Female reproductive health is poorly understood and poorly treated.
Chronic illnesses which are very common (5-21% of American women have fibroids; 15-20% have PCOS; 3% have endometriosis) and cause real problems (infertility and chronic pain) are frequently incurable or nearly so. We don’t know the causes of any of them.. We don’t know how a lot of pregnancy complications (like pre-eclampsia) happen. Anecdotally, women get their reproductive health issues ignored by doctors very often even when symptoms are severe; “period pain” gets written off as trivial even though sometimes it’s severe.
Female sex hormones fluctuate cyclically, of course, but nobody has done studies measuring hormone levels over time and fitting a differential-equation model of how hormones affect each other’s levels. We don’t really know, on the gears level, what happens when you alter hormone levels. Which may be why women get such extremely varied responses to hormonal contraception; “the pill” is one-size-fits-all, but we don’t understand the normal human variation in hormones well enough to target dose to person.
I’m a big fan of normalizing talk about reproductive health in general.
It shouldn’t be considered “TMI” for a man to hear about menstrual or pregnancy or menopause symptoms.
It should be normal to bring up female-specific (or male-specific) side effects of medication, like whether it makes birth control stop working or causes infertility.
It should be normal to talk about miscarriage or infertility, and to have feelings about that.
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.
While I still think all of the above is more-or-less true, I’ve since learned it’s not the real reason.
The real reason is that we don’t (yet) have a way to noninvasively measure blood hormone levels as they fluctuate throughout the day. You’d have to keep your study subjects hooked up to an IV, or taking many blood tests a day, continuously for at least a month (because of the menstrual cycle). This is unpleasant and maybe unacceptably risky (infections!) What we need is noninvasive continuous hormone monitoring, which is currently at the prototype stage in a couple of university labs.
Finding somebody who can do the math will be easy once the data exists.
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.
Female reproductive health is poorly understood and poorly treated.
Chronic illnesses which are very common (5-21% of American women have fibroids; 15-20% have PCOS; 3% have endometriosis) and cause real problems (infertility and chronic pain) are frequently incurable or nearly so. We don’t know the causes of any of them.. We don’t know how a lot of pregnancy complications (like pre-eclampsia) happen. Anecdotally, women get their reproductive health issues ignored by doctors very often even when symptoms are severe; “period pain” gets written off as trivial even though sometimes it’s severe.
Female sex hormones fluctuate cyclically, of course, but nobody has done studies measuring hormone levels over time and fitting a differential-equation model of how hormones affect each other’s levels. We don’t really know, on the gears level, what happens when you alter hormone levels. Which may be why women get such extremely varied responses to hormonal contraception; “the pill” is one-size-fits-all, but we don’t understand the normal human variation in hormones well enough to target dose to person.
I’m a big fan of normalizing talk about reproductive health in general.
It shouldn’t be considered “TMI” for a man to hear about menstrual or pregnancy or menopause symptoms.
It should be normal to bring up female-specific (or male-specific) side effects of medication, like whether it makes birth control stop working or causes infertility.
It should be normal to talk about miscarriage or infertility, and to have feelings about that.
>nobody has done studies measuring hormone levels over time and fitting a differential-equation model of how hormones affect each other’s levels
What in the everloving fuck? That really seems like the first thing you should do. Has that at least been done for the shared hormones?
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.
While I still think all of the above is more-or-less true, I’ve since learned it’s not the real reason.
The real reason is that we don’t (yet) have a way to noninvasively measure blood hormone levels as they fluctuate throughout the day. You’d have to keep your study subjects hooked up to an IV, or taking many blood tests a day, continuously for at least a month (because of the menstrual cycle). This is unpleasant and maybe unacceptably risky (infections!) What we need is noninvasive continuous hormone monitoring, which is currently at the prototype stage in a couple of university labs.
Finding somebody who can do the math will be easy once the data exists.
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.
Nope! I went looking! Not there!