↑ comment by sarahconstantin ·
2017-12-05T05:37:02.520Z · LW(p) · GW(p)
So, in general, as a techy person looking at biology, you need to be aware that most biomedical researchers are not educated in quantitative stuff. Like, when I worked at a biotech company, I got frequent questions from the bench biologists that amounted to "how do I test statistical significance in this experiment?" where the answer was "do a t-test."
This means that in any arbitrary field, you're not necessarily going to find that someone has done the "obvious" applied-math/modeling thing.
Some fields, like genetics or epidemiology or systems biology, have a tradition of collaborating with statisticians or machine-learning people, so the "obvious" stuff will have been tried.
In a lot of fields, you can't do the "obvious" thing because the data is really expensive to collect. (Measuring how much of each protein is in a sample? Really expensive if you have more than a handful of proteins to test.)
But sometimes, like with hormone dynamics, I'm genuinely puzzled as to why someone couldn't just do it.
Another example is graph theory for various biochemical networks -- metabolic, gene regulatory, etc. Have we looked at connected components, measures of centrality, Rayleigh quotients, etc in attempts to find "master switches"? Surprisingly rarely even when we have the data.
"Systems thinking" is, as far as I can tell, simply "the ability to ask oneself whether one is dealing with a system of differential equations" and it is way rarer than I would guess. I'm not sure why -- maybe just the fact that most people have very little math education.Replies from: ChristianKl
↑ comment by ChristianKl ·
2018-01-01T11:32:10.947Z · LW(p) · GW(p)
We had a physiology class in my Bioinformatics studies that approached physiology from a systems perspective instead of the usual approach. Unfortunately there was no textbook that our professor could use.