Development of a Smartphone-Based Biosensor for Rapid DNA Mutation Detection

Thesis Abstract

Rapid and accurate detection of DNA mutations is critical for early diagnosis, personalized treatment, and better management of genetic diseases and cancers. Conventional methods such as PCR, sequencing, and gel electrophoresis, although reliable, are often time-consuming, require sophisticated laboratory infrastructure, and are inaccessible in resource-limited settings. This study aims to develop a portable, cost-effective, smartphone-based biosensor capable of real-time detection of specific DNA mutations with high sensitivity and specificity. The research is driven by the need to bridge the gap between sophisticated diagnostic techniques and point-of-care testing, thereby enhancing healthcare delivery particularly in underserved communities. The specific objectives include designing an integrated biosensing platform utilizing nanomaterials for target DNA recognition, optimizing the bio-recognition element immobilization on biosensor substrates, developing a user-friendly smartphone application for signal readout and data analysis, and evaluating the biosensor’s performance in detecting mutations associated with common genetic disorders such as BRCA1 gene mutations linked to breast cancer. The methodology follows a mixed-methods research design incorporating experimental laboratory development and quantitative validation. The population targeted comprises human DNA samples obtained from accredited biobanks that contain characterized mutations relevant to the study. A sample size of 100 genomic DNA samples will be used, selected through stratified random sampling to ensure representation of mutation-positive and mutation-negative cases. The biosensor’s design involves the synthesis of nanostructured materials, such as gold nanoparticles and graphene oxide, functionalized with mutation-specific probes. Data collection will utilize fluorescence and impedimetric measurements captured by the biosensor, with signal processing facilitated by custom-developed smartphone applications employing machine learning algorithms for pattern recognition and mutation classification. Analytical techniques will include receiver operating characteristic (ROC) curve analysis to evaluate diagnostic accuracy, alongside Bland-Altman plots for method agreement, complemented by regression analysis to assess the correlation between biosensor signals and established laboratory results. The validity and reliability of the biosensor will be confirmed through repeated measures and calibration with known mutation standards, ensuring reproducibility and robustness. Expected findings suggest that the developed biosensor will demonstrate high sensitivity (>95%) and specificity (>90%) in detecting targeted DNA mutations, with detection times under 15 minutes. The smartphone interface is anticipated to provide an intuitive platform for healthcare workers to interpret results efficiently, facilitating immediate clinical decision-making. The integration of nanomaterials is expected to improve the biosensor’s hybridization efficiency and signal-to-noise ratio, while the use of machine learning models will enable accurate differentiation between wild-type and mutant sequences even at low DNA concentrations. This study contributes to the body of knowledge by advancing portable molecular diagnostics, demonstrating the feasibility of integrating nanotechnology with ICT for real-time genetic analysis, and establishing a framework for scalable point-of-care biosensing devices. The main conclusion affirms that smartphone-based biosensors can transform genetic testing by combining sensitivity, portability, and affordability, thereby enabling widespread deployment in diverse healthcare settings. It is recommended that future research explore expanding the sensor’s mutation spectrum, integrating cloud-based data storage for epidemiological surveillance, and conducting clinical trials to validate efficacy in field settings. Ultimately, this innovation has the potential to bridge critical gaps in genetic diagnostics, fostering timely interventions and personalized treatments, especially in resource-constrained environments.