An excellent description of a recent fundamental discovery. The post might have a bit more jargon than a non-biologist would immediately recognize, but it does convey the complexity and logic of both discovery and validation.
As a computational biologist, I enjoy the parallels between the logic of hypothesis generation and testing in biology and algorithm-development on the CS side. In both areas, asking the right big questions is important. But in other cases, recognizing the importance and application of an otherwise obscure discovery is important, e.g., CRISPR and penicillin or discovery of transformers at Google vs their application at OpenAI.
nehal96 17 days ago [-]
Thank you! I find a lot of articles skip the exact techniques used, and usually say something generic like "Scientists ran many tests to confirm..", so I intentionally specified the tests and tried to explain it in simple terms here. The tradeoff is that is can come off a bit jargon-y but I hope that it sparks some curiosity about the process.
You make a great point about the parallels (and differences) of hypothesis generation and testing for wet-lab and CS. There seems to be an ongoing debate about the lack of link between these two fields, leading to some misconceptions -- would be a great topic for an essay!
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As a computational biologist, I enjoy the parallels between the logic of hypothesis generation and testing in biology and algorithm-development on the CS side. In both areas, asking the right big questions is important. But in other cases, recognizing the importance and application of an otherwise obscure discovery is important, e.g., CRISPR and penicillin or discovery of transformers at Google vs their application at OpenAI.
You make a great point about the parallels (and differences) of hypothesis generation and testing for wet-lab and CS. There seems to be an ongoing debate about the lack of link between these two fields, leading to some misconceptions -- would be a great topic for an essay!