Alas, I am a mere bioengineer-in-training, and this blog post is naught else but late.
Anyways, Module 1. I didn't have any particular fervor for drug discovery going into this module, partly because it feels like 99% of such endeavors are futile and partly because everything feels like it it leads to big pharma. But the high-throughput nature of the SMM assay is such a fascinating approach - it's applying engineering principles to biology, which is, after all, what I signed up for as a course 20! So I did enjoy this module quite a lot. In fact, it got me thinking about drug delivery far more than I ever thought I would. Back in 10th grade, my research class teammates and I briefly considered doing our project on Chinese herbal medicines and seeing if they really were as effective as our grandparents insisted they were...if we could just isolate the compounds in them properly for SMM screening and secondary assays, perhaps we could finally bridge the medicinal gap between east and west?
But I digress. As for the data summary: It wasn't too horrible. It was certainly an expedient assignment. But if I have to type out FKBP12 one more time, well...
I was a bit peeved with myself, actually, that it took me so long to finish my part of the data summary. How many papers and lab reports had I written in high school, that I should be so sluggish in hammering out a relatively straightforward account of a small set of experiments? But every experiment poses its own particular set of communication difficulties, and I suppose this is but one more local maxima of work traversed.
A few brief takeaways from the data summary assignment, since that's always useful:
- outline, outline, outline!
- "Figures First" may be a good guiding principle, but we actually ran into the problem of working too much on our figures at the expense of piecing together a proper narrative in our results section. Perhaps "Figures first, and outline results in parallel" would be a healthier approach.
- It's too easy to forget the point of an introduction: to give the reader enough information to not be lost when they hit results and methodologies (which, more and more these days, seem to be placed after results), but not so much as to turn readers away with an inundation of information.
This module posed some novel challenges when it came to data analysis, and it was both fun and frustrating to play around with the data to make sense of it and to get it to display in a meaningful way. I do wish we could have personally done some of the computational work - eternally grateful as I am to Rob, master of the microarray. I think that would have been a particularly transferable skill in managing large data structures, parsing for chemical structure similarity, etc. Though I'm sure I would regret writing this if that actually was part of the assignment.
20.109 is definitely my favorite MIT class so far! Thank you to all the teaching instructors, and to my patient partner, the always lovely and unfailingly sarcastic Rahma.
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