It‘s an evolved machine, meaning that the parts are not designed to be easily understandable or maintainable or to develop or even necessarily particularly easy to evolve.
But the videos are good, thank you.
But the videos are good, thank you.
> recent research on the cellular architecture demands that we look more carefully at what we have previously assumed were well-defined structures and reconsider them as stabilized processes. Because processes are temporally extended, it follows that they can only be understood by giving time due consideration... The structure of a machine, after all, can be grasped in abstraction from time (as it is not constantly changing), whereas the structure of, say, a whirlpool or a stream cannot. This explains why, when we have started using techniques that allow us to examine the cellular architecture in real time, we have found that many of the cell’s compartments and organelles are not fixed machineries at all, but stable macromolecular fluxes.
That the „parts“ do not only perform a specific function is not really surprising, because the machine does not need to be comprehensible or understandable. I would expect single-purpose parts only if that helps the function or if it helps evolvability (in the sense of not creating dead ends).
> The variable flickering of transcriptional activity in different cells is one of the major causes of heterogeneity in isogenic populations. But where exactly does this cell-to-cell variability in transcriptional activity come from? The answer becomes apparent when we remember that gene expression is a molecular process, and like all molecular processes, it is inherently stochastic, given that it takes place in an environment that is subject to the chaotic dynamics of Brownian motion. Each step in the process relies on fortuitous encounters between molecules that are randomly moving about as a consequence of thermal agitation. Evidently, these molecules must be at the right place and at the right time—not to mention in the right vibrational state—for them to be able to participate in the appropriate reactions. The unpredictability of the whole process is further amplified by the fact that the participating molecules in each step are present in the cell in very low copy-numbers, as this decreases the chances of successful interactions between them...
> as research into the non-genetic heterogeneity of cells continues, evidence for the biological importance of this phenomenon is mounting ever-rapidly. We now know that non-genetic heterogeneity plays key roles in both microbial and eukaryotic cells, in embryonic development, and in evolution. For one thing, it is a crucial generator of phenotypic diversity, which enables cell populations to adapt rapidly to changing environmental conditions. It does so by permitting the implementation of probabilistic diversification strategies within a population, such as bet-hedging and divisions of labour, which can confer considerable fitness advantages...
> One very important theoretical implication of the probabilistic nature of cellular behaviour and the observed heterogeneity of cell populations is that, quite literally, every cell (in an organism and elsewhere) is a unique entity. No two cells are identical, given that no two cells respond to a stimulus in the exact same way—even if they are genetically the same.
He had located the data he was working on
near the top of memory ---
the largest locations the instructions could address ---
so, after the last datum was handled,
incrementing the instruction address
would make it overflow.
The carry would add one to the
operation code, changing it to the next one in the instruction set:
a jump instruction.
Sure enough, the next program instruction was
in address location zero,
and the program went happily on its way.
Anyway the system call trap examined the caller stack, found the return address (to the trap table), picked out the combination of segment and offset used to construct a system-call index. Following so far?
We went paged. Changed syscall segment to indirect, 16 segment descriptors all mapped to the start of the jump table. Started failing on one particular system call out of thousands - it would page fault! Why?
The auto-increment and prefetch of the trap opcode in the jump table would fault, for only one syscall, the one that mapped to the last entry in the trap table where the offset came out to 0FFC. Wrapped to 1000, which with that particular segment meant next-page! Which was unmapped (system trap table was in low memory, alone).
My solution: change the syscall index for that operation to something else, and document in the syscall comments to never use it.
Computers don't behave this way at all: you can be reliably sure that if two computers produce different results with the same input that one of them is wrong. If it's a stochastic algorithm at least the averages should be within a certain bound. You can't make the same conclusion with cells.
> No two cells are identical, given that no two cells respond to a stimulus in the exact same way—even if they are genetically the same.
That's just begging the question.
Do you really think modern computers are fully deterministic at the lowest levels?
But cells don't operate on such nicely bifurcated scales (small and random vs large and mostly predictable). If they did, the dream of cellular wiring diagrams allowing cells to be understood would be a lot more achievable.
Compare to loggers, floating trimmed trees down a river to the sawmill: the logs bouncing down the river are behaving in an entirely random manner, but it's within bounds and doesn't matter, because they'll still make it downstream where they get caught by another system. It's a "machine" with random behaviors. Maybe this (obligatory) xkcd illustrates my point: https://www.explainxkcd.com/wiki/index.php/2916:_Machine
I get the impression that what this is coming down to is massive complexity rendering the machine incomprehensible - but again, that doesn't mean it isn't a machine.
I believe that summarizes transistors, cells, and forest-to-sawmill machines.
Which is to say, yes: it is a system composed of comprehensible systems, which makes it, itself, a comprehensible system. And that's the whole point here: given knowledge and time, we can tease out exactly how it works.
I didn‘t say every single molecule in the cell acts predictably. I said that the cell „performs a task it should perform and does so using the same approach across all instances.“
That doesn't seem like they're "using the same approach over all instances" to me.
Wait until you hear about quantum effects in microprocessors!
I think when somebody says "machine", a lot of people just think victorian steam engines. Machines can cover a lot more. In fact in the future we will build devices that do work similar to what's going on in the cells, and these devices will have to obey the same laws of physics - they too will wiggle and randomly do weird things, but on average, they will do what we intend for them to do.
A machine can be arbitrarily complicated.
We designed computers to manage the quantum and thermal noise "at the bottom" so we can mostly ignore it. We go to incredible lengths to make the constituent parts of our machines predictable, and when they aren't, we layer error correction on top. Cells usually don't do that: noise is inherently part of their behavior and isn't suppressed nearly as much as was often assumed.
The fact is that an amoeba cell behaves in a certain way for all amoebas. It does not go around and behave like a stone or like a drop of olive oil or even like a cell of some random bacterium.
It has very clear and obvious functions and the mechanisms inside the cell are the reason it consistently shows amoeba behavior.
I'd hate to break it to DNA correction machinery.