolites) with LODs approaching conventional laboratory methods under field conditions. Stability studies will show preserved activity for at least 14–21 days at 25–30°C when encoded in trehalose-stabilized paper matrices. The dual-readout system is anticipated to improve accuracy by 20–35% compared with single-readout formats, particularly in turbid or colored matrices. Data analysis is expected to reveal minimal matrix interference for selected analytes post-optimization, supported by regression R2 values exceeding 0.95 and Bland-Altman limits of agreement within clinically acceptable ranges. The study contributes to knowledge by advancing a scalable, modular framework for cell-free biosensors that integrates stabilization, multiplexed readouts, and smartphone-enabled analytics, thereby bridging laboratory prototypes and field-ready devices. It offers empirical evidence on performance parity with standard reference methods across diverse matrices and provides a pragmatic model for regulatory-aligned validation of point-of-need biosensors. The theoretical contribution includes empirical validation of the Kinesthetic-Sensor Theory in a cell-free context, illustrating how reaction compartmentalization, signal transduction circuitry, and readout interfaces co-create robust performance under variable environmental conditions. Practical implications include a cost-effective, rapid-response platform suitable for environmental monitoring, public health screening, and rapid disaster-response testing, with potential scalability to panel-based sensing and data-driven surveillance networks. In conclusion, the study is expected to demonstrate that a well-engineered cell-free biosensor platform can deliver real-time, quantitative analyte detection with reliable performance across matrices and operational environments. Recommendations will address optimization of reaction component sourcing, standardization of stabilization protocols, regulatory considerations for field deployment, and pathways for integration with cloud-based data analytics for real-time surveillance and decision support.