BILAYER LIPID MEMBRANE (BLM) INTEGRATION INTO MICROFLUIDIC PLATFORMS WITH APPLICATION TOWARD BLM-BASED BIOSENSORS

Abstract

Bilayer Lipid Membranes (BLMs) have been widely used as an experimental tool to investigate fundamental cellular membrane physics and ion channel formation and transduction. Traditional BLM experimentation is usually performed in a macro-sized electrophysiology rig, which suffers from several well-known issues. First, BLMs have short lifetimes (typically on the order of tens of minutes to a few hours) and the laborious, irreproducible membrane formation process must be repeatedly applied for long-term testing. Second, stray capacitance inherent to traditional test rigs limits the temporal response leading, for example, to poor resolution in determining fast ion channel translocation events. Lastly, BLM testing is done within a single site format thus limiting throughput and increasing data collection time. To mitigate the above drawbacks, BLM technology and microfluidic platforms can be integrated to advance the state-of-the-art of BLM-based biosensor technology. Realization of BLM-based microfluidic biosensors can offer significant improvement towards sensor response characteristics (e.g. lower noise floor, increased time response). In addition, microfluidic biosensing chips can be fabricated with multiple BLM test sites that allow for parallel testing thus increasing data collection efficiency. Other benefits that microfluidics offer are: small reagent sensing volumes, disposable packaging, mass manufacturability, device portability for field studies, and lower device cost. Novel polymer microfluidic platforms capable of both in-situ and ex-situ BLM formation are described in this work. The platforms have been demonstrated for the controlled delivery of trans-membrane proteins to the BLM sites, and monitoring of translocation events through these ion channels using integrated thin film Ag/AgCl electrodes. The detailed design, fabrication, and characterization of various micro-fabricated BLM platforms is presented in this dissertation.