Novel Nano-Liter Scale Microfluidic Platform for Protein Kinetics
Type of DegreeThesis
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In the mid-1970s, integrated circuits exploited miniaturized transistors to bring a revolution in the field of computation. This marked the foundation of computers and information technology. In this century, we are on the brink of a similar revolution in the large-scale automation of biological/chemical material processing with nano/microfluidic chips, in the form of a lab-on-a-chip. Lab-on-a-chip is a miniaturized device that manipulates tiny amounts of volumes in micro/nanometer scale channels to automate and integrate many materials processing steps on a single chip. The advantages of conducting materials processing on a lab-on-a-chip include reduced consumption of reagents, shorter analysis time, greater sensitivity, portability that allows in situ and real-time analysis, and easy disposability. Although lab-on-a-chip has many advantages, its successful development is still a challenging task. Recent progress in the area of lab-on-a-chip would impact the area of genomics and proteomics research and hence ultimately the very way we live in today’s world. As engineers are starting to design such compact devices, they are attracting new applications in biotechnology, medicine, point-of-care diagnostics and environmental monitoring. However, current lab-on-a-chip designs have several limitations in terms of conducting complex biomaterial analysis and its parallel processing. In this thesis, design, development, validation, and testing of a novel microfluidic device to conduct protein kinetics are presented. The microfluidic platform, called a ‘protein kinetics chip’, can also be used for biological and chemical material processing where reagent gradient generation and parallel processing are required. However, the emphasis is placed on performing enzyme kinetic reactions on a chip. The protein kinetics chip automates the process of metering, concentration gradient generation, mixing, and optical detection. In addition, the chip is comprised of numbers of parallel processors which have the capability to conduct multiple processes simultaneously.