My Mod 1 Experience: A Journey in Bloopers

As 109ers, I think we can all agree that the only thing harder than finding a chemical probe for FKBP12 is trying to say "peptidyl prolyl cis-trans isomerase" without stuttering. If, like me, you've spent the past four hours attempting to record a comprehensible mini-presentation, you may relate to this blog post on a spiritual level. If not, perhaps you'll be able to relate to the challenges and victories I've faced thus far in 20.109.


When I first walked into 20.109, I was an optimistic young (future) engineer, eager to learn new lab techniques and generate significant data.
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One thing I quickly realized was that 20.109 is not a class filled with "cookie cutter labs." Sometimes we're going to get unexpected results, and often we'll be left to somehow explain those results. Like when our SDS-PAGE had a single empty lane that just happened to be the only one that actually mattered. Oops. But learning to explain these results is an important part of being a scientist.

Another important part of being a scientist is learning to figure out what exactly your data is telling you. No matter how confusing or how much of it there is...

Data analysis can often be overwhelming at first, but if you focus on the questions you're trying to answer, you can determine which comparisons will be most valuable to make. When I sat down to start the data summary, I was a bit overwhelmed. I think I wrote down and deleted a lot of irrelevant thoughts. I realized after a while that it was because I wasn't focusing on the overall "story" that I was trying to tell. So I stepped back and asked myself what conclusions could be made from the data we had. Once my lab partner and I had decided which conclusion was best supported by our data, I used that as a guiding-point for what to include in the data summary (fun-fact: you can't include EVERYTHING).
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On the topic of data summaries, this module taught me so much more than I expected. From being concise to focusing on the important stuff--I developed a lot of skills that I didn't have before (also skills that still really need work).
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At the end of the day, my biggest take away from this module was the reminder that we must always be honest about the data we gather. It's not that I've considered lying about data before, it's just that there's a big difference between saying "be honest" and actually sitting down to write a data summary about how none of your data is significant.
In times like these, it's important to remember that other researchers can learn our mistakes. "Failed" experiments are as important as successful ones, and if more researchers would publish these results, a great deal of time would probably be saved trying experiments that have failed before.
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Despite these setbacks, 20.109 has thus far been one of the most enjoyable classes I've had the pleasure of taking at MIT. Its relevance to our future careers is indisputable, and the opportunity to pipette things and make cool graphs is (infinitely) more fun than memorizing the process of malonic ester synthesis (can we all just take a moment to appreciate the fact that 5.12 is OVER???). Every day I feel as if I'm becoming a more capable researcher. In my UROP, it's fairly easy to come into lab and do the experiments my supervisor asks of me--this is totally different than carrying out a project from start to finish and then summarizing results in a way that is comprehensible to other people. Although 20.109 is a very structured class, and help is always available from the (incredible) course faculty, the class has still given a sense of responsibility and confidence that I'll carry with me to future classes and research positions.

-Jenna Melanson



Honorable mention bloopers:
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