Smart collaborations beget smart solutions
August 7, 2015 Editor 0
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Following the Synthetic Biology Leadership Excellence Accelerator Program (LEAP) showcase, I met with fellows Mackenzie Cowell, co-founder of DIYbio.org, and Edward Perello, co-founder of Desktop Genetics. Cowell and Perello both wanted to know what processes in laboratory research are inefficient and how we can eliminate or optimize them.
One solution we’re finding promising is pairing software developers and hardware engineers with biologists in academic labs or biotech companies to engineer small fixes, which could result in monumental increases in research productivity.
An example of an inefficient lab process that has yet to be automated is fruit fly — Drosophila — manipulation. Drosophila handling and maintenance is laborious, and Dave Zucker and Matt Zucker from Flysorter are developing a technology using computer vision and machine learning software to automate these manual tasks; the team is currently engineering prototypes. This is a perfect example of engineers developing a technology to automate a completely manual and extremely tedious laboratory task. Check them out, and stay tuned for an article from them in the October issue of BioCoder.
The current issue of BioCoder highlights examples of collaborations between individuals with complementary expertise to provide solutions for problems across diverse areas of biological research. Examples include Biomeme, a smartphone-based diagnostic for the on-site detection of DNA; ABioBot, a smart robot using vision, sensing, and feedback to automate encodable laboratory experiments; and the Pelling Lab, which offers readers information on open source biomaterials as well as a tutorial on how to make your own scaffolds for tissue engineering, sprinkled with some excellent hardware hacks. These projects were developed through collaborations between hardware engineers, software engineers, and biologists.
This leads me to a query for biology researchers: what laboratory processes cause you distress? Is it your data analysis tool (or lack thereof), experiment design, expensive reagents, tracking of experiment information, a tedious technique that could benefit from a little automation, or a piece of hardware that could use some customization? Tweet specific examples to Mac Cowell, Edward Perello, or Nina DiPrimio. We want to hear from you.
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