
We’re Probably Not Mostly Microbes - jdnier
http://www.theatlantic.com/science/archive/2016/01/youre-probably-not-mostly-microbes/423228/?utm_source=SFFB&amp;single_page=true
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refurb
Non-scientists would be surprised how often this happens in science. Take a
really hard question and some scientist will do their best to come up with an
estimate. That part is fine.

The problem is when other scientists do literature searches, find the "answer"
then base their entire work off of it. Every scientist after that does the
same thing and suddenly a wild-ass-guess becomes fact.

A great example is in epidemeology. I work in biotech and many of our
forecasts are based on epi data. I remember one time when someone questioned a
numbered, so our epi team did some work. The number was the only estimate
available, but it turned out to come from a paper from the 1700's that looked
at the prevalence of this disease in orphan children in some small Frech town.
We would have been better off just guessing.

This happens a _lot_.

~~~
biomcgary
Many, maybe most, of the wilder claims in science (by scientists) are made to
bring attention and funding to a field. Science journalists are often happy to
join in because surprising results sells more papers and ads.

As fundamental discoveries are made, future work tends to be more peripheral,
even if important. For example, microbiomes clearly influence the host
organism, but the discoveries in this field are not on par with understanding
the structure/function of DNA. Germ-free mice are recognizably mice, DNA-free
mice are not.

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Dylan16807
>It’s also debatable if cell totals is a useful metric. After all, Milo's
calculations show that 90 percent of human cells are red blood cells, which
don't contain DNA and aren’t capable of dividing. They’re rather poor excuses
for cells—remove them from the equation and the 10:1 ratio reinstates itself.

So all that and it says the ratio might be perfectly correct when you only
count proper cells. Great.

I want to hear where the 724 trillion number came from.

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mdolon
>Which gives a ratio that can best be expressed as ¯\\_(ツ)_/¯.

A bit unrelated but I found it amusing the author used an emoticon in a
magazine article (a reputable magazine, at that). Even more interesting, if
you search for "I don't know emoticon" the first result on Google is an
article from The Atlantic on the subject:
[http://www.theatlantic.com/technology/archive/2014/05/the-
be...](http://www.theatlantic.com/technology/archive/2014/05/the-best-way-to-
type-__/371351/)

And while facts like the cell to microbe ratio may be useless for most in
terms of practicality (as someone else mentioned), for me they are not only
interesting but instill the same sense of wonder as knowing that there are
~100 billion stars in the Milky Way. Definitely makes you think about the
scale of numbers, if nothing else.

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Rexxar
I don't understand the interest of such discussions about cell count in human
body (scientifically or even philosophically). It seems a meaningless metric
to me.

~~~
robotmagician
I think there are pretty important implications of cell counts.

It is probably more apparent if we look at the brain - it has been pretty
important to evolutionary biology to study not only the whole number of brain
cells but also the proportion that are neurons and the proportions that are
other, supporting cells. Similarly, it matters how many cells there are in a
particular region of the brain when we ask the question - how did this region
evolve to be of this size?

If this piques your curiosity I'd recommend Suzana Herculano-Houzel's work -
either her popular TED talk (
[https://www.ted.com/talks/suzana_herculano_houzel_what_is_so...](https://www.ted.com/talks/suzana_herculano_houzel_what_is_so_special_about_the_human_brain)
) or this kind of paper if you want to take a deep dive (
[http://journal.frontiersin.org/article/10.3389/fnana.2014.00...](http://journal.frontiersin.org/article/10.3389/fnana.2014.00077/abstract)
) << should be open access.

Getting accurate measurements of the numbers of cells gives us a good idea of
the number of divisions required to produce them, and could also be used in
normalizing cancer risk across organisms.

Measurements of _types_ of cells in a tissue give us an idea of the magnitude
of the signaling / secretion events from these cells.

