When I started my master’s program in bioinformatics and genomics, I only had a view of computational biology and bioinformatics from the outside looking in. The program and my later experience at Illumina would push me towards genomics, but at this point I had a very shallow understanding of the field and envisioned myself working in any number of sub-areas. One kind of scary thought is that in the summer of 2014 I was starting to look for companies that I was excited about to try to find internships in and I came across Theranos. Luckily, I didn’t even end up applying there and I am grateful that I got to work at Illumina.
Some other companies I looked at were (and are) involved in automated science. A couple specific companies that I found exciting were Transcriptic and Emerald Cloud Lab. Both of these companies have built robotic labs that can conduct assays and expose an API for you to automate other experiments. I find this really exciting, but I know that automating benchwork is a hard problem and that in many (most?) cases the robots can’t produce results of the same quality as a skilled human, even when that human is involved in the process of fine-tuning the robot.
Regardless, I can imagine a future where I, as someone unskilled and untrained in benchwork, can write up a protocol (or many protocols) and send them off to be performed. This removes the skilled mental work of a scientist from much of the tedium of benchwork. As a caveat, I know it’s important to be in touch with your experiments so that you can troubleshoot and investigate strange data, but this seems like it can be a powerful tool for routine experiments.
There’s also some work towards getting robotics in your own lab. There’s a startup called OpenTrons that builds pipetting robots, and I guess they are supposed to be drastically cheaper than the current alternatives, like Hamilton.
On a somewhat related note with regards to scientific instruments there are also open-source scientific instruments. There’s the OpenPCR instrument and there are groups that have open-sourced their instruments, for example.
As a final note, there’s a cool new master’s program in automated science at Carnegie Mellon that is meant to train scientists and engineers that are equipped to operate in this environment and that are skilled at using common instruments for automation, design principles for automation, algorithmic methods for experimental design and selection, and methods for data analysis.