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):
This is exactly why biochemistry is so hard. The smallest cell is a maelstrom of complexity.
It makes sense in my mind that a noisy system will be, on average, operating at something approaching optimality. The noise jogs the system out of local minima and is effectively a form of annealing (in the numerical sense).
Also, great connection to annealing.
Here is an example of the 'genre'
[1 - Harvard MCB : The Inner Life of the Cell ]
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.
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.
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.
EDIT: probably more accurate to call actin "filaments" not tubes, typo
You hear in pop-science how the human body is ~50-70% water as if that is a lot. Really it’s a rather low level of solvent to run chemistry reactions in. I did some undergraduate research in a biochemistry lab and when we would perform an experiment with a protein we were studying, the proteins and other chemicals would always be very dilute, maybe ~99.9999% water or higher. Cells are very “sludgy” and it’s surprising to me that anything works at all the proteins don’t just immeadietly stick together and precipitate out of solution like egg-whites when cooked.
It’s a tricky number to estimate because the orders of magnitude are so stark. Proteins are at very, very low concentrations but each protein is made of hundreds or thousands of atoms. The number of protein molecules is small but the they are almost big enough that their volume is not insignificant. There are other things that are unintuitive about chemical reactions. A lot of chemical reactions are faster than the movement of molecules. A protein maybe able to catalyze a chemical reaction faster than it can diffuse through water so it sits around most of the time waiting to collide with the molecule it will catalyze. Basically like a CPU waiting an eternity for data from a “fast” SSD to show up.
For reference, gelatin is about 85% water, and still manages to be a solid glob you can balance utensils on top of.
0: The stuff I had on hand is 20g powder to 118g water.
Realistic animation: http://www.cellimagelibrary.org/images/28234
More modern pictures are like so: http://www.conte.harvard.edu/news/2015/8/18/imaging-the-brai...
> how insanely chaotic a cell is
"how insanely high a cell's spatial and temporal entropy is"
And instead of:
> denoising the chaos.
> decompressing (interpreting) the spatio-temporal activity
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.
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.
 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.
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.