Perl software to control lab syringe pump and valve device, for biology automation, initial version finished today. Works great. Next, need to add the network code, it can be controlled remotely and in synchronization with other laboratory devices, including the bio-robot. This software will be used in the microfluidics project. The software is also part of the larger Perl Robotics project, and a new release will be posted to CPAN next week.
More details on the software follow:
I’ve now tested the digital microfluidics board via microcontroller. The digital microfluidics board moves a liquid droplet via Electrowetting-on-Dielectric (EWOD). The microcontroller switches the high voltage via a switching board (pictured below, using Panasonic PhotoMOS chips), which controls the +930VDC output by the HVPS (posted earlier), and runs over USB using no cost Processing.org software. This is alpha stage testing.. cleaner version to be built. The goal of course is to scale the hardware to allow automation of microbiology protocols.
Labview is quite expensive, and industrial-grade high voltage switching boards are also quite expensive. So I built my own hardware and the Processing.org language is an easy way to test things. The Processing.org language is a free, open source graphics/media/IO layer on top of Java (as posted previously here).
What follows is the super simple test software written in Processing.org & Java.
I have designed this high voltage, low current power supply for various experiments in systems & synthetic biology. I have cleaned up the design and I am placing the schematic and board layout online below! This circuit outputs up to +1,866VDC at under 1 mA or can be tapped at various points for +622VDC or +933VDC. This is useful for either DIY Biology or institutional research experiments such as:
Below is the schematic; read the full post below for the board layout information. Click on the schematic for the full sized version. The schematic operates in stages, so leaving out or bypassing before the 2nd final stage will yield only +933VDC, and leaving out that stage will yield only +622VDC, etc.
Schematic for the HVPS
Imagine reading these kinds of instructions and performing such a task for a few hours: “Resuspend pelleted bacterial cells in 250 µl Buffer P1 and transfer to a micro-centrifuge tube. Ensure that RNase A has been added to Buffer P1. No cell clumps should be visible after resuspension of the pellet. If LyseBlue reagent has been added to Buffer P1, vigorously shake the buffer bottle to ensure LyseBlue particles are completely dissolved. The bacteria should be resuspended completely by vortexing or pipetting up and down until no cell clumps remain. Add 250 µl Buffer P2 and mix thoroughly by inverting the tube 4–6 times. Mix gently by inverting the tube. Do not vortex, as this will result in…” (The protocol examples used here are from Qiagen’s Miniprep kit, QIAPrep.)
Wait a minute! Isn’t that what robots are for? Unfortunately, programming a bioscience robot to do a task might take half a day or a full day (or more, if it hasn’t been calibrated recently, or needs some equipment moved around). If this task has to be performed 100 or 10,000 times then it is a good idea to use a robot. If it only has to be done twice or 10 times, it may be more trouble than it’s worth. Is there a middle ground here?
If regular English-language biology protocols could be fed directly into a machine, and the machine could learn what to do on it’s own, wouldn’t that be great? What if these biology protocols could be downloaded from the web, from a site like protocol-online.org ? It’s possible! (Within the limited range of tasks that are required in a biology lab, and the limited range of language expected in a biology protocol.)
Biology Protocol Lexical Analyzer converts biology protocols to machine code for a robot or microfluidic system to carry out
The point of this prototype project is this: there are thousands of biology protocols in existence, and biologists won’t quickly transition to learning enough engineering to write automated language themselves (and it is also more effort than should be necessary to use a “easy-to-use GUI” for training a robot). The computer itself should be used to bridge the language gap. Microfluidics automation platforms (Lab on Chip) may be able to carry out the bulk of busy work without excessive “training” required.
Systems biologists and synthetic biologists spend a large amount of time moving small liquids from one vial to another. I would say it makes up the majority of their work day, even in a technologically cutting-edge lab which has robotics. Strange, isn’t it, that the most advanced biological science labs in the world are dependent on a human physically moving small drops of liquid samples and reagents around a lab?
Microfluidics aims to move liquids without humans, under computer control. A small flow of DNA in water, for example, might trace a path between two glass plates, within a tiny, etched microchannel. The movement of the flow is controlled by numerous micro-mechanical valves connected to electronics.
Digital microfluidics aims to move liquids without humans, under computer control, using only single droplets under electrical control: no micro-mechanical valves. It works by using electric fields (electrowetting-on-dielectric properties, abbreviated “EWOD”), which polarize water atoms enough to move a very small water droplet across the surface of a computer board. Droplets on the board can split into two, or join together into one.
Standard PCB etching techniques can be used to make low-tech digital microfluidics devices
October 3, 2007 lecture by Bill Thies for the Stanford University Computer Systems Colloquium (EE 380). Bill Thies provides an overview of microfluidic technologies from a computer science perspective, highlight areas in the which computer science researchers can contribute to this field; he will also describe recent work in developing new architectures, programming languages, and CAD tools for the microfluidic domain.
EE 380 | Computer Systems Colloquium:
http://www.stanford.edu/class/ee380/