Automating functional enzyme screening & characterization

This work has been presented in the 10th IWBDA workshop.Microfluidics continue to gain traction as an inexpensive alternative to standard multi-well plate-based, and flow cytometry- based, assay platforms. These devices are especially useful for the types of ultra-high throughput screens needed for enzyme discovery applications where large numbers (>106) of unique samples must be screened rapidly1. Coupled with cell-free protein synthesis2, microfluidics are being used to identify novel enzymes useful for a variety of applications with unprecedented speed. However, these devices are typically produced using PDMS, and require considerable infrastructure and artisanal skill to fabricate, limiting their accessibility. Likewise, enzyme hits obtained from a screen are often validated manually and would benefit from automation of downstream validation processes. To address these limitations, we propose a workflow which leverages software tools to automate the rapid design and fabrication of low-cost polycarbonate microfluidic devices for use as high-throughput screening platforms for enzyme discovery, as well as an automated DNA assembly tool to streamline validation of screening candidates. Using this workflow, we aim to identify novel oxidoreductase enzymes from environmental metagenomic DNA libraries, for use in electrochemical biosensors

A reverse predictive model towards design automation of microfluidic droplet generators

This work has been presented in the 10th IWBDA workshop.Droplet-based microfluidic devices in comparison to test tubes can reduce reaction volumes 10^9 times and more due to the encapsulation of reactions in micro-scale droplets [4]. This volume reduction, alongside higher accuracy, higher sensitivity and faster reaction time made droplet microfluidics a superior platform particularly in biology, biomedical, and chemical engineering. However, a high barrier of entry prevents most of life science laboratories to exploit the advantages of microfluidics. There are two main obstacles to the widespread adoption of microfluidics, high fabrication costs, and lack of design automation tools. Recently, low-cost fabrication methods have reduced the cost of fabrication significantly [7]. Still, even with a low-cost fabrication method, due to lack of automation tools, life science research groups are still reliant on a microfluidic expert to develop any new microfluidic device [3, 5]. In this work, we report a framework to develop reverse predictive models that can accurately automate the design process of microfluidic droplet generators. This model takes prescribed performance metrics of droplet generators as the input and provides the geometry of the microfluidic device and the fluid and flow settings that result in the desired performance. We hope this automation tool makes droplet-based microfluidics more accessible, by reducing the time, cost, and knowledge needed for developing a microfluidic droplet generator that meets certain performance requirement

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

Rapid prototyping, performance characterization, and design automation of droplet-based microfluidic devices

Droplet generators are at the heart of many microfluidic devices developed for life science applications but are difficult to tailor to each specific application. The high fabrication costs, complex fluid dynamics, and incomplete understanding of multi-phase flows make engineering droplet-based platforms an iterative and resource-intensive process. First, we demonstrate the suitability of desktop micromills for low-cost rapid prototyping of thermoplastic microfluidic devices. With this method, microfluidic devices are made in 1 - 2 hours, have a minimum feature size of 75 μm, and cost less than $10. These devices are biocompatible and can accommodate integrated electrodes for sophisticated droplet manipulations, such as droplet sensing, sorting, and merging. Next, we leverage low-cost rapid prototyping to characterize the performance of microfluidic flow-focusing droplet generators. Specifically, the effect of eight design parameters on droplet diameter, generation rate, generation regime, and polydispersity are quantified. This was achieved through orthogonal design of experiments, a large-scale experimental dataset, and statistical analysis. Finally, we capitalize on the created dataset and machine learning to achieve accurate performance prediction and design automation of flow-focusing devices. The developed capabilities are captured in a software tool that converts high-level performance specifications to a device that delivers the desired droplet diameter and generation rate. This tool effectively eliminates the need for resource-intensive design iterations to achieve functional droplet generators. We also demonstrate the tool’s generalizability to new fluid combinations with transfer learning. We expect that our newly established framework on rapid prototyping, performance characterization informed by design of experiments, and machine learning guided design automation to enable extension to other microfluidic components and to facilitate widespread adoption of droplet microfluidics in the life sciences

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

Modular microfluidic design automation using machine learning

Microfluidics is the science of handling liquids inside sub-millimeter microchannels at nano-liter and pico-liter scales. This volume reduction enables increased resolution, sensitivity, and throughput, while, reducing the reagent cost significantly. These advantages make microfluidic devices to be ideal substitutes for bench-top and robotic liquid handling in numerous life science applications, specifically, synthetic biology where there is a need for low-cost, automated, and high-throughput platforms. Despite the need, implementing microfluidic platforms in the life science research work-flow is an exception rather than being the norm. This can be attributed to the high cost of fabricating microfluidic devices and a lack of microfluidic design automation tools that can design a microfluidic geometry based on the desired performance. As a result, designing a microfluidic device that delivers an expected performance is an iterative, time-consuming, and costly process. To address this, we previously described a low-cost and accessible micro-milling technique to fabricate microfluidic devices in less than an hour while costing less than $10. However, still designing a microfluidic device that performs as expected is an iterative and in-efficient process. Therefore, microfluidic design automation tools that are able to design a microfluidic geometry and provide the necessary flow conditions and fluid properties that would deliver a user-specified performance is with great importance. We propose a modular design automation tool, called DAFD, that is able to design a microfluidic device based on user-specified performance and constraints. DAFD uses machine learning to generate accurate predictive models, and then exploits these models to provide a design automation platform. DAFD can be implemented in many microfluidic applications such as droplet generation, high-throughput sorting, and micro-mixing

Efficient large-scale microfluidic design-space exploration: from data to model to data

Droplet microfluidics is well poised to improve the gold standard in many fields such as synthetic biology. However, the lack of available design automation tools that can create a microfluidic droplet generator based on a desired performance, forces the design process to be iterative, inefficient, and costly, thus, hampering the wide-spread adoption of droplet microfluidics in the life sciences.Published versio

Active learning for efficient microfluidic design automation

Droplet microfluidics has the potential to eliminate the testing bottleneck in synthetic biology by screening biological samples encapsulated in water-in-oil emulsions at unprecedented throughput. Sophisticated screens require functional and complex devices that perform exactly as designed. Effective performance characterization and predictive design of droplet microfluidic components has been hampered due to low-throughput and expensive fabrication with standard soft lithography techniques. This has limited droplet microfluidics to proof-of-concept devices. Even when some of these barriers are removed through rapid prototyping, developing a robust dataset to effectively represent all parameters as a "lookup table" is near impossible.Published versio

Machine learning enables design automation of microfluidic flow-focusing droplet generation

Devices for droplet generation are at the heart of many microfluidic applications but difficult to tailor for specific cases. Lashkaripour et al. show how design customization can greatly be simplified by combining rapid prototyping with data-driven machine learning strategies