
The Ferocious Complexity of the Cell - YeGoblynQueenne
https://rbharath.github.io/the-ferocious-complexity-of-the-cell/
======
entee
One of the major misunderstandings even many biologists have about how a cell
works is a complete misconception of how jam-packed and dynamic a living cell
actually is. See the following images for a pretty simulation of what a cell
actually "looks" like on the inside:

[http://mgl.scripps.edu/people/goodsell/books/MoL2figures/Fig...](http://mgl.scripps.edu/people/goodsell/books/MoL2figures/Figure8.2-reduced.jpg)

Webpage with more info:

[http://occamstypewriter.org/scurry/2010/08/30/the_crowded_ce...](http://occamstypewriter.org/scurry/2010/08/30/the_crowded_cell/)

Physicists/chemists often make fun of biology as "blob-logy" because we tend
to draw biological systems as Blob-1 interacting with Blob-2 which leads to a
change in Blob-3. It's a very useful abstraction, but it's also limited. In
reality all these blobs are jammed together and moving around, creating a lot
of random noise in the system. This is because every component in a cell is
working at nano-scale (proteins are to a 1st approximation about 5nm in
diameter). The rules are different at that scale, we don't experience anything
like them in our daily lives.

In other words, the components of the cell aren't doing chemistry at a bench,
passing the results of one reaction to the next reaction, it's more like doing
chemistry in a mosh pit.

Biology has developed coping mechanisms for this insanity, but analogies drawn
with things like computer processors and macro-world items are going to be
somewhat wrong because of the inherent differences in scale.

~~~
rhcom2
Here's a list of "Molecule of the Month" images from David S. Goodsell. Some
great looking images.

[http://pdb101.rcsb.org/motm/motm-image-
download](http://pdb101.rcsb.org/motm/motm-image-download)

------
adrianN
This article seems to equate "exhaustively understand" with "can be simulated
down to an atomic level". I think that's not a good way to go about it. A cube
of silicon with 1cm side length has about 10^20 atoms. Storing one bit for
each atom would use exabytes of memory. Luckily, we don't have to simulate the
cube at that level of precision to get a pretty good idea about how it
behaves.

The "we could just simulate the whole brain down to the quarks" argument is an
argument for the existence of AI. Nobody seriously suggests that it's the
practical path forward to achieving it.

~~~
jszymborski
I agree with your general premise, that abstraction is essential and
sufficient for us to understand a lot of the functions and mechanisms of the
cell.

I do, however, think that the author is right in calling the cell ferociously
complex, and that a lot of the abstractions we use hide and gloss over a
chaotic system whose particulars can explain a lot of unexplained or
misunderstood processes. That's fine to help us get an initial understanding,
but you always want to drill down to a lower level abstraction until you
(don't) reach the bottom of the stack of turtles.

Biology undergoes a major paradigm shift couple of decades, and minor paradigm
shifts on an even more regular basis, and I attribute that largely to the
increasing resolution with which we're able to observe things.

Ten years ago, microscopes were shittier, we were using microarrays instead of
RNA/DNA-seq, we weren't using biotinylated-fusion proteins... but that stuff
was necessary for us to really prove in a persuasive way ground-breaking
shifts like that the DNA->Protein->Function paradigm isn't sufficient to
describe a lot of what goes on in a cell.

PREEMPTIVE EDIT: I know that DNA->Protein->Function paradigm was questioned
way earlier than 2007, but it's taken a lot of persuasive evidence since then
using modern techniques to get things like lncRNAs implicated in cancer

~~~
RobertoG
"a lot of the abstractions we use hide and gloss over a chaotic system whose
particulars can explain a lot of unexplained or misunderstood processes."

Is not that always the case with abstractions? In fact, is not their point?

The article says:

"How far is human science from understanding a human brain? [..] Assuming that
each neuron contains 175 trillion atoms, the total brain contains roughly 2 *
10^25 atoms."

Well, by the same logic, we could argue that we don't understand cars.

~~~
amatic
>Well, by the same logic, we could argue that we don't understand cars

Or, by similar logic, that we don't understand how the planets of the solar
system interact. It seems that in the history of science, initial views of
complex things as chaotic got gradually replaced with better abstractions. As
in, the word 'planet' means 'wanderer', then there were epicycles, perfect
circles, elipses... I'm not a biologist, but maybe the current abstractions
and models of cells are waiting for their Newtons and Mendeleevs to figure out
the right models.

~~~
adrianN
The epicycle theory was actually pretty good and explained planet motion to
the precision that measurements of that time provided.

------
nabla9
Even higher level formal approaches are ferociously complex:

Within each cell is very complex gene regulatory network (GRN). GRN behavior
can be modeled as complex stochastic recurrent neural network. Genes are
on/off and they can put other genes on and off with gradients and interaction
of the proteins they produce.

Then there is the functioning of neurons on top of that.

There are even higher level abstractions, like cortical columns and other
structures. It's conceivable that there are 4-7 levels of complex functional
interaction that produces human behaviour and understanding all of those
levels is important.

The brain of C. elegans (roundworm) has been mapped exactly (connectome is
known) and we know it's 302 neurons and 8000 synapses well (it has little over
1000 cells total) but we still can't understand how its primitive brain works.
It don't even have spiking neurons and it's still a mystery.

------
grondilu
It's probably hopeless to try to simulate a cell at the atomic level. Instead,
it seems to me that computational biologists try to build models of cells[1]
where if I understand correctly all occuring molecules are tracked as
concentrations, not as particles. Presumably, possible reactions are
established not by solving the Schrödinger equation but with empiric models
and whatnot.

I don't know how closely such a model can match an actual cell behavior, but I
don't see any reason it can not do so pretty well, while also being
computationally efficient thus doable.

1\.
[https://en.wikipedia.org/wiki/Cellular_model](https://en.wikipedia.org/wiki/Cellular_model)

~~~
letitgo12345
The challenges involved are still insanely tremendous. For ex, correctly
simulating folding a lot of the larger human proteins de novo is still beyond
our capabilities.

~~~
grondilu
It's not exactly the same problem. A cell model will hopefully not have to
know the exact geometric configuration of a protein, as long as it contains a
rich enough collection of possible reactions with other proteins. Determining
those reactions is difficult indeed, but it only has to be done once, and one
can hope it will be done soon with quantum computers.

------
reasonattlm
The complexity of cellular biochemistry is why the most effective near-term
approaches to medical biotechnology involve sidestepping the need to learn
very much more about how it all works in detail. They are engineering rather
than scientific efforts.

This is most apparent in aging research, though the principle applies to most
medicine. The idea is to identify the root cause differences between normal
and diseased metabolism, a task that is a lot easier than figuring our how
exactly those root causes propagate and expand into the disease state over
time. Root causes are those that have no other cause beyond the normal
operation of cells. You then intervene to revert the root cause differences
and see what that does to metrics of disease state and progression. The goal
is to return metabolism to its normal state, a state that we don't fully
understand, but that we know works.

A practical example is the presence of senescent cells. They cause problems,
they are not present in large numbers in young, healthy people, so remove
them. Removing them sidesteps the whole involved process of trying to
understand exactly how they cause problems, in detail, at the level of
cellular mechanisms. Researchers are working on that goal of full knowledge,
but it will be years until that slowly starts to produce useful therapeutic
options, and there are probably decades yet in any research program or
combination of research programs that seeks to alter the behavior of senescent
cells to make them less harmful. Meanwhile, removing senescent cells works
reliably to improve health and turn back disease, and you don't need to know
why that happens in order to obtain the benefits, beyond the high-level sketch
of "less harmful signaling".

Unfortunately most medical research takes an entirely different approach,
which is to start with analysis of the disease state (complex, expensive,
slow) and then work back through proximate causes of disarray. At each point
at which a therapy might be attempted, try to adjust the proximate cause.
Since this doesn't touch on root causes, it is usually marginal, and even the
successes just postpone the inevitable.

------
killjoywashere
For comparison, here are the opinions from some of the leaders in pathology
informatics, coincidentally out today:

Andy Beck (winner of the Camelyon16):
[http://www.archivesofpathology.org/doi/full/10.5858/arpa.201...](http://www.archivesofpathology.org/doi/full/10.5858/arpa.2016-0471-ED)

Alexis Carter (Emory, works mainly on wrangling clinical databases and
generally keeping tech from killing patients):
[http://www.archivesofpathology.org/doi/full/10.5858/arpa.201...](http://www.archivesofpathology.org/doi/full/10.5858/arpa.2016-0593-ED)

------
tboyd47
Here's a thought experiment for all Singularitarians.

If a modern-day Music Man appeared in your town with virtual everlasting life
in a box, and all the miraculous brain-uploading equipment attached, then
started selling tickets and "uploading" your neighbors... how would you be
able to tell whether he's actually migrating their consciousnesses, or just
using a deep network to train _simulations_ of their minds inside the virtual
world while concurrently euthanizing them?

~~~
mattnewton
Not a singularitarian myself but personally, I don't care as long as the
simulation is sufficiently good (waves hands vigorously). Though I would
prefer in the case of a duplicate that I wasn't euthanized.

~~~
vkou
What does it matter to you that a copy of you is uploaded, but _you_ are left
to wither away in meatspace?

That's nice for the copy, I guess, but how will it benefit you?

~~~
mattnewton
Similar to how I want people to remember me, and how I would like children of
mine to live on and prosper. We are all, on some level, replicating machines
who want to organize the world for more of ourselves.

Edit: Even our bodies are completely renewed over time, and any sense of self
similarity that relies on the particular arrangement of matter seems fraught
with problems to me.

------
pc86
Forgive me, quoting the first few sentences of the article:

> _Fifty years ago, the first molecular dynamics papers allowed scientists to
> exhaustively simulate systems with a few dozen atoms for picoseconds. Today,
> due to tremendous gains in computational capability from Moore’s law, and
> due to significant gains in algorithmic sophisticiation from fifty years of
> research, modern scientists can simulate systems with hundreds of thousands
> of atoms for milliseconds at a time. Put another way, scientists today can
> study systems tens of thousands of times larger, for billion of times longer
> than they could fifty years go. The effective reach of physical simulation
> techniques has expanded handleable computational complexity ten-trillion
> fold._

Doesn't setting the initial bar to the _first_ academic paper set it so low
that you'd have massive gains regardless after a few decades?

~~~
SomeStupidPoint
I'm not sure what your point is.

They don't try to tease out whether it was raw computing power or better
algorithms, just that it was tens of trillions of times more computed.

Also, minor nitpicking -- they said _papers_ not _paper_ , likely meaning the
first several years to decade of it.

~~~
alextheparrot
To be fair, they don't try to tease out if it is algorithmic or computational
because they state it was both.

------
j7ake
Lets imagine you can simulate a sperm a cell over it's cell cycle. What do you
want to learn about the sperm after you've got your simulation? After you've
simulated the sperm you have not yet gained any insight towards how the sperm.

Analogously, you don't need to do molecular dynamics simulations of rocket
ships in order to understand how it works. In fact that would be the wrong
abstraction. Similarly an atom by atom simulation of the human body would be
the wrong abstraction to understand physiology.

Computational models need to be useful. I am not convinced this approach to
simulate the cell would be useful.

------
amelius
I think computational efforts could be greatly reduced if we focus only on
protein-protein interactions for any pair of proteins (or a small group of
them). From this, we obtain a functional description of every protein, and
this allows us to perform simulations at a much higher level.

Of course, the number of proteins that can be produced from a given genome is
still very large, and the number of binary/ternary/... interactions is even
larger.

~~~
jfarlow
The trouble is a protein's function is tied to to its solvent, and the small
molecules it interacts with on timescales orders of magnitude smaller than a
protein itself, AND protein complexes up to microns in size can contain
strucutres critical for functionality at time scales orders of magnitude
larger.

There just is no way to accurately simulate across so many orders of magnitude
when the effects both above and below it efficiently impinge on that function.

------
dkarapetyan
> Following the historical example from the previous paragraph, and assuming
> that Moore’s law continues in some form, we are at least 50 years of hard
> research from achieving thorough understanding of the simplest of human
> cells.

Things fall apart here. Moore's law has been dead for a while.

------
jerf
I'm pretty sure none of the futurists who believe in uploading themselves into
a computer expect to do it by completely simulating a brain on the quantum
level. Indeed, that would in some sense miss the point since that would
accurately simulate all the degeneration a human biological brain would
experience, in which case why bother?

Secondly, I think there's an implicit definition that a "full simulation" is
equal to "fully understanding something", but that's not the case. For any
non-trivial process, we say we "understand" it if we have a cognitively-
tractable _simplified_ model that works well enough for our use case.
"Understanding" almost never implies that we have the ability to fully
simulate it. I "understand" quite a lot of computer technologies without the
ability to simply sit down and spew out an implementation of them with no
further thought but my already-existing understanding; nevertheless, I
"understand" them well as evidenced by my ability to harness them thoroughly
and effectively.

And for the other direction, the mere ability to produce vast quantities of
data does not mean that we have "understanding", either. You can give me the
source code for a large simulation [1], say a weather prediction program, and
you can give me the incoming data, and in principle I could manually compute
that entire program one step at a time, but at the end I still might not
"understand" anything I just produced. On its own, even if we were handed a
computer system that did have a full simulation of a neuron in it that still
would not grant us "understanding" of a neuron; it would grant us a _tool_
that we might be able to use to obtain understanding, but it would not itself
be "understanding".

So the literal facts in the essay are interesting, the historical perspective
is interesting, the observation that full simulation of the physical world at
certain significant scales is still dozens of orders of magnitude away is
interesting and relevant, but the conclusion that therefore brain simulation
is impossible and the reasonably-inferred conclusion that we can never
understand these other large structures is logically invalid, due to a
strawman view of what "brain simulation is" and due to a very peculiar and
not-widely-shared implicit definition of "understanding".

[1]: Note I'm not saying _anything_ about the simulation corresponding to
reality today, I'm strictly referring to the terabytes of data the simulation
itself can produce on its own terms.

