Responses to some of the common critiques I've gotten: *1. I disagree with your definition of a "machine."* I was deliberately vague on how to define what a machine is. I plucked out two key features (has static + specific parts) purely because this is how the metaphor is being used to do work in biology today. These are both wrong and are actively misleading people, as I explained in the video. Sure, perhaps in the future we could build machines with jiggly, non-specific parts. Perhaps our future machines will even be inspired by biology. Fantastic, but I don't care (at least not in this video). Biologists don’t hear “cell=machine” and think “ah yes you mean a complex, unpredictable, fluid, self-organising, agential machine that we haven’t built before” (well maybe all but two biologists: doi.org/10.3389/fevo.2021.650726 ) All in all, the point of the video was to help us conceptualise the cell more accurately, not get into the metaphysical weeds about what a machine is. There was a long back and forth conversation on Twitter about this point, between some of the philosophers working exactly in this area: twitter.com/evantthompson/status/1581410077831233537?s=46&t=9h3xV4_73Scc6VP5P-4SGw *2. Drew Berry's animations are commonly considered to be very accurate, why did you call them misleading?* In terms of the biology they depict, Drew does a decent job of illustrating DNA replication (minus a few nitpicks about proteins being too static, seeming to appear to 'know' where to go etc.) The scientific animation community has come quite a way from these animations though - here's one of my favourite's depicting the true chaos of the cell (albeit with proteins still a bit too stiff). kzfaq.info/get/bejne/q66Vh7R9s6y4l4k.html Nonetheless, I still call Drew's animations misleading. Not because they're inaccurate but because of how they influence us to think about the cell. We see proteins moving like clockwork and then begin to think that the whole cell behaves that way. Everything must be running on clockwork, with static, specific gear-like pieces. Case in point, the Veritasium comments I put on screen. This is wrong, and should not be the mindset we aim towards. Hence, the animations are misleading. *3. What's your solution then?* No theory will continue to produce knowledge forever. There comes a time when the gold begins to run out. Some may disagree with me on this stating that we seem to be in a 'golden age' of data for biology. I would counter that and say that we still have no idea how to put the data together. And we are no closer to answering the tagline of this channel: what is life? As I have argued in another video (kzfaq.info/get/bejne/d5qpra5enbWrpJc.html ) The problem stems from the fact that organisms embody a very different kind of causality to the type we are used to in physics/mechanicism. Namely, they make themselves. This cannot be captured with the machine metaphor and we need to move onwards to get a better picture of life. Onto what you ask? Well, I hinted at it in the video and perhaps I should have outlined a positive case for an alternative but I wanted to keep it below 10 minutes. So I can only defer to the source material, Dan Nicholson's paper (philpapers.org/archive/NICITC.pdf ) particularly section 6: "The cell is not a machine, but something altogether different-something more interesting yet also more unruly. It is a bounded, self-maintaining, steady-state organization of interconnected and interdependent processes; an integrated, dynamically stable, multi-scale system of conjugated fluxes collectively displaced from thermodynamic equilibrium." There are also many alternate metaphors we could employ e.g. a stream, a vortex, a fire. None of these are perfect either, but they capture the processual nature of organisms that much better. *4. You've completely ignored how successful the machine metaphor has been!* Yes I have, because you can get that from pretty much any other KZfaq biology channel, paper or high-school textbook. Machine talk in biology is everywhere, it needs no introduction. If you’d like to make your own video talking up how good the machine metaphor has been, be my guest. All I am saying is that the “cell=machine” seam is running out of gold. If we acknowledge that reality and begin to look elsewhere, we might just find a whole lot more gold.

Speaking as a budding biochemist, I agree with 90% of this video with the big exception that the pathway maps were made to “make us feel more optimistic about what we can understand.” At least for real scientists, no not at all. We use these maps to chart out what we know to be a subset of known protein interactions, from a much larger set of known and unknown interactions, in order to help designing experiments about particular interactions.

My proteins don’t jiggle, jiggle; they _fold_

As a PhD student in biophysics I constantly use these analogies betweens biological processes and engineering systems, never have I claimed that the cells behaves exactly according to those models, but such analogies are incredibly usefull and allow us to apply shitloads of methods and protocols used for decades in systems engineering to better understand the complexity of living things

the one critique I have is when you said "proteins aren't really solids but more jiggly liquids" this is a misnomer. phases of matter are an emergent macroscopic phenomenon, it emerges from layers of specific structures of molecules. calling proteins, which are singular molecules (admittedly a drastic simplification) a specific state of matter is akin to calling a chemical reaction a specific state of matter, you can't because they are both sub-macroscopic, they come together to form the macroscopic.

Just because its jiggly, multitasking and shapeshifting doesn't mean it isn't machine like. Ironically Veritassium also made a video on soft machines.. and after all its not man made machine, is just machine like metaphorically.

Nice! The metaphor is exactly backwards: living systems aren't complicated machines. Machines are extremely simple mechanical systems. Simple mechanical systems are qualitatively different from complex living systems. Very few people getting engineering degrees are being taught systems theory, so they approach the horse from behind and wonder why it doesn't seem to have any interest in hay.

Hard to say it's unpredictable when the end result is a function.

There's nothing wrong with saying that living organisms are LIKE machines. Metaphors are not meant to describe exactly. That's why they are metaphors. They are used to describe something similar (not exactly the same), to convey some aspect(s) in an imperfect manner. Veritasium need only make a small disclaimer something to the effect of "this is a model of a functioning molecule", and it's fine. Oversimplified, perhaps, but that is usually the case when trying to explain complex topics to a lay audience. And all of this is for a lay audience. We still use Bohr's model eventhough we know electrons don't orbit the nucleus that way. This metaphor doesn't give us a false sense of confidence in how much we know, it dispels a false sense of ignorance in how much we don't know. A lay audience would not have know otherwise, and presumed the scientific community didn't either, unless you happen to be a conspiracist who things they know it all and just aren't telling you. "These animations would be an incredibly useful learning resource for students learning these processes for the first time." Precisely. As for all the comments, this is the same kind of cringe comments you get from creationists. "You think you're nothing more than atoms" or "just a bunch of chemicals". No. We are atoms and chemicals, but more. Not "just", not "nothing more". There is indeed much more. The error is in thinking a narrow explanation from one domain explains the totality. Only the incurious think this way. If researchers in the field are overusing the metaphor inferring more than is valid to use, then thats an issue among researchers using the metaphor. Correct me if I'm wrong but I don't thing any of those researchers are listing a Veratasium video as reference source for their papers. Also, definitions for what constitute a "machine" are our definitions. Like any word, it is subject to change, just as our world does. What happens when we invent "machines" that are not solid, or if we find ways to build them from generic parts. Will you deny "machines" built from lego or erector sets?

From a molecular point of view those types of animations are extremely valuable. In the field, we are all aware that brownian motion and microscopic reversibility are always present. Depicting the overal trend, however, allows us to better understand the process. Of course, they dont depict the full picture, but in most cases this is not needed. Anyway we could say that molecules are so small we cannot directly observe them, therefore, any visual represenration of them is wrong. But we need some level of abstraction to understand and communicate things, don't we? Now about the definition of molecular machines, this term is widely used in academia (it was even awarded a nobel prize in chemistry in 2016). The fact that they are not static doesn't mean that we cannot regard them as machines, but rather a new type of them operating under a different set of rules due to their size. And I think that there is the beuty of these things, we don't limit them to the macroscopic description of machines, but we rather expanded the concept of machines to the molecular level.

This pretty much just proves that the cellular process is just a far more complex machine, one that we don’t fully understand; A machine doesn’t need to be simple or complex it just is a process fulfilling a purpose right?

I see Veritasium as a channel that promotes curiosity in STEM, providing dissectible information about subjects that give viewers a solid foundation to begin building their own research on. I don't expect him to go into the fine details about how each individual protein behaves because, the way I see it, it is now my job to find that information. He sparked my curiosity, I set out to learn more, I watched your video. You provided great information to expand on the points made in the Veritasium video, but to say this is "NOT" how to think about cells is a pretentious statement considering most people only know "mitochondria = powerhouse of the cell."

And to sum up, the proteins and complex molecules in a living organism are just like machine parts in a mechanical object; its just that with organisms your dynamic is based on affinities and reaction rates (chemistry). And again, chemistry is just the visible surface of particle physics.

"Impossible" and "infinite" are strong words.

Werner Heisenberg was the first to point out that there is always some amount of unavoidable blurriness in taking a picture. Most of the time, this is of no consequence. But if you are trying to take a picture of something very very tiny, then it does matter. You can still tell that something very very tiny is dancing, and you can even reckon how much energy is wrapped up in the dance routine, but you can't extract the fine details of the choreography.

So it sounds like cells are basically tiny, wonderfully complicated machines.

It's actually interesting looking at the proteins move because this really shows you how temperature is so important to enzymes and why most temperature-hardened enzymes physically look tougher.

While I do appreciate a critical view of science communication, this video seems to avoid engaging with the reason why models like this exist in the first place. They simply give us the best chance of making useful conclusions. If there is a superior model for something scientists will generally trend towards using it. By stating only “here is where all of the scientific models have failed.” This video seems to beg the question: “Maybe we should stop trying to understand things?” Kind of a suspiciously vague take imo. That being said, I do want to thank you for putting together a video about your thoughts, as it was well polished and brought up some interesting ideas.

The definition of machine according to the dictionary: "A machine is a physical system using power to apply forces and control movement to perform an action" so proteins may be super complex and unpredictable in all actions they can perform, but they still fall under a machine. These diagrams of metabolic pathways are good for teaching as it would be too much to explain it in a fluidly, dynamically changing system to new students.

The only comparison to machines that I encountered in biology class was ATP synthase, where it seemed vitally important that the rotational motion is what causes ATP to be created. I don't remember being taught that everything that goes on inside a cell is mechanical.

This video is basically "Look how smart I am saying these analogies doesn't describe the true nature of the protein"

These animations are very cool! I agree. And, it's good to be a bit critical as, yes, they don't show everything and couldn't possibly do so... nor are they meant to. They are learning/teaching tools. As such, being overly critical of them rings a bit hollow. There are a few issues with your critics: Proteins, when interacting with binding partners, absolutely can become rigid and tightly bound. This isn't misleading. Most proteins have some intrinsically disorders regions. This doesn't mean that the functional or protein interacting domains don't have specific roles and confirmations, though.. even in highly disordered proteins. But yes, alpha fold and AI, in general, will never be able to predict a structure for IDPs or disordered regions, as those generally do not have structure, independent of their binding partners. X-ray crystallography doesn't just give us the structure of a protein in one confirmation. It gives us many confirmations so that we can see most of the states the protein is capable of assuming. Most papers that discuss crystallography results will include discussions on the distribution of confirmations in order to make sense of the proteins' potential function(s). Most proteins are not moving about randomly throughout the cell. Most proteins are highly localized to where they perform their primary function (with the caveat that they first need to be assembled and delivered to that location). For many proteins, this means that they are localized in the cytosol, which, granted, is a huge portion of the cell and proteins that are cytosol-localized move around a lot. Aside from these errors and being a bit too critical (IMO) of some cool animations, this was an informative and well-made video. Thank you for working to push science communication forward, truly! ❤😀

Just because a machine is more complex than you initially thought, it doesn't mean it's not a machine. But I appreciate the point you are trying to make.

So basicly its EVEN MORE complex and detailed than most think. wow these protiens are beyond anything in complexity that we have invented or hope to invent

Great food for thought! I like how you stress the relevance of these analogies while warning against taking them as fact. An analogy usually helps to grasp one specific aspect of a situation, while failing to convey the complexity.

I don't think the "parts are solid" as the main difference is entirely accurate but more that "parts are single-action". There are plenty of non-solid and multi-state parts in machines we commonly use (springs, compliant bendy mechanisms, resonating crystals) but from what I can think of in any machine the individual parts are never complex enough to change their function entirely to something else. That results in fundamental differences in both flexibility and capacity for self-repair, in addition to an informational richness that our current high-tech machines are not close to

Seems to me that "stochastic machine" might be a better description, or perhaps "very stochastic machine". Computers chips are also stochastic (i.e. random) to a certain degree, and chip makers take this into account when designing them by adding redundancy, however the amount of randomness is far far less than shown in this video.

I'm an engineer and have also studied bio in college. However, to me, cell biology is so complex that it's almost MAGIC.

My family has a genetic disease where proteins Misfold in the brain and become infectious or prions. The entire explanation for this disease process is proteins folding into an incorrect shape. Then those proteins touch other proteins causing them to also misfold. The disease is completely explained by the shape of the protein. Do you think misfolding proteins and the shape of those proteins is an accurate way to describe Cruetzfeldt Jakobs disease?

I'm glad I have met another KZfaqr who thinks like a biochemist. The cell is complex and proteins switch function based on so many things. The rigidity of proteins varies thats why we might never know all the functions of one particular protein. In addition some functions show up only in rare environmental conditions. Proteins also show quantum effects on the molecular scale like the generation of excitons by pigment protein complexes.

Y'know there was a time when we didn't understand computers. Had we understood the relationship between DNA and protein, we would have said, "see, not a machine". Then we invented computers and the process appears fundamentally computer-like. As far as your argument that proteins play multiple rolls, I have two "machine" analogies for you: Consider the wonderful transformer class of toys. These things convert between multiple radically different toys -- kinda like the proteins you describe. The other day I went fishing. I didn't have the necessary fishing weight. I looked around and found a nut and bolt. Poof, fishing weight. Now nuts and bolts are "moonlighting" as fishing weights. Yes, all biology is far beyond our current understanding. Yes, multi-cellular life is leaps and bounds beyond "simple" bacteria. However, I have found no magic in there. Stuff moves around because of its interactions with the other stuff moving around. Virtually all the movement, at its core, comes from processing ATP. Beyond our simple understanding? Yes. A machine? That too.

Hanahan and Weinberg: The cell is a machine. It's true. Daniel "Jack" Nicholson: You can't handle the truth!

Onde diz que maquinas devem ter partes rígidas? há vários exemplos de maquinas compostas de partes flexíveis e ate por componentes essenciais em modo de fluidos.

As others have pointed out, the machine metaphor has been resoundingly successful in other contexts. There's perhaps a closer analogy to the modern understanding of how a cell works: LINDA systems. These software systems consist of a plurality of widely disparate (what we call "highly distributed") software modules communicating with each other (where communication is left deliberately nebulous) through what's called a "tuple space." A module is able to perform work only if it finds a tuple that matches its desired characteristics, and in exchange, it places its results back into tuple space. It does not care one whit who produces its input, nor does it care who consumes its output. Data consumption and production is not at all guaranteed to be deterministic (and in fact rarely is). This seems to me to behave just like MMO example you gave, being used for 150 different purposes: in a LINDA system, a single software module can also be used for 150 (or more!) different purposes. And, yet, the whole mechanism is still considered a machine. I'm not saying your thesis is wholly invalid because of this; I find the topic very fascinating either way. But, I do invite you to reconsider your understanding of what a "machine" is, because it will affect your argument in potentially profound ways. Thanks for the video though! It's been a long time since I even thought of LINDA computation systems.

Thank you for sharing this thought provoking video. I don't think that the machine metaphor is misleading, as long as the student is told repeatedly that "this is just a model, the reality is much more complex". We use schematic models all the time and switch between them according to needs. When I say "one hour before sunrise" I can use the simplest geocentric flatearth model. When explaining why my friend is in a totally different timezone I need to use a model where the Earth is spherical and turns around it's axis. To add the complication of seasons I have to imagine a tilted axis, and the Earth traveling around the Sun, etc. The problem is when people are told that a particular model is really how reality works... And they get stuck in the model...

"You can take a blurry picture of someone, but you won't know how well they can dance" is such a brilliant line

Evolution: How well do you wanna dance? Life: Yes.

When I watched the original video I knew that it was showing a schematized version of the molecular process. I understood that the molecular machine metaphor was ... a metaphor. I think implying that the machine metaphor is incorrect will confuse some people even more. I think that often, when people use the molecular machine metaphor they intend to convey that there is no "ghost in the machine". No extra-physical process involved in the functioning of a living being. This is an important message in this age of alternate truth and moral relativism. The original video did a good job of showing some of the unbelievable complexity of living beings. It also did a good job of showing that although we don't understand everything we do understand a lot about the world. Human beings know enough about the world to understand each other and come to an agreement on most topics if everyone sticks to science and stays in touch with reality.

I never had any issue understanding that they're much more complicated than the machines we're familiar with. And having had this analogy does not impair my ability to understand that cells are a soup of molecules floating about randomly, leading to complex interaction well beyond main functions. I think the claim that the machine analogy detracts from the actual complexity and that this complexity is not analogous to a machine is unfounded. I continue to argue that it is a machine but far far more complicated than any physical machines we've created... Even computers are very complex machines that operate on data with ridiculous scope of their capabilities due to emergence. But the CPU is only one small part of a computer. So again, still the complexity is nothing compared to a cell.

“my protein don’t jiggle jiggle, it folds”

I imagine all them little walkers have any number of folk inside them, making sure they get the things to the place. "Oh you how it is. They need it upstairs somewhere.", "Hah! More resources to the brain huh? What else is new?", "Hahaha"

It seems to me the primary benefit of this metaphor for the layman is to understand that biology is deterministic. This is true even for the more complicated models you propose need to/are taking over in biology. It's apparent that biologists need to move beyond this theory, but as you pointed out it's a good entry point. It might even still be useful for some things within the field, I don't know. I'm not a biologist. But I do know that we still use Newtonian mechanics even though we know they're wrong. For most things, they're right enough.

BABE! COME QUICK, NEW SUBANIMA VIDEO'S OUT!!

If your proteins jiggled as much as you jiggled that bike, your body would explode

I think the machine analogy is extremely useful, though not completely matching; but that's what an analogy is all about. Cells and its components are not designed or made by humans, that the difference with an actual machine for starters; but its workings and mechanisms (even with all its flexible parts instead of solid ones) are certainly the same of that of a machine for all we know, and extremely complex one that is.

This was great! Its always "Now we know such and such". Then 10 years later, "we used to think such and such but now we know", on and on. How about " We may never know reality but we continue to explore it" .

1. Veritasium appeals to a much more general audience but I understand the need to be more correct. 2. Looking at a chemical, biological, or neurophysical system from the perspective of a machine is still a useful perspective in unlocking new discoveries. You don't have to do one or another. You need a spectrum of perspectives at least in the beginning.

I finally find a person who confirms what I have been saying for decades, concluded from logic, and no one believed me.

I knew Resident Evil t-virus lore made sense. The Tyrant monster was just a victim of reshaped proteins all at once.

I'm no biologist, just an interested savant -- but I always wondered about those diagrams, knowing the general human tendency to oversimplify. I suspect a far more pertinent metaphor than the machine would be cellular automata such as described by Von Neuman and Wolfram etc, in which nothing persists but the dynamic underlying pattern itself. They can appear completely chaotic, while constantly preserving some essential pattern of information and performing various operations.

Proteins not being rigid or simple does not make them not behave mechanistically. We ALREADY have machines with jiggly parts. The problem comes in comparing things to common specific technologies, which can actually be very innaccurate.

Thanks for your clarification of the bleeding obvious.

"Jiggling" is not a problem. Machines purposefully do it - look up accelerometers and gyroscopes. Machines also have multiple configurations even on molecular level. Simplest example is the all-familiar chemical batteries we use everywhere. More complex examples would be the hundreds and thousands of computing cores in your every-day gaming videocard. In-between there are CPLD and FPGA chips. All the flexibility is there. "Moonlighting" is just like a CPU core getting constant context switches to process multiple running applications on a single core. It's obvious that you are strong on one side, but the other side is weaker. For some reason, i expected more balanced approach.

Okay. Now we will have to treat microbiology like quantum physics. With probabilistic models. Hopefully, 21st century is going to be analogous to the 20th in terms of discoveries in biology.

Super underrated video. Instead of thinking it completely disagrees or disregards those animations or the machine metaphor, it properly expands on the knowledge and gives a pretty good overview of the true complexity behind a cell. After all, isn't all education the process of reducing ignorance?

Self assembling soup! The perfect metaphor! It continues to stay perfect!

Every single cell is a small universe that we can not still comprehend.

Always leaving subanima mind blown 🤯🤯

I don't think im quite catching what the distinction is between a complicated machine and a cell. If a machine incorporates randomness, does it stop being a machine?

Essentially what you are saying is that because the animations aren't depicting everything going on in the cell, or could possibly happen in the cell, all at the same time that they are inaccurate...

As a layman in both biology and chemistry, Berry's work is still an amazing glimpse at the vast complexities of biochemistry. You seem to disapprove of how these topics are introduced, I worry about never having been introduced to signal cascades at all. Scott Manley once spoke about "lies for children", simplifications we use to introduce a new topic to someone who knows nothing about it. Strictly speaking, lies for children are still lies, and are strictly speaking misleading. But they are pedagogic tools we use to teach a lesson. And I believe this is what Berry's work does, you label them misleading, I think they are floaties you give a child to have fun in the pool without sinking to the bottom.

Appreciate this video very much. Shows lots of hard work and insight. Thanks!

Im currently studying medicine and have had a far greater success thinking of cells as machines insted of my classmates that want to desperately see them as only biology or somethibg magical. The truth is that biology and chemistry is just physics at the celular level. I wouldnt say they are literal machines but they are more similar to machines than to living organisms or something magical that happens. Maybe very advances and similar to AI that isnt quite static but still machines. I think this view is limited to thinking of machines as the way we live in, cells are more like machines that are far more advanced than what we currently can make, all while being leagues older than anything we first made

Strange for machines to be doing unpredictable jobs... and yet complex neural networks accomplish their trained tasks in a very similar black-box style where a single given neuron the network may be filling multiple different roles in delivering the solutions to different input problems in ways that we can't comprehend.

“A machine is a crude cell, that will evolve to be as complex as the things it’s mimicking”

FANTASTIC video! I was blown away by dr Drew Barry's animation and not being a biologist I accepted it as a mostly accurate representation of the cell "machinery". I had a hard time wrapping my mind around the insane complexity shown in the animation, but was nevertheless left with the impression that we are on the cusp of deciphering and eventually "bio-hacking" our bio-hardware. With wobbly dancing proteins that change configurations and functions this achievement is probably further away in the distant future. Your explanation is very clear, the production style is also classroom--ready. The world needs much more material like this! You have a new subscriber (or actually two, because my son who studies biology will also subscribe, he also liked the video). You deserve 1000 x more subscribers. Keep up the good work!

The probabilistic nature of these processes makes me wonder about the nature of computation that goes on in our brain and how it could be different to what computers do, especially when it comes to determining whether a neuron should fire or not, whether this randomness gets smoothed out over trillions of cells and whether the randomness can be reinforced and amplified. You've given me a lot to think about.

I really appreciate the clarity and nuance of this video, it really conveys a more accurate understanding of how dynamic and complex cells truly are. That said, I feel like this deeper understanding of the cell’s dynamic components should qualify, rather than negate, the fact that a cell’s protein components have/are parts (1), which are solid (2) and specific (3). Namely, it should qualify them in the following ways: a cell’s protein components are not fixed parts in a single mechanism or process, but are part of many different mechanisms/processes (1*); these proteins are not rigid (whether rigidity equates to solidity is possibly a moot point), but flexible and dynamic, adopting many different (and often useful) conformations (2*); these proteins are often not monofunctional, but polyfunctional, playing a role in a huge variety of processes. My fear is that negating, rather than qualifying that a cell has solid, specific parts (like machines) may cause someone to think that cells are less amazing, intricate, or worthy of wonder than artificial machines, when actually the opposite is true. (I believe the opposite is true because, to me, the co-option of proteins into many processes, their dynamic nature, and polyfunctionality makes them more worthy of wonder than artificial machines with less dynamic and integrated parts). Furthermore, having observed motor proteins (like dynein) in action using fluorescence microscopy, I would also say that, at least for a significant portion of proteins, there would still be relevant and helpful analogies which could be drawn between them and artificial machines. In summary, I would argue that a cell’s components are, in the main, unlike artificial machines, not because they are less amazing, but rather more amazing than such machines. I understand, however, that you probably believe the same thing, and so again I am very grateful for you sharing this information and getting it out there, as it is extremely helpful and interesting.

Thanks for sharing your great information I have a question about the Mitochondria ability of Motility seems like you might know or someone who dose. I been studying Cancer and the link to the Mitochondria and there motility i only know of is by the Kinesn and Dynien motor molecules.

I've always wondered about those animations, if everything is so nicely structured and neatly organised how do all the parts seem to know just where to go? In the animations you just have these random bits flying into place out of nowhere and I thought well what's all that about? It just looks off. Like entropy in reverse. Now you've answered my questions, thanks!

> if my bike jiggled like that I wouldn't be able to ride it > pretty strange for your machine to be doing unpredictable jobs The thrust of these statements is somewhat confusing to me. Are you suggesting that because some parts of the cell have functions innumerable to human beings at the moment, they they do not qualify to count as a component of a machine? I agree that oversimplification is something that must be avoided in all complex fields. But is the machine analogy as a whole bad because the cell is far more complex than any functional machine that we've ever been able to make a species? Is it not the limitations you're placing on the definition of what a machine can look like thats the over simplification? I feel this video is throwing the baby out with the bathwater a bit. The cell performs functions on a mass scale more consistently and reliably than anything we've ever seen, if anything we could look at the cell and conclude that our machines could take a page from its book.

some people hear the metaphor of 'a cell is like a machine' and forget it's still a metaphor. Our best attempt to make sense of it in our terms, rather than describing what it really is.

He’s splitting hairs. The molecular machine metaphor is completely applicable.

You make several good points, especially the multifunctionality of many proteins and the role of disordered regions. But we dont want to throw out the baby with the bathwater, we do actually know many things. On the one hand we shouldn't be overconfident, on the other cell biology should not be mysticism. The "pathway" approach allows many predictions to be made that can actually be tested, e.g., the entire field of systems biology. But it is true that at small molecular scales, there is a lot of randomness. Again, not a new idea. I spent a good portion of my career watching single ion channels randomly flicker open and closed, and yet when it was added it all added together the macroscopic behavior when recording from the whole cell was entirely predictable and reproducible. The statistical behavior of single molecules gives rise to pretty firm rate constants at higher scales.

🤓 tell me more mr anima 🤓

The primary structure of proteins remains the same, regardless, correct? Of course the tertiary and quaternary structure can change, but is what you are saying in this video different from the previous statement?

I wonder if the inherent randomness of protein function is related to quantum mechanics and whether quantum biology will deepen our understanding of this randomness.

Our group of long-time friends has a guy that arguments in a very similar way to you. Whenever he starts his rants, everybody starts rolling eyes. He will always claim to be "technically correct", not to say enlightened... while COMPLETELY missing the point and annoying everybody while doing so.

Good consideration. The circumstances that life holds is often based on that which is most likely. What would maintain the definition of a system that maintains a direction whether specific or general in an environment of change/motion?

“Is the analogy valid? Naurrr” I love the Australian accent 5:20

great video

I teach high school and I see great value in the machine metaphor as an introduction to molecular biology to replace the naive model students hold which is, "black box magic and anthropomorphisms." They can unlearn the machine metaphor after they have "mined all the gold" there is to get from it, just as physics students unlearn much of classical mechanics. It doesn't mean we stop using it, it just means its not the final tool in the tool box.

I am studying plant biotechnology and i have never heard someone critcally asses this topic. Wow! You just changed my view significantly.

I get your point. Same as when people say that organs are specialized, while in reality they their roles are way more fuzzy and complex that pieces in a machine. Also brain is not a computer.

The machine metaphor is a tool to lend understanding to the non-technical like myself. The machine metaphor is meant to imply mission, intent, precision, inputs and outputs, non-randomness, complexity, etc. A schematic drawing is also useful to convey similar information. The advance science necessarily outgrows earlier explanations. Isaac Newton's apple too was a crude and humble beginning for physics. Curious to know if the machine metaphor swims too close to those pesky non-materialists who maintain that a machine requires a design.

Biology is such a mess...! I'd love for some individuals to tell me how is that "intelligently designed", erjremmm... Now I perfectly understand why some biologist said alphafold is cool but the challenge it's not solved. Great lighting, script, vibes... everything! You'll go far my man. EDIT: Can I suggest you to break apart Michael Levin's Bioelectricity stuff? It's fascinating.

Honestly this mostly depends on the level on which you need to process the topic. The "machine" abstraction is just that, and makes presenting these topics much much easier. Just like chemistry doesn't skip directly to wave functions, because something like the Bohr model is just so much simpler to grasp at first. Great video though. Life is incredibly complex, and messy.

You are incredibly well spoken and talented at simplifying complex subjects - thank you!

I think you're wrong. But very constructively so! This is a response to several of your videos, not just this one. I think a lot of my issues with critiques of the machine metaphor and math & physics-based approaches to biology boil down to the assumption that these approaches are purely reductionist, static, analytical but not synthetic, and materialist - when in fact they needn't be. It over-simplifies math, machine thinking, and physics and then blames them for over-simplifying biology! The machine metaphor is a metaphor and all metaphors are misleading on some level. This I happily grant you. Machines are intentionally made by humans using the technologies we've developed - cells aren't, for now. However, the machine metaphor suggests that we can understand, disassemble, redesign, and repair biological organisms - as we most certainly can. I used the CRISPR Cas9 system (with HDR) to edit the genome of E coli. cells and turned (some) of them from blue to white. It didn't work perfectly. I don't know why it didn't (I suspect my streaking technique and the method I used to introduce additives to my plates of bacteria). But the success of others indicates to me that what was going on in the cell was highly predictable and determined by only a few controllable factors. There were doubtless many other processes occurring - but these simply didn't influence the outcome very much. I take issue when people use language like, "cells can't be *just* machines!" or "mere" machines, or "brute" machines. This is the perspective of someone who uses machines - but not someone who designs or makes them. No machine is perfectly rigid and solid. I'm not just talking about flextures here either. It's a settled fact in statics that everything is made of rubber and everything that moves vibrates. Slop is nessesary for assembling machines and allowing their parts to move. Issues of clearance for parts in different positions are in many ways analogous to different conformations of proteins. Oils are used to keep internals away from surrounding solvents. A flathead screwdriver is rarely ever used to turn flathead screws and nearly everything looks like a hammer when you need to drive a nail. The machine metaphor also helps to give people hope that very complex systems are ultimately explicable and that their experimental behavior depends on a finite number of factors. It's not just a heaping pile of protein spaghetti as some claim. Every knot was once straight rope, as they say. But sure, I hear you. Like all metaphors, the machine metaphor is limited. For deeper understanding we turn to math and to physics. The ordered structure of living things that emerges from a largely chaotic environment results from the pumping of entropy. This requires the concentration and dissipation of energy. Entropy in closed physical systems always increases, meaning that unlikely ordered states regress to the mean and become disordered. Yet living organisms clearly run backwards in this respect. They're growing ordered structures. This can only be explained by understanding that organisms are not closed physical systems. But that doesn't mean that they're not physical systems. It just just means they're Open physical systems that require interaction with their environment in order to survive and grow. There's some really interesting work being done in integrated metabolic theory - and even more fascinating work in understanding how organisms model their environments through embodied computation by understanding the process in terms of thermodynamics and information theory. Math and physics have absolutely no issue at all with treating objects as stable flows. In many ways the difference between a noun and a verb breaks down when you look at it in terms of physical behavior. This is perhaps most famously depicted in the case of light having both properties of a photon particle and properties of an electromagnetic wave. Mathematics is not the most empirical of the sciences... to say the least, but it can very neatly model processes and relationships that might be thought irreducibly complex, unapproachably abstract, or purely philosophical and beyond the reach of logic and rationality. Dynamics, chaos theory, information theory, and statistical mechanics are not easily escaped. I believe strongly that every phenomenon in the universe can, in theory, be boiled down to mathematics. Perhaps that mathematics has yet to be developed and the study of some very unusual physical systems will inspire its creation - or perhaps someone will invent the math in a flight of abstract fantasy only to discover that it's how the world actually works. But I think there is absolutely nothing beyond the grasp of math. Not consciousness nor love nor life nor death nor meaning nor purpose nor taste nor goodness itself is. Math is the ultimate metaphor - and it can represent reality arbitrarily closely. Brownian motion is statistical and follows predictable patterns, displaying regularity that can allow us to make conclusions about the nature of the things it influences. This is how Einstein developed strong evidence for the atomic theory of matter. It's not truly random - only complexly psudo-random. Relatedly, let's turn to supposedly "non-functional" non-coding genetic material. I understand biology largely from the perspective of the gene level. Genes built from nucleic acids, whether as DNA or RNA or whatever, are the fundamental units of replication in biology. They're probably not the only replicating units, but they are necessary for all living things to carry on from generation to generation. If the organism is the riverbanks, the gene line is the water it needs to flow. Consider that there might be environments in which it is beneficial to live in a series of organisms with greater or lesser susceptibility to genetic drift. The amount of genetic drift is clearly and noticeably different across different species that live in different environments. Some species of jellyfish have been around unchanged for more than 200 million years. In other cases we can see genetic drift in action. The genes decide which genes are conserved - and which aren't. It makes sense, then, that large stretches of genomes would sometimes be composed of experimental new sequences. Species in competitive environments requiring frequent adaptation would have died out if they weren't capable of trying out new, mostly useless or even harmful changes. And the conserved genes in those organisms would have died out - being out-competed by combinations of genes which make adaptations more likely. Circling back to proteins, we can still try to figure out how much of the time proteins spend in one type of confirmation vs every other, or how often they're close enough to do some particular function. Their motions are complex, but again, not completely random. There's still order there to be sussed out. I have my own thoughts on the formation of multicellular organization and the evolutionary pressures that give rise to it but it's beyond the scope of this comment and hasn't been tested. Consider the work of EO Wilson on mathematical modeling of ant hive behaviors. Or consider the work of Richard Dawkins on the extended phenotype to get a better idea of of how we can actually understand some of the more complex behaviors of organisms in their environments.

Edit: before you read my critical review, please know that I have just watched several of your other videos and I liked them very much. I just disagree with this one. I'm also neurodivergent and it makes me sound harsh when speaking seriously. I don't know how to change that. I wish you the best in your journey. I have a fairly low opinion of Veritasium. I think they push hype way too far, and Derek definitely thinks he's smarter than what he is. However, you have given me no choice but to come to their defense. I think the problem is with your definition of a machine. Why would you define a machine as having fixed parts with each a specific function? I'm not aware of any such rule, and it certainly does not define my idea of a machine. Then you go on to say something like "Pretty strange for parts of a machine to do a bunch of unpredictable jobs". So not only does a machine not care whether you can understand it or predict its behavior, but also: good luck predicting the behavior of complex machines like microsprocessors with a voltmeter and an xray scan, especially when they are running complex algorithms like neural networks that really nobody can predict except in extremely simple cases.

When I'm in a spreading misinformation competition and my opponent is veritasium: 🗿

Interesting video. Are models just simple representations of more complex systems? At school I was taught the model of electrons circling a nucleus, like planets orbiting the sun; yet the reality is more like it's an electron cloud. Models are useful up to a point.

It's not misleading. It's a demonstration to improve understanding.

As someone dealing with damaging machine metaphors in my own field, I have to say this is an excellent video and I agree that sometimes we have to be ready to drop metaphors, once the purpose they served is no longer being served.

You know what, this actually makes me happy. There's still A LOT to discover, and it doesn't look like we'll run out of mysteries for a while. So we can keep doing what humans are best at doing, keep exploring

I've been animating biochemical processes for years -- and I always loved the 'clockwork' metaphor as a way to help communicate the complexities here. But that way of thinking really boxes you in and keeps folks from grasping some of the essential complexities here. Great video and a great way to unpack that Nicholson paper. Not sure how I can rebrand here--but figuring it out.

Really awesome video. I am always happy to have my mental model about something totally rewired in way that immediately makes sense and builds newfound curiosity!

I don't know, I have mixed thoughts about the overall idea of this video. I can wrap my mind around the idea that this biological circuit boards can lead into wrongful thinking. On the other hand are these part of middle school curriculum and public edutainment and maybe they are at the right level of detail for these purposes. Complex enough to transport information while not being overwhelming and discouraging. It is only at academic level when they are to rough. When you learn a second language at school for the most part of it you learn 1 : 1 translations and it's fine. Only when you get to higher levels you have to correct your knowledge and you learn the fine shifts of meaning (due to different cultural contexts) when it comes to translating from one language to another. I think the same applies here. Molecular "machines" are "good enough" knowledge for Joe Average. Also: the animations are pretty cool. They are kinda the beautiful colorful pictures of galaxies. Not accurate, but engaging.

Dude, thank you so much for correcting previous assumptions on biology, it helped me a lot.

For IDPs, I went to a really interesting talk recently at my university that showed that even the formation of synapses between neurons depends on intrinsically disordered regions (bits of proteins that are long and stringy and not a structure) - these are key because they allow proteins to kind of form gel like phases in solution almost like an oil bubble in water which is key for neurons apparently