CAREER: Transforming Biosensor Reliability using Sensor Time-series Data and Physics-based Machine Learning

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Access to reliable biosensors could transform public health by aiding ongoing and future pandemic management. However, biosensor reliability (e.g. false positive (type 1) and false negative(type 2) diagnoses) remains a barrier to widespread industrial and clinical use. Preliminary work performed in the Investigator’s lab suggests that using biosensor time series (TS) data and physics-based supervised Machine Learning (ML), a form of artificial intelligence that makes predictions from data, can reduce the probability of these errors. Thus, the research goal of this CAREER project is to examine the integration of machine learning and chemical engineering domain knowledge for improving biosensor reliability and performance. The proposed methodology will be applied across various sensor types, sizes, form factors, and data structures. If successful, access to reliable biosensors could catalyze biomanufacturing innovations and improve the speed and accuracy of current and emerging diagnostic methods. The education goal of this project is to create an interactive Open Course Ware (OCW) platform to increase education and workforce development opportunities at the interface of healthcare and data sciences for urban-underserved students. Planned activities include Gaming-driven Simulations in Biosensing for High School Students, a Virtual Lecture and Workshop on Data Archiving for Sensor Machine Learning for Undergraduate Students and Virtual Lectures on Emerging Applications of Machine Learning in the Bioanalytical, Life, and Materials Sciences for High School and Undergraduate Students. The investigator’s overarching career goal is to help transform biosensor performance through concepts in data-driven chemical engineering and expand the leadership of underrepresented groups in emerging data-driven life sciences industries. In keeping with this goal, the objective of this project is to transform the reliability of biosensors through the integration of physiochemical process modeling and supervised ML. The central approach is to integrate supervised machine learning and mass transfer-limited surface binding reaction theory for improving the reliability of bioanalyte quantification via biosensor time-series data. This project will test the hypothesis that integrating experimental parameters and mass transfer-limited surface binding reaction theory with supervised machine learning models for target analyte classification can reduce the extent of type 1 and 2 errors relative to state-of-the-art calibration methods. The proposed methodology will be applied to reliable biosensor-based detection of RNA, microRNA, and protein targets and benchmarked against standard clinical bioanalytical methods. This work will identify new data- and model-driven features of target binding, nonspecific binding, and biosensor drift in biosensor time-series data that can support the reliable classification of bioanalyte concentration using machine learning. If successful, identifying features of target binding and interfering inputs in biosensor time-series data could significantly improve the reliability and reproducibility of biosensors and biosensor-based controls.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.