The development of polydimethylsiloxane-based microfluidic devices with integrated controlling components has led to the translation of many bench-top biological processes to microdevice platforms. While many biomedical scientists recognize the applicability of microdevices to their research, their lack of engineering background and training in computer-aided design (CAD) makes them unable to design the desired microchips using AutoCAD software, which is the universal first step for chip fabrication. In this work, we used the .NET platform to develop a user-friendly AutoCAD add-on feature that includes the ability to create channels, loops, valves, and punches, as well as channel to loop connections, valve to channel connections, valve to loop connections, channel to channel connections, and punch to channel connections. This add-on feature greatly simplifies the process of AutoCAD drawing, which allows people not familiar with AutoCAD to easily learn to use the add-on. For testing purposes, we used the add-on to draw a series of two-layer microchips designed to synthesize luciferase using a coupled transcription-translation system with different capacities. The AutoCAD files for the microfluidic designs were then sent to the Stanford Microfluidics Foundry for chip fabrication. Our results show that the quantity of luciferase produced on the chip is proportional to the capacity of the reaction loop.
Recently, significant improvements have been shown in proteomics, an analytical technology recognized as a method to discover biomarkers. Several methods for proteome differential display have been developed to discover biomarkers, and these are based on labeling methods such as ICAT™ reagent. These labeling methods are not easily able to detect biomarkers in complex biological samples, and they have several issues such as the limitation of the modification site and the production of artificial errors. We therefore developed a computational quantitative proteomic analysis system i-OPAL (internal standard guided Optimal Profile ALignment) based on LC-MS measurements for biomarker discovery. This system can provide extensive quantitative analysis because it does not involve any artificial labels that might cause sampling bias and limitation of quantifiable proteins.