Learning to walk before you run 🏃♀️
Hi there!
When I first tried using automation as an intern at Synthace, I got excited.
I wanted to learn all the ways it could save me time, and I couldn’t wait to stop using manual pipettes every day.
But I was too ambitious.
I chose a difficult starter protocol to automate: The preparation of competent E. coli cells, and their subsequent transformation.
At the time, my choice seemed logical.
After all, it was a protocol I'd run manually multiple times in the lab. I thought that by delegating the tedious aliquoting of cells to robots, I would save myself time (and sanity).
But the process turned out to be more time-consuming than I'd hoped.
Back then, I didn't know how difficult cell-based experiments are to automate. I had to do extensive amounts of testing and optimization to handle the competent cells gently enough. I also had to keep the cells cold—which is hard to do on-deck over extended periods of time.
After a month of hitting one hurdle after another, my excitement faded. Time was ticking, I became impatient, and I found myself reaching for those manual pipettes once again.
This made me wonder, when you’re constantly under pressure to deliver, how should you start learning to automate? And when should you accept that sticking to doing things manually is best?
My first failed attempt at automating came down to limited expertise.
I thought I was being smart by picking a protocol that I was familiar with. But in hindsight, I learned that I should’ve started with a simpler process and built my knowledge step by step.
That way, experiences from automating easier experiments could become building blocks—information I could use and assemble to automate more ambitious experiments, and save myself time in the long run.
Automation has huge potential to save time, improve reproducibility, and enable more complex experimentation.
But you can rarely realize that potential immediately. Ultimately, doing things differently comes with risk. It means putting in extra effort in the short term.
It can be tempting either to try and do everything at once, or feel reluctant to adopt a new way of working when the old way worked just fine.
While I was guilty of doing the former, I still got value out of my experience. I applied the principles I'd learned, such as the liquid handling optimization, to my future cell-based assays.
But next time around, I remembered to learn to walk, before trying to run.
Speak to you soon,
👀 Markus and Phil's first podcast episode dropped
We get why Markus felt self-conscious about releasing episode 1 of The Next Experiment podcast (we're glad that newsletters don't involve cameras). But we're so happy that he and Phil got out of their comfort zone and did it—because we loved it. If you've got this sense that there's a better way to do experiments in biology, it's a must-watch.
🪦 RIP, Nathan's plant experiment
Thankfully, Nathan Hardingham's first botched attempt at growing plant cuttings using Design of Experiments didn't put him off gardening. But as stats + planting guru Phil Kay commented, plant cuttings is "serious" gardening. Yet another case where resisting temptation to be overly ambitious and starting small is best.
🤯 Did someone just say a 1000+ run DOE?
Our resident automation specialist Daniel Yip sure did. With a capable device like the Echo, you can scale your DOE workflows by executing thousands of runs with final volumes as low as 5uL—and explore an entire design space in one go.
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Field Application Scientist II at Synthace
1moWise words, Emily! Investing in small steps to creating a strong foundation will pay off in the long run. 🏃♀️