The overall goal of this research was to develop an efficient microfluidic system to study signal transduction in stimulation dynamics. This research applied reactive transport fundamentals in concert with biological systems knowledge to completely understand diffusion of soluble signals, fluid and particle flow properties, and dynamics of cellular responses. First, a device capable of parallel multi-time-point cell stimulation and lysis on-chip was developed in collaboration. Second, to understand flow of cells through complex 3-D flow schemes, a Single-field Three-dimensional Epifluorescence Particle (STEP) imaging technique was developed. Using the STEP imaging technique, we were able to determine particle distributions and track individual particles in complex flow geometries. Third, during the design of the stimulation device it was observed that the cells do not distribute across the channel in the same way as the fluids. Based on the observation that geometry and particle size were most influential factors on particle distribution, it was hypothesized that our earlier observation and all observed phenomena in our experimental range were due to the volume exclusion of particles of finite size near the wall of the complex flow geometry. Overall, this work contributed to the realization of microfluidic platforms as powerful tools for probing areas of biology and medicine that are difficult with existing technology. The high-throughput format enabled simple and fast generation of large sets of quantitative data, with consistent sample handling. We demonstrated the necessary first steps to designing efficient unit operations on cells in microfluidic devices. The model can be used for informed design of unit operations in many applications in the future.
| Identifer | oai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/45818 |
| Date | 25 August 2011 |
| Creators | Paul, Alison Marie |
| Publisher | Georgia Institute of Technology |
| Source Sets | Georgia Tech Electronic Thesis and Dissertation Archive |
| Detected Language | English |
| Type | Dissertation |
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