CAREER: Modeling, Estimation and Coding for Biosensor Arrays

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Project AbstractThe investigator studies stochastic modeling and signal processing aspects of biosensor arrays. The biosensor arrays are time and cost efficient, and enable exciting new applications in medicine, drug discovery, defense systems, and environmental monitoring. Protein arrays, for instance, test for multiple pathogen infections by examining dozens of different antigens at once. DNA microarrays, on the other hand, are capable of screening tens or even hundreds of thousands of different gene sequences at the same time, revealing critical information about the functionality of cells, effects of drugs on organisms, etc. To fully realize the potentials of the biosensor array technology, however, several key research challenges must be addressed.Molecular binding between the bio-molecules of interest and sensing elements, which enables detection in biosensor arrays, is a random process. Real-time biosensors can take multiple temporal measurements of this process, thus allowing observation of the binding kinetics as well as a precise characterization of its steady-state. The investigator specifically aims to: (1) Develop stochastic models and solve the estimation problems in real-time biosensor arrays. This research involves solving parameter estimation problems in discretely observed systems modeled by stochastic differential equations. (2) Determine limits of performance of estimation algorithms in biosensor arrays, characterizing them via lower bounds on the minimum mean-square estimation error. (3) Develop coding strategies that improve the performance of biosensor arrays, and study signal recovery techniques which enable economic use of the sensing resources therein.The results of the outlined work are expected to have a major impact on the development and applications of biosensor arrays, and are expected to broaden the educational experience of engineering students at the University of Texas at Austin.