Optimization of a microfluidic mixing process for gene expression-based bio-dosimetry

In recent decades advances in radiation imaging and radiation therapy have led to a dramatic increase in the number of people exposed to radiation. Consequently, there is a clear need for personalized biodosimetry diagnostics in order to monitor the dose of radiation received and adapt it to each patient depending on their sensitivity to radiation exposure (Hall and Brenner 2008). Similarly, after a large-scale radiological event such as a dirty bomb attack, there will be a major need to assess, within a few days, the radiation doses received by tens of thousands of individuals. Current high-throughput devices can handle only a few hundred individuals per day. Hence, there is a great need for a very fast, self-contained, noninvasive biodosimetric device based on a rapid blood test. This article presents a case study where regression methods and designed experiments are used to arrive at the optimal settings for various factors that impact the kinetics in a biodosimetric device. We use ridge regression to initially identify a set of potentially important variables in the mixing process, which is one of the critical subsystems of the device. This was followed by a series of designed experiments to arrive at the optimal setting of the significant microfluidic cartridge and piezoelectric disk (PZT; Sadler and Zenhausern 2006; Lee et al. 2005) related factors. This statistical approach has been utilized to study the microfluidic mixing to mix water and dye mixtures of 70L volume. The outcome of the statistical design, experimentation, and analysis was then exploited for optimizing the design, fabrication, and assembly of microfluidic devices. As a result of the experiments performed, the system was fine-tuned and the mixing time was reduced from 5.5 to 2 minutes.