https://www.youtube.com/watch?v=zpIqQ0pGs1E
https://www.youtube.com/watch?v=jPhvic-eqbc
Referenced paper: https://philpapers.org/archive/NICITC.pdf
The author's notes on genetics: https://www.subanima.org/mendel/
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.
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.
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.
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.
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.
The one about genetics seems to force conclusions that are palatable to modern sensibilities. How does Mendel accidentally fudging his results using eugenics prohibit “talk of good genes and bad genes”? There clearly are good and bad genes, this is obvious to anyone. It’s practically the whole point of the olympics to showcase people who have the best genetic prerequisites for their discipline. None of the technical minutiae of inheritance prevent us from breeding better sprinters. In fact we’ve done it with several animals. Whether or not any of this is ethical is not the field of biology’s concern.
The video about cells has a similar problem, where it acts like it wants to invalidate higher levels of abstraction because reality is more complicated. Of course biology doesn’t operate like a designed and manufactured apparatus, but omitting complexities irrelevant to the question at hand is the entire point of making diagrams. Does the thing turn methane into methanol? Yes, it does. There is no problem here. These could be great educational videos if they weren’t strawmanning so hard. This is especially obvious in his carefully designed definition of a machine.
Biology is just stupidly stochastic, but this stuff is akin to “nooooo, systems don’t »prefer« low energy states, they’re not conscious qq”. Like, okay, use a different verb if that makes you happy??
Saying Gene's aren't machines, because Machines are rigid bodies?? That is wrong, that seems to be the crux of his arguments, that gene's flex, and can become different shapes. Hello, machines do this.
My fishing rod is flexible. And I can cast over hand, or underhand, or side arm it under some trees.
> First, machines can be described in terms of a list of parts and a blueprint indicating how those parts fit together, meaning that someone who has never seen a particular kind of machine should in principle be able to assemble any number of copies—each virtually identical in appearance and performance—provided they can consult the machine’s design specifications. Second, as machines are designed to perform highly specific functions, their operation is tightly constrained, which is why it is possible to predict and control their behaviour. Third, machines are highly efficient in what they do because they always follow the exact same sequence of steps in every cycle of their operation. And fourth, the operation of machines is not continuous; their functioning can be interrupted and their parts examined without thereby jeopardizing their structural integrity.
And argues that cells look more like this:
> according to this alternative view, the cellular architecture is regarded as a fluid, self-organizing process; protein complexes are considered to be transient, pleomorphic ensembles; intracellular transport is deemed to result from the harnessing of Brownian motion; and cellular behaviour is viewed as a probabilistic affair, subject to constant stochastic fluctuations.
And
> Viewing the cell as a machine allows us to think of its organization in terms of modular, solid-state circuits that can be approached reductionistically, and it also gives us the confidence to expect that when we eventually work out how all of the cell’s parts fit together, we will be able to completely predict its behaviour. If, on the other hand, we view the cell as a highly integrated, self-organizing, fluid system composed of densely interconnected processes ever-subject to stochastic fluctuations, we no longer have reasons to suppose that achieving such epistemic goals is even possible, let alone feasible. The stark contrast between these two outlooks is exemplified by their strikingly different ways of understanding causation in the cell (Bizzarri et al., 2019), and it serves to explain why some researchers find it easier than others to obtain funding and publish their work.
But just looking at mitochondria as an example, and ATP transport. That looks very repeatable, machine like. It isn't like mitochondria just suddenly change function, or form out of random goo and change shape and function, they are pretty much transporting ATP. That is what they do. It is highly regular and repeatable and built from a blue print.
The self regulating goo is nice idea, I think using it to say we aren't machines is the stretch.
> this conclusion does not imply that every entity or process within the cell is being (or needs to be) reconceptualized. To be clear, the thesis I have sought to defend is not that all organelles exist as irreversible steady states, that every protein complex is a pleomorphic ensemble, and so on. It is rather that a very large number of cellular and molecular phenomena that were traditionally interpreted in terms that support the MCC are now being explained in terms that directly oppose it.
Where's my medal?
Computational or mechanical analogies are not totally wrong but they're dangerously over-reductive.
I never used to understand this position, until I saw right wing snowflakes getting triggered over a woman looking too manly at the Olympics. They confidently explained XX and XY to whomever would read their little meltdowns, and even rode back on their usual line that genitals determine gender - suddenly they care about hormone levels.
So I'm having to come around to the idea that over-reductive can be a real thing. Although I'm not sure that the sort of dumdum that dive off the deep end with these things would do any better.
A biology professor of mine put it this way: pretend you are reverse engineering alien technology. The stuff you are trying to understand was not designed by human minds. It was designed by evolution over billions of years, literally an alien intelligence that doesn't "think" anything like you do.
A big difference between biology and human engineering is that humans like one-part-one-function and linear chains of cause and effect. Biology isn't like that at all. It's an analog causal matrix where everything affects everything else to varying degrees and most parts have multiple simultaneous functions.
With biology, the code is running against nature, so the instruction set includes assumptions like "but if these two get close enough, then this other reaction occurs first". I suspect it can get so complicated that the only way to find out what it does is to run it.