Design automation based on fluid dynamics

This article was accepted and presented at the 9th International Workshop on Bio-Design Automation, Pittsburgh, Pennsylvania (2017).Microfluidic devices provide researchers with numerous advantages such as high throughput, increased sensitivity and accuracy, lower cost, and reduced reaction time. However, design, fabrication, and running a microfluidic device are still heavily reliant on expertise. Recent studies suggest micro-milling can be a semi-automatic, inexpensive, and simple alternative to common fabrication methods. Micro-milling does not require a clean-room, mask aligner, spin-coater, and Plasma bonder, thus cutting down the cost and time of fabrication significantly. Moreover, through this protocol researchers can easily fabricate microfluidic devices in an automated fashion eschewing levels of expertise required for typical fabrication methods, such as photolithography, soft-lithography, and etching. However, designing a microfluidic chip that meets a certain set of requirements is still heavily dependent on a microfluidic expert, several days of simulation, and numerous experiments to reach the required performance. To address this, studies have reported random automated design of microfluidic devices based on numerical simulations for micro-mixing. However, random design generation is heavily reliant on time-consuming simulations carried out beforehand, and is prone to error due to the accuracy limitations of the numerical method. On the other hand, by using micro-milling for ultra-fast and inexpensive fabrication of microfluidic devices and Taguchi design of experiments for state-space exploration of all of the geometric parameters, we are able to generate a database of geometries, flow rates, and flow properties required for a single primitive to carry out a specified microfluidic task

Standardizing design performance comparison in microfluidic manufacturing

Microfluidic devices published in literature today lack sufficient information for automating the physical design process. Moreover, the constantly changing landscape of manufacturing and technological requirements poses a large problem in the physical design automation space. In this talk, we discuss some of the methodologies and standards formulated by CIDAR at BU and CARES at UC Riverside that allow not only allow the researchers in the physical design automation space to share and compare their results but also provide means for capturing the Specify, Design and Build lifecycle in microfluidic design

mLSI design with MINT

Fluigi is a microfluidic design framework that allows researchers to realize abstract descriptions of liquid flow relationships automatically as physical devices and corresponding control software. Its goal is to provide synthetic biology researchers with the tools to use microfluidics for novel computation, discovery, and test applications. A critical component of this work-flow is MINT, a format for describing the microfluidic components and the connectivity of the control and flow layers in the microfluidic device. This work describes MINT and where it falls in the larger Fluigi software flow in design mLSI system for Synthetic Biology

Function-driven, graphical design tool for microfluidic chips: 3DuF

The use of microfluidic chips for applications in biology to reduce the cost, time, and difficulty of automating experiments, while promising, has proven to have barriers to entry. In particular, the cost of the equipment required for manufacturing techniques like soft lithography, the difficulty in designing functional microfluidic chips, and the time associated with manufacturing them have made rapid production for prototyping and iterative design difficult. Our lab’s microfluidics design flow is capable of automating much of the design process of microfluidic chips using the paradigm of defining them as primitives placed on a layout grid and exporting standard formats for use in fabrication. 3DuF, a design tool that allows the user to carry out the placement and connection of primitives through a browser-based GUI, simplifies the design process to specifying the primitives through parameters and using a pointer to connect them with channels. But this approach assumes that the designer knows exactly what physical dimensions the primitives need for the chip to perform adequately for experiments, which may not be the case if sufficient literature or a fluid dynamics expertise are not present. By communicating with DAFD, our lab’s currently in-development database and model-fitting framework, 3DuF will be able to define microfluidic primitives for placement on chip layouts not only through physical dimensions, but also by specific performance metrics desired of the primitives’ functions, which will result in automatically generated dimensions for those primitives. This will allow chip design through the simple paradigm of using a GUI to place primitives and connect them with channels, while also making a useful definition of those primitives for the designer’s needs less reliant on their fluid dynamics expertise

3DμF - Interactive design environment for continuous flow microfluidic devices

The design of microfluidic Lab on a Chip (LoC) systems is an onerous task requiring specialized skills in fluid dynamics, mechanical design drafting, and manufacturing. Engineers face significant challenges during the labor-intensive process of designing microfluidic devices, with very few specialized tools that help automate the process. Typical design iterations require the engineer to research the architecture, manually draft the device layout, optimize for manufacturing processes, and manually calculate and program the valve sequences that operate the microfluidic device. The problem compounds when engineers not only have to test the functionality of the chip but are also expected to optimize them for the robust execution of biological assays. In this paper, we present an interactive tool for designing continuous flow microfluidic devices. 3DμF is the first completely open source interactive microfluidic system designer that readily supports state of the art design automation algorithms. Through various case studies, we show 3DμF can be used to reproduce designs from literature, provide metrics for evaluating microfluidic design complexity and showcase how 3DμF is a platform for integrating a wide assortment of engineering techniques used in the design of microfluidic devices as a part of the standard design workflow.Published versio

Effects of once-weekly exenatide on cardiovascular outcomes in type 2 diabetes

BACKGROUND: The cardiovascular effects of adding once-weekly treatment with exenatide to usual care in patients with type 2 diabetes are unknown. METHODS: We randomly assigned patients with type 2 diabetes, with or without previous cardiovascular disease, to receive subcutaneous injections of extended-release exenatide at a dose of 2 mg or matching placebo once weekly. The primary composite outcome was the first occurrence of death from cardiovascular causes, nonfatal myocardial infarction, or nonfatal stroke. The coprimary hypotheses were that exenatide, administered once weekly, would be noninferior to placebo with respect to safety and superior to placebo with respect to efficacy. RESULTS: In all, 14,752 patients (of whom 10,782 [73.1%] had previous cardiovascular disease) were followed for a median of 3.2 years (interquartile range, 2.2 to 4.4). A primary composite outcome event occurred in 839 of 7356 patients (11.4%; 3.7 events per 100 person-years) in the exenatide group and in 905 of 7396 patients (12.2%; 4.0 events per 100 person-years) in the placebo group (hazard ratio, 0.91; 95% confidence interval [CI], 0.83 to 1.00), with the intention-to-treat analysis indicating that exenatide, administered once weekly, was noninferior to placebo with respect to safety (P<0.001 for noninferiority) but was not superior to placebo with respect to efficacy (P=0.06 for superiority). The rates of death from cardiovascular causes, fatal or nonfatal myocardial infarction, fatal or nonfatal stroke, hospitalization for heart failure, and hospitalization for acute coronary syndrome, and the incidence of acute pancreatitis, pancreatic cancer, medullary thyroid carcinoma, and serious adverse events did not differ significantly between the two groups. CONCLUSIONS: Among patients with type 2 diabetes with or without previous cardiovascular disease, the incidence of major adverse cardiovascular events did not differ significantly between patients who received exenatide and those who received placebo