
Cells seem to decode their fate through optimal information processing - Errorcod3
https://www.quantamagazine.org/the-math-that-tells-cells-what-they-are-20190313/
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entee
This is super cool! People underestimate how insanely chaotic a cell is at a
molecular level. Often diagrams show blobs cleanly interacting but the reality
is more like the images linked here:

[https://mgl.scripps.edu/people/goodsell/illustration/public/](https://mgl.scripps.edu/people/goodsell/illustration/public/)

Everything touches everything. Everything is always moving around.

This work suggests that the cell has taken advantage of this enormous
challenge. If everything is always in motion, that means you have trouble
controlling things, but that gives you a chance to maximally sample your
environment. This makes this sort of efficient data processing possible.
Downstream are a number of mechanisms that help make sense of that signaling,
denoising the chaos. One example from a lab I worked in briefly (old but still
cool):

[https://www.sciencedirect.com/science/article/pii/S009286741...](https://www.sciencedirect.com/science/article/pii/S0092867411002431)

[https://www.ncbi.nlm.nih.gov/pubmed/18599789](https://www.ncbi.nlm.nih.gov/pubmed/18599789)

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jmole
Awesome images, thanks for sharing. I wish they'd have put these in science
books growing up.

Are these to scale? It's interesting to see how much more particular (as in,
composed of large particles) these cells look compared to the illustrations
I'd seen growing up.

I always imagined the proteins that made up a cell wall would be more on the
order of how grass looks to a human, in terms of size respective to the cell.

Or another analogy, I used to think of a cell like a room (the cell membrane)
with furniture in it (the organelles). Now it seems more like just a big pile
of furniture.

~~~
entee
I believe the components are indeed drawn to scale! The cell is a mess, there
is virtually no central coordination, just individual molecular signals. A
protein gets phosphorylated by a receptor, then its on its merry way till it
hits something it interacts with and maybe because it's phosphorylated it
activates that thing to do something else, or maybe, luck of the draw it
doesn't. It's amazing that it even works, but it does so because billions of
years have shaped life to be resilient to chaos. Some context:

Unicellular life is roughly 3.5B years old

Eukaryotic life is roughly 2B years old. It took 1.5B years to figure out how
to build and use organelles

Multicellular life is roughly 1B years old. Another billion years to figure
out how to coordinate and specialize cells.

Sea sponges, essentially "animals" are 700M years old

Animals that can move around are about 500-600M years old, backbones in
animals show up around the same time, then we're off to the races.

This sort of patterning is maybe in the 700-800M year old window. It took
billions of years for life to figure this out, you can do a lot by evolving
that long.

[https://www.newscientist.com/article/dn17453-timeline-the-
ev...](https://www.newscientist.com/article/dn17453-timeline-the-evolution-of-
life/)

~~~
est31
> A protein gets phosphorylated by a receptor, then its on its merry way till
> it hits something it interacts with and maybe because it's phosphorylated it
> activates that thing to do something else, or maybe, luck of the draw it
> doesn't.

There are proteins that spread via diffusion, but there is also transport
along microtubules in transport vesicles. Not entirely sure how the
routing/navigation question is solved though, aka how does some vesicle know
it needs to go to the nucleus vs golgi apparatus.

~~~
entee
Oh yes, of course there are many motorized processes, but the point is there's
no inherent logic to them. A myosin grabs onto a filament and goes to the plus
end of the actin filament (usually, not some of them, again it gets
complicated :) but it doesn't know where that tube is going. It'll just
happily move to the end and fall off/get blocked. If the destination "wants"
or "needs" supra-diffusion transport, it needs to recruit the tubes to come to
it. Every entity in the cell essentially acts as its own agent, and it's only
through interactions that it does one thing or another.

EDIT: probably more accurate to call actin "filaments" not tubes, typo

~~~
rrock
Actually, there’s quite a bit of inherent logic to actomyosin traffic
networks, as we’re now beginning to discover. For example, myosin 10 has
evolved to walk along specific bundles of actin, but not so well along single
actin filaments. The bundle has mechanical integrity and leads to a distant
location in the cell, and the myosin 10 can detect that the actin is bundled.
So in some sense it knows that it is walking along the right structure that
leads to that distant location. There are some other examples, but this is all
at the cutting edge of motility research.

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dannykwells
I spent some time in the Gregor lab during grad school. I'm obviously biased
but I think the work represents some of the most original happening right now
in biophysics. These papers were extremely rewarding to read and represent
almost a decade of work on part of many members of the lab.

For those interested, I recommend diving into some of the lab's earlier work
as well as the work of Bill Bialek, Thomas's advisor, who formulated a lot of
these theories for photon sensing in the eye decades ago.

~~~
selimthegrim
Have you got any references for Bialek’s theory of photon sensing?

~~~
d136o
Bialek is an amazing explainer, you'll be left convinced it's all so simple
and straightforward. He wrote a book and taught a graduate level class around
it, you'll find a draft of the book available at [1]

Chapter 1 cover photon counting:

``` 1\. Photon counting in vision (Lectures W 8 Feb through W 22 Feb 2012)

In this Chapter, we will see that humans (and other animals) can detect the
arrival of individual photons at the retina. Tracing through the many steps
from photon arrival to perception we will see a sampling of the physics
problems posed by biological systems, ranging from the dynamics of single
molecules through amplification and adaptation in biochemical reaction
networks, coding and computation in neural networks, all the way to learning
and cognition. For photon counting some of these problems are solved, but even
in this well studied case many problems are open and ripe for new theoretical
and experimental work. The problem of photon counting also introduces us to
methods and concepts of much broader applicability. We begin by exploring the
phenomenology, aiming at the formulation of the key physics problems. By the
end of the Chapter I hope to have formulated an approach to the exploration of
biological systems more generally, and identified some of the larger questions
that will occupy us in Chapters to come.

```

[1]
[http://www.princeton.edu/~wbialek/PHY562.html](http://www.princeton.edu/~wbialek/PHY562.html)

~~~
selimthegrim
Thank you immensely!

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dr_dshiv
These biological computers are intrinsically based on vibrations. The
vibrations aren't just a source of diffusion and Brownian motion; they cohere
into meaningful harmonic structures. Alan Turing described morphogenesis in
terms of inhibition and excitation loops, which gives rise to banding patterns
due to oscilatory harmonics and resonances [1]. We are so accustomed to
thinking about things in terms of discrete, separable parts, we have a hard
time imagining emergent temporal structures. Living organisms, from cells to
brains to cities, are composed of interacting waves and harmonic structures.
(I'm emphasizing a hippie-style "resonance and harmony" language here because
it really is so critical for understanding these systems.

[1] Yang, L., Dolnik, M., Zhabotinsky, A. M., & Epstein, I. R. (2002). Spatial
resonances and superposition patterns in a reaction-diffusion model with
interacting Turing modes. Physical review letters, 88(20), 208303.

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j7ake
William Bialek is my favorite scientific speaker (he has some good ones on
youtube). His depth in such a wide range of sciences and topics is remarkable.

I think Thomas Gregor has some of the most precise biological measurements at
the single molecular level.

The combination of the theory and precision measurements in studying the fly
embryo by these people have resulted in very unique and creative progress in
the field. From what I hear when they first started this work, the old-school
developmental biologists thought what they were doing was absurd. They have
successfully put a much more quantitative perspective back into biology.

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carapace
This reminds me of the work over at Levin lab.

[https://ase.tufts.edu/biology/labs/levin/](https://ase.tufts.edu/biology/labs/levin/)

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MetaMonk
Is there an information equivalent to gravity, e.g. some sort of gradient is
formed that the cell simply follows like a bowling ball on a sheet?

