How good are our mouse models (psychology, biology, medicine, etc.), ignoring translation into humans, just in terms of understanding mice? (Same question for drosophila.)
post by Ruby · 2021-01-22T20:45:20.469Z · LW · GW · 6 commentsThis is a question post.
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Answers 9 eapache 5 Kamil Pabis None 6 comments
I've been thinking about why some domains have reached more definite, mathematized, and law-like models than others, e.g. the hard sciences of physics and chemistry vs the soft sciences of psychology and political science.
One hypothesis is that it's just much easier to experiment on, say, pendulums than on humans. Experimenting on humans is slow, it's hard to create very controlled conditions, IRB's severely limit what you can do. Theoretically, if I could experiment on a large number of humans for long periods of time without constraints on what I could do (something like Aperture Science?), then I maybe could harden the soft sciences.
A piece of evidence on this would be how good are our mouse models? There are substantially fewer restrictions on mice experiments and resultantly we do a lot of experiments on them (hence the "IN MICE" that ought to be appended to so many scientific result headlines). Maybe translation into humans is poor, and we're generally focused on humans so we feel like there's a lot we don't know, but if you just asked about how well we understood mice, actually we have a lot of really solid knowledge about psychology, metabolism, immune function, political economy, etc.
Can anyone familiar with mouse models in any domain comment on how good the mouse models are vs human models? 1x as good, 5x, 20x? Good = really solid predictions, we feel like we know what's going on, etc.
For that matter, the same question could be asked for drosophila.
Answers
I sent this to a person I know (PhD in biology; has done some work with mouse models though it is not their focus). They said:
Yeah I don’t know... we definitely know more about mouse biology than people biology in a lot of ways. But can we make good predictions in mice? Not for a lot of things - living systems are still very complex and stochastic!
The sad fact is that we do not even understand mice very well. There is this old joke that can be paraphrased like this: if I were a mouse I could be cancer free and live forever, because it is so easy to cure these guys of diseases. As it turns out, however, this is not true. Within my field it was long gospel that caloric restriction (discovered some 100 years ago) can robustly extend mouse lifespan until studies in the last 20 years called this into question.
What the joke gets right is that we understand humans even less than mice. In fact, despite the controversies several interventions are relatively robust in mice when it comes to extending their life and health span (rapamycin, caloric restriction, growth hormone loss) while the evidence in humans is much weaker for these.
Delivering useful drugs, hopefully faster not slower than in the past, despite these issues will be an interesting challenge.
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comment by johnswentworth · 2021-01-24T01:13:51.181Z · LW(p) · GW(p)
Relevant to the thing this question is trying to get at: my impression is that our c. elegans models are dramatically better than mouse/human models. Like, I just googled it real quick, and found that an adult c. elegans has 302 neurons and 56 glial cells. I'm pretty sure those are not population averages; those are exact numbers in normal adult c. elegans. I think I've even heard that we have a decent idea of what each of those neurons does?
Replies from: ChristianKl, Ruby↑ comment by ChristianKl · 2021-01-24T12:01:07.969Z · LW(p) · GW(p)
C. elegans indeed has a fixed number of neurons (and other cells) and that's partly the reason why it gets used as model organism.
There's the OpenWorm project (googling also finds WormSim) that tries to model C. elegans whole neurology. From what I heard from people with domain expertise the model isn't good enough for us to say that we truly understand everything.
comment by Jason Gross (jason-gross) · 2021-01-25T13:40:36.194Z · LW(p) · GW(p)
I think another interesting datapoint is to look at where our hard-science models are inadequate because we haven't managed to run the experiments that we'd need to (even when we know the theory of how to run them). The main areas that I'm aware of are high-energy physics looking for things beyond the standard model (the LHC was an enormous undertaking and I think the next step up in particle accelerators requires building one the size of the moon or something like that), gravity waves (similar issues of scale), and quantum gravity (similar issues + how do you build an experiment to actually safely play with black holes?!) On the other hand, astrophysics manages to do an enormous amount (star composition, expansion rate of the universe, planetary composition) with literally no ability to run experiments and very limited ability to observe. (I think a particularly interesting case was the discovery of dark matter (which we actually still don't have a model for), which we discovered, iirc, by looking at a bunch of stars in the milky way and determining their velocity as a function of distance from the center by (a) looking at which wavelengths of light were missing to determine their velocity away/towards us (the elements that make up a star have very specific wavelengths that they absorb, so we can tell the chemical composition of a star by looking at the pattern of what wavelengths are missing, and we can get velocity/redshift/blueshift by looking at how far off those wavelengths are from what they are in the lab) and (b) picking out stars of colors that we know come only in very specific brightnesses so that we can use apparent brightness to determine how far away the star is, and (c) use it's position in the night sky to determine what vector to use so we can position it relative to the center of the galaxy, and finally (d) notice that the velocity as a function of radius function is very very different from what it would be if the only mass causing gravitational pull were the visible star mass, and then inverting the plot to determine the spatial distribution of this newfound "dark matter". I think it's interesting and cool that there's enough validated shared model built up in astrophysics that you can stick a fancy prism in front of a fancy eye and look at the night sky and from what you see infer facts about how the universe is put together. Is this sort of thing happening in biology?)
Replies from: Rubycomment by gwern · 2021-09-13T18:29:31.148Z · LW(p) · GW(p)
https://www.gwern.net/Replication#animal-studies might be a useful bibliography. It is focused on translation into humans, but a lot of the failure to translate appears to be due to the original animal studies being quite bad in terms of both internal & external validity (ie often just wrong, and when right, doesn't even translate to other strains, much less species).