It doesn't really make sense to talk about DNA as source code vs object code vs whatever.
Biology doesn't have the same clean levels of abstraction that we've developed in computer science. DNA functionally operates at many levels. It long term storage, local working storage, and it is used to compute. It's a single molecule that does everything.
Then you have to throw in all the secondary processes that modulate and regulate DNA replication, transcription, as well as activation/deactivation.
While it can be useful to lean on the abstractions we've developed to try to understand what DNA is doing those abstractions can only be taken so far.
Yeah, I was kind of making a joke. But to stretch the analogy...
Maybe there is a common source that is cross compiled to different chemistries producing a seed object code cell.
Indeed, I classify the main difference between life (biological systems) and technology (civilization engineered systems) is about the structure of complexity.
Human civilization is severely time-limited (or just time-pressed). We can't wait millions of years running a simulation to optimize a little widget. We need to rely very much on high level design and comparatively little on efficiency and optimization. On the other hand, life cannot afford huge DNA (very costly), or energy waste (generally disfavorable from evolution). So human built systems tend to be of a low "Compute complexity": the computational complexity of obtaining solutions and solving problems themselves (like civil engineering structure problems, or design of objects) must be fairly low. For life, systems can be amazingly intricate, every tiniest cell a wonder that would probably take thousands of years for civilization to maybe be able to replicate. But it all ranges from about 130kbp to 8Mbp[1], which would be around 16Mbit/2Mbytes at most. So it fits (uncompressed) in a diskette (floppy).
Even now with powerful computers, we're still mostly constrained by cognition (specially human), you see simplicity all around you.
So if you look at the human world, you see (computational) simplicity everywhere, but the natural world has undergone trillions of generations of optimization to arrive at almost perfect (in an almost literal way) little machines, complicated but with a hidden amazing (size) simplicity.
I think there's a connection to be made to algorithmic inference as well. Originally we came up with ideas for Universal Inference (from ideas from by Solomonoff, Kolmogorov among others) [2][3], the most glaring candidate was the "size prior": evidence explainable by the least algorithmic information ought to be most likely (Solomonoff inference). Later, there were promissing ideas around an additional term: the "speed prior" (from Schmidhuber[4]) -- the biological word is one where the "size prior" (simplicity is most likely) works almost perfectly, and human civilization is one where the "speed prior" (computationally easy is most likely) is helpful.
Side note: I think intellectually one of the ways we're really far behind is recognizing Algorithmic Information theory as a foundation for statistics and metaphysics. We're very stuck making little progress on the metaphysical realm (which physics is advancing more into) because of a lack of widespread acceptance of those advanced tools for science. Algorithmic inference gives a solid basis for comparing metaphysical models and deep questions about the cosmos.
embryology made me realize that there's also an inherited context in how genes control development, IIRC the womb triggers some key structural changes in the very first days.
Biologist performed so many crazy experiment on fruit fly, development biology is very interesting for reading: what happen in each stage and all of mechanism we can understand.
I did my undergrad in electrical and computer engineering. My career has been focused on software. Yet, all through high school and into university, I took biology courses. I did my masters in medical imaging. I wrote the MCAT.
I’ve always loved biology. The intricacy of the systems, and how they work together is so fascinating and really presses the same buttons as computing.
I think this is not a biology-only phenomenon. I have the impression that chemistry and mathematics are also not taught well in many (if not most) high schools. Physics education in contrast seems to be in better shape.
If you think US high school education is abysmal where do you think does it better? US Asians do very well compared to other Asians, US whites to other whites, etc.
If they can’t do well on tests designed to measure skills the students have been failed. They have not learned skills. The US education system is quite good at teaching skills. A large majority of countries do worse. The skills that PISA tests are a prerequisite for almost any more rarefied learning that is often held up as the real purpose of education.
Being able to read for meaning, extract information, combine knowledge from two texts, distinguish between what is stated and what’s implied, even to figure out something is implied, all of those are the kinds of things we expect an educated person to be able to do. PISA tests them. Trying to make people care about academic subject matter is very difficult because most people do not care and do not find it useful. Thus they forget most of what they learn in school. Insofar as education is forcing the tastes of one class on everyone else it can burn. Most people don’t care, just like most academics don’t care about sports. Forcing sports on them would also be an injustice.
> Trying to make people care about academic subject matter is very difficult because most people do not care and do not find it useful. Thus they forget most of what they learn in school. Insofar as education is forcing the tastes of one class on everyone else it can burn. Most people don’t care, just like most academics don’t care about sports. Forcing sports on them would also be an injustice.
Ah, yes, we should teach less science to everyone because that would be like forcing every academic to play sports. Perhaps physical and science education should be provided for every student? Education does not come at the expense of sports. If anything, the opposite is often true.
People are naturally curious. Shuffling them into the confines of some narrow and often purposeless maze fucks that up. That's a considerable portion of TFA, the institutional curricula stunted their interest in biology.
My curiosity was drugs, drugs lead me to biology, lead me to chemistry, physics - but it was independent study. Political challenges from my partner got me interested in history and anthropology, but it was all independently structured.
I think if the institution gave all these little knobheads enough autonomy to actually derive, from themselves, some real interest, they would ultimately end up intersecting with all the sciences, it's actually inevitable. Instead they're just forcefed a bunch of information they don't have a relationship with.
Sports is biology and mechanics is molecular biology and kinesiology and so on. It doesn't matter where you start, you track into that shit. Passion the latitude it lends to the people possessed by it is what allows us to push deeper and deeper. Not stunting intellectual growth by conditioning people into a state of repulsion at the premise of learning.
Yes, yes. Education is multifaceted in its consequences. Merit depends upon objective testing. Common culture and high trust society depend in large amounts upon education and schooling. With the quality of schooling available, shortage of teachers and quality teaching personnel due to abuse and low salaries, political interference with teachers handling their own material, religious indoctrination in charter schools, and the amount of students requiring remedial classes in college. Of course, more data and parameters can be considered, but I don't think anyone can consider the broad state of secondary and primary education in the US as "healthy" or "improving."
I don't think you can objectively test. When you do test you're making a singular data point that doesn't reflect ability, necessarily, but instead a coincidence of factors at a given point in time. The data point is arbitrary, even if the test is scored against the distribution.
Take, for instance, a FT-working non-trad that scores above the mean. The mean who predominately consists of students who are FT-students. Should some respect not be paid to the considerable handicaps suffered by the non-trad? How do you even begin weight that?
Of course this is multiplied a million times over in several dimensions.
"The science of government it is my duty to study, more than all other sciences; the arts of legislation and administration and negotiation ought to take the place of, indeed exclude, in a manner, all other arts. I must study politics and war, that our sons may have liberty to study mathematics and philosophy. Our sons ought to study mathematics and philosophy, geography, natural history and naval architecture, navigation, commerce and agriculture in order to give their children a right to study painting, poetry, music, architecture, statuary, tapestry and porcelain."
-- John Adams in a letter to his wife Abigail
I'm sure many people would love to have their children study the arts and humanities and develop profound insights into human nature and life itself. Unfortunately, many people are stuck studying mathematics and other subjects like it in the hopes of having a decent career.
For most of us, these great awakenings come with age. They are rich, sublime flashes of clarity and intellect to be enjoyed (first and foremost) and nourished (thereafter) with more such awakenings. The simplest of deductions lead us to wonder how, what, and if. This is the tunnel through which some people end up believing, through disbelief and astonishment, that there is a grand design at play and that it is a thing of beauty and wonder.
There is a large gap between the mechanisms of chemistry and the magic of biology that most people do not see closed until late in their education. It's a real shame that this gap cannot be closed sooner.
In undergrad I took a bunch of biology and chemistry classes. It wasn't until I took Biochemistry (a senior level class) that everything came together. The biochemistry class I took was a re-telling of all the stories you learned in molecular biology but with the tools you acquire in organic chemistry.
Equipped with those tools I relearned the Krebs cycle and photosynthesis as real chemical reactions that make sense rather than a chain of facts to be memorized.
The class left me with a deep and profound reverence for life. Every process in a cell has a mechanism that can understood with chemistry. However, the magic of life exists where those processes come together and interact in incredibly complicated ways.
It's seductive to think that we should be able to tease apart this complicated processes and figure out how "life" works, and maybe someday we will. However, it's easy to underestimate the level of complexity and interconnectedness in these systems.
Many of us understand how hard it can be to debug a distributed system. Imagine trying to reverse engineer a distributed system with tens of thousands of interconnected services and messaging queues that all just sort of evolved and were not built with clean engineering practices.
Lovely life lesson shared. It also blew my mind how much complexity handling non formal, discrete systems adds. Just samoling root development takes years and endless hours of tedious, non automatable work. No wonder the field progresses orders of magnitudes slower. Also, add chaos theory, quantum mechanics, differential equations and enzyme molecules to the distributed system to make it a bit more realistic.
I would go further to describe living systems as not just distributed and so on. Also they are self-assembling and self-repairing. They are redundant - which makes them more damage resistant and 'evolvable'.
Also, these complex assemblies of machines work at (mostly) room temperature and pressure. Except for extremophiles that can work down to freezing or up to boiling temperatures, or in acid or high pressure environments.
Also enzymes catalyse stereospecific reactions, or can use light to drive proton gradients across a lipid membrane, or reduce nitrogen gas. I've always found it funny the sci-fi obsession with 'nanomachines' when living systems are basically composed of exactly that.
Proteins are the runtime on which DNA is executed, because they are the mechanism that "reads" DNA. But proteins are the compiled output of DNA, because they are the result of "reading" DNA. So the DNA defines the runtime environment that is necessary for DNA to run.
RNA actually has a large role to play in going from DNA to protein. Its been suspected that the first life was RNA based because RNA can actually form functional site similar to proteins to do enzymatic reactions. RNA is some of the secret sauce to many of these systems
Definitely true, and my comment was without a doubt extremely oversimplified and wrong in several respects in an attempt to explain the analogy. Thank you for giving the clarification on it.
I'm not sure there is such a fundamental difference. In biology the code is the DNA and RNA, whereas the hardware is the proteins. DNA and RNA are self-modifying and imperfectly transmitted, but those traits can also exist in computer code (to the extent that they aren't, it's because humans make sure of so, because they hate trying to understand dynamically changing things). The hardware of life is self-creating and self-repairing, but - again - this can also be easily simulated in computer hardware, to the extent that it isn't, it's because it's costly and there is no good reason for it.
Biology's difference from computers is in scope (organisms are whole factories who just happen to have computational abilities by necessity) and origin (organisms aren't designed, and this profoundly and significantly affects everything about them).
> In biology the code is the DNA and RNA, whereas the hardware is the proteins.
This distinction isn't as clear as you think. The active parts of ribosomes (the machines that translate mRNA into proteins) are catalytic RNA. There are organisms that use RNA to store templates (RNA viruses).
I went through biochem, but didn’t fully understand just how gigantic & complicated proteins are until I started learning about computational protein folding. There’s several levels of abstraction just between rna/ribosomes and functional proteins… that’s one of the most shocking complexities to me, most pieces of life are rather elegant when you come to understand them but it’s hard to imagine how complex proteins evolved spontaneously. There’s just endless complexity there.
There’s 574 amino acids making four separate interlocking chains in a single globin, plus the heme, all just to bind 4 oxygen molecules. It’s simultaneously elegant but hugely complex, far above any discussion of the rna sequencing.
It’s a big part of the “gap” between chemistry and biology IMO.
I worked for a professor (James Milner-White) who was interested in early protein evolution and I remember a conversation we had about the possibility that proteins could have evolved from large to small.
Not sure if it was from a published paper, but the idea was that early proteins might have been large - say several hundred residues - but mostly disordered.
The smaller, more ordered 'domains' would then have evolved within these larger chains. Recombination and deletion would then have pruned down the disordered parts to leave more efficient smaller proteins.
No idea if that idea makes sense or has any research behind it, but it's quite a neat theory.
Its actually top down and bottom up at the same time. All of biochemistry operates on the basic rules of physics which determine how the chemistry happens with feedback from the surroundings/system as the top down part
There was a paper a few years ago about a similar effect in artificial neural networks [0]. The gist was that a large network can contain many subnetworks, and the number of subnetworks grows much faster than the size of the network they are contained in. They were able to find a subnetwork in a randomly weighted network with equivalent performance to a trained network of a much smaller size.
> I went through biochem, but didn’t fully understand just how gigantic & complicated proteins are until I started learning about computational protein folding.
Some years back, there seemed an opportunity to create an educational web interactive, a full-scale 3D folding sim, with hands-on direct manipulation, by aiming for plausible-not-correct folding. The simulation literature having built up lots of shortcuts for slashing computation costs, which sacrificed correctness but not plausibility. So one might variously knead a protein, alter it and its environment, and watch it flail. I wonder if anyone ever got around to it?
The hemoglobin molecule is different for every species, and if you chart changes in the molecule, it forms the same tree as evolutionary biologists had already figured out.
Humans have the most complicated hemoglobin molecule.
I recall the same "everything coming together" feeling, but for me it didn't happen until Applied Biochemistry in grad school.
I recall the final exam being only a single question, with a bunch of blank lined pages to write your answer, and the question was something like "You just ate a ham sandwich. What happens to it?" A good answer needed to include everything down to the molecular/chemical level and tie it together all the way up to the macro scale, and I finally felt like that class had prepared me to tell the story.
... it'll be fun if you start from what happens when the enter key is pressed- the mechanics and electronics involved in submitting that URL (and some chemistry and physics behind what your eyes see on the screen), the physical transmission of the signal from your computer to through the interwebs and some error correction protocols to ensure your signals are still useful.
Maybe toss a line or two in about the complexities of running a large data center and how your response time varies based on some sorcery.
Then you go the extra mile and weave a tale of electrons wrestling with their universe of invisible electromagnetic wave overlords that determine their fate while they embark on a treacherous journey to convey information thousands of kilometers across with blistering speed. Tell them of the aged electron saw a family member get attacked by a stray cosmic ray and the fright of the pack when one simply tunneled out of existence...
When we start looking at life at the level of physics, chemistry, and biochemistry, the absolute beauty of the system begins to appear. The complexity is on a scale that's difficult to imagine or even unimaginable even to those trained in the fields, and there is a feeling of wonder that words can't capture
Not during an interview, though. The interviewer would see it as trolling (at best), and you would fail the interview. And for a good reason! Because as an engineer (and an intelligent person in general) you must be able to separate what is essential from the non-essential for the subject in question. For instance, the physics or the physiology of the process of pushing a key on a keyboard is probably not what the question was about, nor do those things in fact have much to do with typing, even (which you can do on a touchscreen or using the mouse).
This answer reminds me of the blog post of the guy asked to implement a linked list iirc during an interview, and he does it all in Haskell's type system.
Going over quantum electrodynamics to explain how to make microcircuits would be fun, that was our first class specific to electrical engineering for me some 30 plus years ago.
My finger gently depresses the black plastic key. As the machine begins to pull in data from the net I shift my weight back in the chair and look up over the top of screen, out the window at the lunch hour foot traffic passing silently by beyond the steam tinted cafe window. The overheavy graphics begin to render, but my gaze is caught by a momentary glimpse between the rushing cars of a woman in a red dress on the far side of the street...
Oh man, where do I even start? Sensory input from the inner ear to balance, the networks that handle feedback from afferent signals from the periphery, efferent pathways to control motor movement. I don't even know all the details but it's mind bogglingly complex. Do I explain the molecular basis of action potentials? The modulating effects of inhibitory feedback within the networks? I feel like all of that barely scratches the surface of the insane complexity of neuronal networks
And how does one even begin to talk about our desire and internal drive to do things like ride a bicycle
It took me a long time to figure out how I made a turn on a bicycle. No, it's not just turning the steering wheel in the direction you want to turn. You actually slightly turn it the other way, then the bike tilts into the turn you want to make, and you turn the steering wheel into the turn to stop the tilt from turning into a crash.
It all happens so subtly, and your body does it perfectly with no input from the brain other than "I want to turn".
It is a fun one to show new or inexperienced riders. "Now that we are coasting, what do you think a slight push forward of your left hand will do to the bike?"
Assume an experienced rider, as learning is different.
Intention is set, requiring the basal ganglia and fore brain and either a notion of free will, determinism, or whatever you fancy.
The area ahead is scanned and mapped for a clear path via retina- optic nerve - visual cortex and particularly the dorsal parietal pathway.
Initial organised motor signal sequences originate in pre and pre pre motor areas, hitting motor strip of the brain, particularly those homoncular areas corresponding to legs, arms and torso. Basal ganglia loops prime these circuits into action and help maintain their engagement.
Activated motor strip neurons pass through the internal capsule, down the pyramidal tracts, the spinal cord, and meet a lower motor neuron in the anterior horn, which then carries the baton and traverses out if the cord (still the central nervous system) into the body and to the final destination: a muscle. Electrical depolarisation along the axon hops rapidly between nodes of ranvier, enabled by insulation myelination. At the terrminal synaptic bouton lower motor neurons branch across a muscle body. Each neurons innervated patch is a motor unit; multiple combine into a motor pool. Depolarisation triggers fusion of vesicles to the membrane endplate and release of acetylcholine into the thin synapse. Rapid diffusion moves thr snall molecukes to the muscle membrane, the sarcolemma, who then bind to the membrane spanning Nm nicotinic receptors which open and allow a rapid flooding of sodium into the muscle cell syncitium and efflux of potassiun into the extra cellular matrix. Depolarisation of that muscle allows further calcium released from the sarcoplasmic reticulum to activate protein machinery; myosin and actin run across each other and fibres contract. With enough activity concentric movement is achieved across the associated joint.
In a manner similar to walking, various spinal reflexes and the spinal locomotor pattern generator create a local, fast framework for actualisation of the impulses.
Feed back on state of the musculature ascends the spine via dorsal root ganglia and the dorsal horn. Amongst these are proprioceptive afferents, rapidly feeding back state of tension in muscle fibres from golgi tendon organs along highly myelinated type 1a fibres. These signals pass into the cerebellum where they are co processed with signals from the eyes and vestibular system.
The cerebellum modulates
the intensity of descending motor activity by comparing expected to perceived muscle state. It also orchestrates balance by integrating general body state, visual cues and vestibular information. In this way the small and large oscillations of riding the bike are maintained and constrained into an orderly process.
Experienced riders can dedicate higher function, i.e Executive frontal areas to other tasks, or to refined modulation of thr task to overcome specific issues.
Beginners must use all their frontal powers to focus attention on the task, painstakingly sequence actions, and reflect on the numerous errors and their consequences. Learning is slow, multi system, and largely independent of autobiographical memory.
You forgot the metabolic cycles of the signalling molecules and their receptor proteins, as well as the ion pumps important for those reactions. I think that is really what the professor was going for. Also, perhaps, some [partial] description of the learning process as it impacts a single neuron.
I had a similar revelation for structural biology, applying the physics I learned for bridges and buildings to microscopic proteins. They are structurally like a cathedral built by a blind and deranged architect. The fact that mechanically bend, pivot, and move like a complex machine at a micro scale to do real work is the most sci-fi thing I can conceive of.
Think of a even a simple walking protein like Kinesin [1]. What is not shown in the video is that this is all happening in a hurricane of molecules battering it from all sides. Each part of the structure is being pushed, pulled, bent, robot made out of sticks and rubber bands.
The other word missing is "cheap". Proteins are under a massive selection pressure: many thermodynamic reactions in fundamental bits of biology are as thermodynamically efficient as they can be, else some slightly more efficient mutant would have out-competed it aeons ago.
I became interested in biology as a physicist when I realised that all of the problems, on some level, boil down to putting a load of lego pieces in a box, shaking it up with some energy not terribly different to k_B T, and getting a fully-formed, self-replicating lego models out the other end. It's all physics. It's all utterly incomprehensibly mind-bogglingly complex with layers of complexity wrapped around each other, and far out of the realms of either physics or chemistry to compute completely. It's why I work at the intersection of the two fields.
Another famous paper, often-mentioned, related to this is "How a biologist would fix a transistor radio", essentially armed only with a shotgun. The tools of modern molecular biology may be scalpels rather than shotguns, but still, the idea is arguably the same.
I was about two sentences into the parent comment when these videos came to mind. If I had seen just about anything done by Drew Berry when I was in middle school I probably would be in a completely different career:
I feel the same way about just math in general, and all the sciences that derive a lot of their knowledge and systems from it. You start learning math as just high level/abstracted away things where you just have to memorize that this thing does that and in this case do this instead, especially derivation I remember they showed us the formula with dy/dx, but they never showed us any proofs of why or how that lead to the different outcomes, we just had to memorize.
Meanwhile, later when you get to higher education, math just kind of explode into this creative problem solving field with loads of interesting problems and ways to reason about them, but you almost have to relearn it/properly learn the basics over again when you get there, because you never learned why or how the basics works, just the input and output of the basics.
I had the opposite experience. My teacher took extra care to explain to us why and how certain things worked in math. The reason I loved math so much, and still do, is because I never had to memorize anything. I just had to understand how it worked. In biology, however, it was very different. I had to memorize facts instead of understanding them.
I agree in spirit. I’d love to see a curriculum that somehow teaches kids to “discover” counting, addition, the utility of notation, squares, cubes, up through square roots, complex numbers, derivatives, etc…. But I feel like it would be tough to create.
While retaining the typical high school separation between math, biology, chemistry, and physics, but given control over the curricula taught in those courses, do you think it is possible to teach a single very high-level concept such as the Krebs cycle in full complexity at a high school level (i.e. starting from algebra and very limited science education, completed in four full-time years)? This seems like a foothold for a potentially interesting restructuring of how we educate children, oriented toward depth in a few things to enlighten future breadth. I ask specifically about feasibility, since that seems like a necessary prerequisite to a discussion of beneficial value.
The more I understand about biology, the more bizarre it is that people try to beat it down to simple, obvious, narrow, and globally consistent binaries to serve their ideological purposes.
> In undergrad I took a bunch of biology and chemistry classes. It wasn't until I took Biochemistry (a senior level class) that everything came together.
In high school I really hated biology and chemistry. It was just a bunch of abstract stuff. What made me (re)discover biology was taking up gardening. To me gardening is like applied biology. After a while you really start to get a sense of how it all works and just how unbelievably complex life systems are: photosynthesis, the carbon cycle, the different water cycles, how soil life affects the plants that grow in it, and how incredibly resourceful plants are in interacting with their environment (not to mention insects and other creatures higher up the food chain...)
This happens so much in education, and specifically academics. A lot of theoretical explanations without stepping back from theory and going back to reality once in a while to ponder about the implications. I passed a lot of courses by memorizing theoretical concepts without truly understanding it. I feel that's wasteful because I'm not advocating for longer lectures, but rather more effective teaching methods (at least they would be for me). Understanding a concept is very different than proving a theorem.
I was told that this was so that they could craft cirriculums that stretch decades when it could be taught much quicker. Doing so would be deterimental to the labor market in academia.
> Imagine trying to reverse engineer a distributed system with tens of thousands of interconnected services and messaging queues that all just sort of evolved and were not built with clean engineering practices.
I read a good book review[1] that moved me towards the view that figuring out how a living thing works is possible, though not necessarily easy. I highly recommend reading it, but here's a summary.
Evolution promotes fitness enhancing functionality, so we should expect biological processes to be useful for some purpose and hence not be distributed like a random graph (e.g. Erdos Reyni graphs). Indeed, if we look at biological structures, we can find that the causal networks they form are far from random. Furthermore, there are often repeated motifs present. These motifs are quite simple and seem to map neatly onto human understandable concepts (like XOR gates or autoregulators or feedforward networks etc.)
And often, the overall graphs seem like they're tree like rather than some complicated mess of feedback loops (barring autoregulation). This kind of structure is quite modular, and hence we can leverage our understanding of component parts to understand greater and greater pieces of the organism.
There are two problems with this arguement: one, that a lot of the data used for it is not nearly exhaustive. Maybe the people examining biological circuitry stumbled on the rare areas where there are repeated sub-components. Second, even if there are repeated sub components, why should we get modularity i.e. few connections, mostly local?
The former may not be an issue if there hasn't been a lot of dedicated effort towards finding human comprehensible structure in biological circuits, which there might not have been. These things are big and complicated, with many constituent parts, and teasing out the underlying structure may require loads of computation and statistical analysis, which was hard for most of the history of biology.
The latter is not adressed in the book review, or in the comments, but the review author's work makes me it plausible to me that modularity will be common in biological systems. I don't have a good summary of that, or can clearly articulate why I'm hopeful about this. But read the rest of the work of the writer of the article if you're interested in this kind of stuff (key words: natural abstractions, interfaces, selection theorems).
There are additional factors that make molecules in cells not subject to pure diffusion rules. Charge depending on the pH of the area ( even if in such a crowded space it is likely not really a pH anymore), and molecular interactions. Proteins (and virtually any other molecules but proteins and to a lesser extent nucleic acids are particularly good at that) can stick or be repulsed by their overall composition (external charges, hydrophobicity) but they can also stick to each other. Biology is fascinating but you can't isolate it long from chemistry and physics if you want to understand it.
Yeah see the art of David Goodsell. I believe he said the concentrations of the various biomolecules are roughly accurate based on calculations he does before starting painting. Cells are incredibly crowded. The human body being 60-70% water is usually presented in pop-sci as “wow we are mostly water!” but that’s actually very concentrated for chemical reactions. You usually don’t perform reactions that concentrated in a lab whether it’s biochemistry, organic chemistry, inorganic, analytical, etc. It’s a wonder all this stuff doesn’t just gunk up and precipitate out of solution.
All the living cells spend continuously a lot of energy as long as they are still alive for avoiding the appearance of precipitates inside the cell, e.g. by pumping out of the cells the ions of calcium and sodium and pumping inside the cell the ions of magnesium and potassium, because the former are much more prone to produce precipitates than the latter.
This continuous ion pumping is a major component of the energy consumption of a living being when it is idle, apparently doing nothing.
It is a big part of the communication, regulation and sensory system of cells. A lot of receptors are linked to ion channels for example. That's also the reason why there are pumps to bring the ions back on the other side too.
All these functions have appeared much later, most of them only in multicellular living beings, billions of years after the appearance of the living cells, and they have just adapted the already existing ionic pumps to other purposes.
In the beginning, ionic pumps had only 2 purposes, both essential for the survival of any cell, the first was expelling the ions that can form precipitates and the intake of the useful ions that are not dangerous, and the second was the energy interconversion between ionic gradients and forms of chemical energy like ATP hydrolysis/condensation.
yes, biology education in schools are terrible. remembering so many things actually is important, students keep reminding new thing even they in master degree...but the fun of science do not show in text book.
Seems like the author has a limited sense of wonder. Just because the teacher didn’t say “Isn’t X amazing!” He didn’t realize that it was amazing. Hint: Everything is amazing. Start looking at the world that way and you’ll do way better in everything you do. If teachers had to tell you all the amazing things in the world they wouldn’t have time to tell you how things work - which is also amazing. (I’m locked out of additional comments so have to respond to my critics by edit: I’m a molecular biologist and a computer scientist. I find the names are part of the wonder. It’s hard to describe how this can be true unless you have a wholistic sense of what wonder means. Maybe I have an overactive sense of wonder. )
One of my favourite course I took when studying physics at university was “Biological Nanomachines”. I find it absolutely bizarre that trying to gain a physical intuition for biology is not the norm. Throughout school I hated biology because it really did just feel like rote learning. This is embarrassing but I had such a poor understanding of what a cell was by the end of HS biology that I still had an image in my mind of a little conscious being that makes choices: “the cell wants to x” is language that we can’t use in a HS classroom
> “the cell wants to x” is language that we can’t use in a HS classroom
If you can’t use language like that you’re giving up on getting anything through to over half of the class. Trying to impart information to people who don’t care and aren’t interested is amazingly hard. Not using agentic framing makes it harder.
I don’t think we should teach wrong things because they are easier. It may be true that it’s easier to teach biology if you gives cells individual agency, but it’s just false
During the first part of the pandemic I watched the lectures for the Introductory Biology course [1] from MIT OpenCourseWare. I cannot recommend those highly enough!
Almost every lecture brought up and highlighted something really cool and fascinating. Like how RNA sequencing over the last couple of years has gone from expensive to almost free, and what its uses are. Or time-lapse of bacteria adapting to antibiotics. Or just the first lecture showing a video of someone sticking a syringe into a cell. There were even some labs that could be done via a normal web browser.
For me this was so much more engaging than the biology I was thought in high school, where we mostly learned things from outdated books.
To anyone who watches these, watch the basic chemistry series and organic chemistry lectures if you can find them. Follow that up with biochemistry to get a deeper understanding of the mechanisms of life
What are the prerequisites to handling these books? Would they be good for someone who never took Bio in college or even AP Bio in high school? Or would some remedial work be needed first?
They are self-contained. [1] discusses very little biology, it is mostly about biological sequences as formal languages. [2] is meant as an introduction to biology. [3] is self-contained, but to appreciate its content probably it would help to skim through Molecular Biology of the Gene by Watson et al.
"Everywhere you look—the compiler, the shell, the CPU, the DOM—is an abstraction hiding lifetimes of work. Biology is like this, just much, much worse, because living systems aren’t intentionally designed. It’s all a big slop of global mutable state".
Brings me to wonder, could we ever create anything so marvellous as what biology does so effortlessly.
It seems like the larger argument here (which I wholeheartedly agree with) is that the role of skilled teacher (whether it be human, book, YouTube, whatever) simply cannot be understated when it comes to creating that “spark” in a learner to develop and pursue their own passions.
What’s interesting to me, and what follows from this, I think, is that we therefore have a lever for creating more passionate people: create more extremely skilled teachers.
It’s obvious to me that this idea isn’t new; I just wonder why it’s so deprioritized at almost every level of education. (Not least the highest.)
Defund schools and create horrible working and learning conditions for those who stay. Create a problem and then say you are the only one who knows how to fix it. Prey on peoples’s fear, especially fear for their children. Sow mistrust and bigotry in their communities.
Unfortunately far, far too many in power who actively and intentionally do not have the best interests of students at heart.
“There should be no such thing as ‘good schools’ and ‘bad schools.’ All schools should be great.” shouldn’t be be a controversial opinion, but every time I’ve brought it up people get uncomfortable because parents now how precarious their child’s education can be. They’ve seen what has happened in other schools and they don’t want anything bad to happen to their child’s education. So even if things could be better, they fear change and anything that could rock the boat because they don’t won’t it to get worse. And they aren’t wrong to be afraid: how many times has a politician promised to fix education and it turns out the fix is something like one more layer of standardized testing, or cutting art classes, honors classes, special education services to “focus on the fundamentals” while class sizes balloon and the money from those cut classes and services just goes poof?
On top of that, there is the "education first" mindset. When a culture puts education first(e.g Eastern Asia countries), it not only means they will sacrifice their limited resources for education, it also means teachers are well respected and well paid across the society, relatively speaking.
Further, when a country focuses on CRT and LGBT+-education and Equity-grading at K-12 these days, school is no longer a place to prepare kids for a meaningful career, instead it is a playground to raise future everyone-is-a human rights activist or politician, we will have to rely on skillful immigrants that actually _DO_ things to sustain the economy, this pattern won't last very long obviously.
It's not a teacher's problem, or school's funding problem, it's more of a political problem to me these days(including the recent education-unrelated law changes). I feel lost as an independent.
About 18% born after 1995 are claiming they're LGBT+, I have friends that are LGBT+ so no worry about the scaremongering part, but some of those folks are going too far these days. Specifically I don't get it why it has to be on K-12's agenda where my kids attend with my tax money and they told me that it's offensive to address classmates if you use the wrong he/she, these days you better ask "how am I supposed to address you?", it's ridiculous.
I think this puts the cart before the horse, and presumes that you can find the right balance of teacher skills to student requirements and that you can maintain that balance year after year.
I honestly think you get more mileage thinking the opposite direction. If you want students with passion and a life long commitment to knowledge, then they're going to need to develop those skills first. At that point, in our modern world, a single oracle of generational knowledge isn't a particularly useful addition.
What you want is generalists that know how to motivate students and to help them find and utilize the resources they need. You might only need a bunch of librarians and proctors and can skip the specialists of the "oral tradition" entirely.
Pet theory: for most of human history, biology has been an increasingly complex detective story, a notepad of mysteries laying on a table next to an unfathomably massive evidence room stuffed with barely organized facts. This appeals to certain people and not to others. Only recently has it become possible to approach it from more of an engineering perspective, which appeals to a different set of people.
can you expand this thought? I'm on your path but not to your destination yet.
Can I summarize it as: Earlier, biology was "hunt and peck" or "observe" ... and now we're moving to a more "rigor of process & ability to create as seen in the past few decades of computer science now applying to biology" type of world?
Here's a story to illustrate. Recently there was a headline about some project at MIT that used CRISPR to figure out the function of every protein in a human cell (or something like that, I'm sure I misinterpreted it in some way). I told a friend who is an actual biologist, and he said of course they didn't literally do that, that would be impossible. So I guess what they really did was.... something-something with CRISPR that gave information about a wide range of proteins in the cell, or something. They added a lot of facts to the library. But they marketed it as if they had made a huge stride towards understanding how the whole machine works. That gets people like me more excited. We'd like to know how the machine works and then use that to make it work better.
I believe the parent refers to this [1,2,3] study. Indeed, this was about targeting many (11,923) genes with Perturb-seq (CRISPR screen with single-cell RNA-sequencing readout). There are two human cell lines used in the study (K562 and RPE1). For functional annotation, authors focused on 1,973 targeted genes that had strong transcriptional phenotype after the perturbation. As there's some correlation structure, that's what they studied, annotating clusters of individual perturbations using public databases (like STRING [4]) and literature. Seems like a lot of great work has been done here though stating that we now know all the functions of all the genes might be a bit of a stretch indeed.
Biology did seem more like a recitation of facts in school to me, but it was very different in university. I think part of it was having a pretty bad teacher, but also the school textbooks are just so much worse than the introductory textbooks for university.
I think some parts are fixable, others are difficult. A part of the problem is that you need to cover some parts in more depth if you want to really make sense of them. Without some basic chemistry and thermodynamics knowledge the entire metabolic pathways seem very arbitrary. This is probably hard to fit into the amount of time you have in school for those subjects.
Low effort comment, but, wow. This article is a super thorough version of the shower thought that, biology is a lot cooler once you reframe it as the study of naturally occurring self-replicators.
I think what is described here comes down to the fact that we don't have much (any?) _deep_ understanding of biology. The most concrete aspects of biology are observations. For example, anatomy is very well understood because it's essentially observations of structures within living organisms, as field it has been relatively stable for a long time, hence there are well-established methods for teaching anatomy.
There's a huge gap between the fundamental units of biology (biochemistry) and the resulting emergent behaviour (living things). We don't have a good bottom-up system to predict the emergent behaviour so we're mostly left with observing from the top down and poking/prodding sub-systems, hoping to gain some insight.
When so much of biology is observation without deep insight, it shouldn't be a surprise that biology is difficult to teach, and even more difficult to find beauty in for new students.
I would argue that depends on your definition of deep - we are certainly getting better at developing both genetic and chemical tools that allow us to probe specific pathways/sub-systems of biology, and read out the resulting perturbed phenotype(s).
> There's a huge gap between the fundamental units of biology (biochemistry) and the resulting emergent behaviour (living things). We don't have a good bottom-up system to predict the emergent behaviour so we're mostly left with observing from the top down and poking/prodding sub-systems, hoping to gain some insight.
I think this is just about the last thing that we will ever solve/figure out.
There are just a mind-boggling number of parameters, feedback loops, dynamic modifications, interactions, etc that are effecting cellular state (let alone organism state) - something that I think many CS oriented folks ignore when talking about "DNA as source code" (perhaps if program behavior depended on the size of indents, font, variable names, how many lines of code you wrote, the proximity in source location of different functions, etc).
> (perhaps if program behavior depended on the size of indents, font, variable names, how many lines of code you wrote, the proximity in source location of different functions, etc)
I think I've seen all of those functionalities implemented in esoteric programming languages! Nice comparison.
What you haven't seen is a CD-ROM sized program with no abstraction, encapsulation, or modularization, all implemented on a language that has all of those.
Oh, and that is being interpreted by more than one incompatible interpreter at the same time.
Yeah, I think programmers would better appreciate the complexity and subtlety of biology much better if they had to evolve their programs rather than code them up explicitly. (I say this as someone with degrees in both subjects.)
> There's a huge gap between the fundamental units of biology (biochemistry) and the resulting emergent behaviour (living things).
And then a very similar gap between the fundamental units of one small branch of biology, ecology, where the fundamental units are living things and the emergent behavior is everything you see outside your window! We have a lot of math that explains how things act and evolve together and it's all just the tiniest little smidge of what actually happens.
You can have what I would consider deep knowledge of a system without the ability to manufacture it or modify it. For instance, we have pretty deep knowledge of how the sun or other stars work, but we can’t even begin to dream about creating one, or controlling one.
In the same way, we know a lot of how biology works. Obviously nowhere near all of it; but we are far beyond just scratching the surface. It just turns out that modifying a working complex system is pretty hard.
> ow the sun or other stars work, but we can’t even begin to dream about creating one
Wolfram didn't answer "how much would a solar mass of hydrogen cost" for me, but it did tell me that the solar mass is 1.988435×10^33 grams, and another search found hydrogen prices [1] in the range of US$ 250 to 1350 per MT ... So just the financing on building another sun is going to be tricky.
[1] I know it's not all hydrogen but we'll burn those bridges when we get to them.
Hard disagree. We understand biology for the most part. The issue is in the exact implementations.
An analogy would be like understanding how a computer works. We know how chips are made, the physics behind them. We know how bits are stored and processing steps are executed. We also know generally how operating systems work. We have the full compiled code as assembly instructions. But we don’t have the source code of the OS. We just use crude tools to figure out The details of the OS and how it works on particular subsystems but because of the crude nature of the tools the knowledge we gain is ambiguous at times.
Having done a lot of biology, I'd disagree that we understand biology.
My background is neuro, so take that into account. But in neuro, we've nearly no idea about the larger parts of how it all works. Sure, yeah, electrically active neurons, we have that down. But the non electrically active parts? I mean, we're still debating about how much of the brain is glia. Like, we can't even agree on how to count. Don't get me started on synapses.
I don't even think we understand software, much less biology :-) We can only hope to understand the pieces that are most relevant to the business domain we're trying to solve (like curing a disease or expanding an online business). The complexity of both types of systems is just increasing exponentially over time, so there's little hope (or even need) to understand the whole thing. The challenge is, of course, to understand what's relevant in the first place.
And just like in software, we can only hope to come with the right levels of abstraction and disregard the irrelevant parts at each level of understanding.
For sure - and I don't think we necessarily have the ANYONE part either :-) The reason for that is folks who build systems often leave, and the details of why or even how they did something leave with them. At some point, there's no one in the company who understands certain things.
And the analogy with biology actually goes even further - just like in software, we "know" the code (DNA), but how does that translate to the behavior of the complex system (and business requirements in software), is lost to time and the sheer complexity of these systems.
So someone builds something and they leave, and you can’t figure out their code? Or more importantly, you’re suggesting it’s impossible to figure out that code by anyone? Seems a stretch don’t you think?
he's not saying it's impossible to figure out some piece of code, but "all" code. There is just too much of it! Going into even something as relatively simple as an operating system, let alone a whole ecosystem with drivers, internet protocols & more would take many lifetimes.
The "anyone" part comes because there are countless parts of those infinately complex systems that have no documentation and no maintainers. They can individually be reverse engineered if it is needed in an individual case, but nobody is going to do that for most of them.
Even in your own example, there’s no ambiguity in the fact that glia play a role is not under question. What percentage, IMO, is just a detail. My analogy still stands I think but I suppose that’s open to interpretation.
One question I implore you to ask yourself is how much of this “we don’t fully understand it” attitude comes from indoctrination of that way of thinking that you need to have to write grants and aggrandize your own research topic. As Sydney Brenner said a long time back, (in the context of mol bio) the fundamentals have been discovered, we can let the Americans figure out the details.
If we really understood biology, or even just precisely what aspects of a given phenomenon we need to investigate and how in order to understand it, we wouldn't have wasted a decade trying to reduce CVD by increasing HDL. Billions of dollars wouldn't have been wasted chasing the wrong mechanistic hypotheses in Alzheimer's treatment. Cancer would have already been sorted.
Since 15 years ago, extracellular vesicles went from particles used to export rubbish from cells, perhaps with some vague immune involvement, to one of the fundamental mechanisms involved in intercellular communication, carrying nucleic acids between cells.
The reality is the more we understand in biology, the larger we realise biology is and the relative amount of information that still needs to be figured out doesn't change much - especially when you look at it in terms of labour required, because what left is progressively less-low hanging fruit.
Biology is incomparable to computers, or to any other man-made machine. In computers the components interact in well-defined separable and independent roles. In a biological organism, all components depend heavily on not just one or two other components, but many. The role we impute for each mechanism often interfere and/or collaborate with other seemingly unrelated mechanisms, often in hierarchical and nonlinear fashion. That's why the function of even simple biological subsystems is so challenging to decipher. Context and interdependency are everywhere. That's why the oxymoronicism of a biologist “fixing a radio” rings so true.
Very much applicable to software as well :-) Modern systems are so complex there're very few people (if at all) who understand everything in them, even though they were man-made over time.
I suspect most biology majors would be plenty interested in these topics. The barrier tends to be the foundational knowledge they have to get through, like statistics and organic chemistry.
I presume the author now has that foundation, so it's unsurprisingly much easier to approach.
Yeah it doesn’t help that things like HS Chemistry and Chem 101 classes aren’t really Chemistry, it’s all the things to you need to know to get started doing and learning Chemistry.
It would be like if we saved learning how to read and write or learning Arabic numerals and basic number sense until early high school.
> I wanted to conjure models I could play with in my hand. I wanted a museum where I could walk around inside the epithelium during an immune response. I wanted to put ideas into physical space, like on a pinboard—TLRs go here, with the other innate armament; CD4+ T cells are there, in the adaptive world—but I wanted it to be as searchable, copy-pasteable, shareable, and composable as text.
To be fair, he wouldn’t have known what DNA and RNA were which he mentions many times in the article, if he hadn’t learned the acronym soup and the basics that biology’s wonder is built upon.
6th paragraph: Someone should have said this to me: Imagine a flashy spaceship lands in your backyard. The door opens and you are invited to investigate everything to see what you can learn. The technology is clearly millions of years beyond what we can make. This is biology. –Bert Hubert, “Our Amazing Immune System”
When I watch animations of how the cell works at a molecular level [0], I can't help but wonder how can this level of sheer complexity in dna transcription, protein production, and many other supporting functions in a single cell works in perfect harmony. It's mind boggling.
I admit that I'm biased, but I don't think this could have evolved through random processes. I'm a believer in Intelligent Design.
> I don't think this could have evolved through random processes.
It's a logical fallacy that complex processes cannot be created from random events. It certainly can, and evidence is abundant.
Biochemistry of life is an advanced form of brownian ratchet [1]. It started simple, but can get to absurd level of complexity due to selective pressure, and memory via genes. And selective pressure is nothing but maximizing for greatest replication.
There are many interesting philosophical questions inside biochemistry, but a Judeo-Christian Diety is not the most interesting.
So you look at everything that we've been able to figure out, things we didn't know about even a few decades ago, and you conclude "WELL I CAN'T SEE THE REST OF THE PUZZLE RIGHT NOW SO I GUESS MY IMAGINARY FRIEND DID IT"
Ok let's stay scientific. What are the odds of forming a single enzyme (necessary for life) composed of a chain of roughly 200 amino acids, each is drawn from a pool of 20 possible amino acids? 20^200, right? The estimated number of atoms in the entire universe is 10^80 atoms. Can you explain what process would consistently keep winning the protein lottery with those kind of odds?
> However, this argument is premised on the notion that genes and proteins evolve through a process analogous to tossing a coin multiple times. This is untrue because there is nothing analogous to natural selection when you are tossing coins. Natural selection is a non-random process, and this fundamentally affects the probability of evolving a particular gene.
> ... Modern proponents of intelligent design (ID) are usually too sophisticated to make such an error. Instead, they present a superficially more sophisticated probability-based argument. Their idea is best illustrated by example. ... ID proponents argue that it is the combination of improbability and matching a pattern that makes them suspect that something other than chance or purely natural processes are at work. They use the phrase “complex, specified information” to capture this idea. In this context, “complex” just means “improbable,” and “specified” means “matches a pattern.” ...
> The argument likewise founders on the question of complexity. According to ID proponents, establishing complexity requires carrying out a probability calculation, but we have no means for carrying out such a computation in this context. The evolutionary process is affected by so many variables that there is no hope of quantifying them for the purposes of evaluating such a probability.
> The calculation of odds assumes that the protein molecule formed by chance. However, biochemistry is not chance, making the calculated odds meaningless. Biochemistry produces complex products, and the products themselves interact in complex ways.
> The calculation of odds assumes that the protein molecule must take one certain form. However, there are innumerable possible proteins that promote biological activity. Any calculation of odds must take into account all possible molecules (not just proteins) that might function to promote life.
> The calculation of odds assumes the creation of life in its present form. The first life would have been very much simpler.
> The calculation of odds ignores the fact that innumerable trials would have been occurring simultaneously.
Richard Dawkin's book "Climbing Mount Improbable" "is about probability and how it applies to the theory of evolution. It is designed to debunk claims by creationists about the probability of naturalistic mechanisms like natural selection." (quoting https://en.wikipedia.org/wiki/Climbing_Mount_Improbable ).
The coolest thing about biology is that it's not just in every cell of your body, but every cell on life on earth.
But the funny thing about that is that the genes for say the 'helicase' looks like it was made by a copy machine, churned out by the millions, for every life on earth. But if you look very carefully, it's not a copy made from a master copy, but copied from each other. There are small mistakes made by this 'copy machine', so that you can trace the different generations of the copy of the 'helicase' based on what mistakes have been accumulated. You dig further, and you can map out different generations and make a tree like diagram. The further away from each other the two helicases are, the more mistakes have been accumulated.
You keep doing that for every life on earth, and you get something like this [1].
And then you dig further and realize that there is no Hand of God there, and creationism is a primitive explanation for something people didn't understand, like how lightning was God being angry.
> Here’s the central problem: molecules don’t behave that way. What is portrayed is wonderfully precise movement; it looks like the molecules are all directed, purposeful, and smooth. Take for instance the behavior of kinesin, that stalk-like molecule seen marching in a stately way down a tubule, with two “feet” in alternating step, towing a large vesicle. That’s not how it moves! We have experiments in which kinesin is tagged — it’s towing a fluorescent sphere — and far from a steady march, what it does is take one step forward, two steps forward, one step back, two steps forward, one back, one forward … it jitters. On average it progresses in one direction, but moment by moment it’s a shivery little dance. Similarly, the movie shows the monomers of tubulin zooming in to assemble a microtubule. No! What it should show is a wobbly cloud of monomers bouncing about, and when one bumps into an appropriate place in the polymer, then it locks down. I made this same criticism in my review of Mark Haw’s excellent book, Middle World, which does get it right. For purposes of drama and minimizing complexity and confusion, though, the animators of that video have stripped out one of the most essential properties of systems at that scale: noise, variability, and the stochastic nature of chemical interactions.
> That’s particularly unfortunate, because it is the seeming purposefulness of the activity of the cell that has made that clip so popular with creationists. It fits with their naive notions of directed activity at every level of the cell, and of their denial of the central role of chance in chemistry and biology.
When I was in college, I took Entomology and Zoology for my bio requirement. The first one was because the teacher had such high ratings in the student reviews. At one time, I could name all the orders of insects. No longer.
It did seem like just a bunch of memorization to me then. Much, much later, I took an Extension course in Molecular Biology, and what a difference! I still think the diagrams they draw for biological processes in Molecular Biology of the Cell are just stunningly beautiful, and completely blow away anything you'll ever see in a CS text.
But expecting to have those revelations when you don't know how anything works yet is foolish. You have to pay your dues.
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[ 4.4 ms ] story [ 317 ms ] threadI would disagree, we see the object code. If we had the source, with comments, genetic engineering would be much easier
Biology doesn't have the same clean levels of abstraction that we've developed in computer science. DNA functionally operates at many levels. It long term storage, local working storage, and it is used to compute. It's a single molecule that does everything.
Then you have to throw in all the secondary processes that modulate and regulate DNA replication, transcription, as well as activation/deactivation.
While it can be useful to lean on the abstractions we've developed to try to understand what DNA is doing those abstractions can only be taken so far.
Human civilization is severely time-limited (or just time-pressed). We can't wait millions of years running a simulation to optimize a little widget. We need to rely very much on high level design and comparatively little on efficiency and optimization. On the other hand, life cannot afford huge DNA (very costly), or energy waste (generally disfavorable from evolution). So human built systems tend to be of a low "Compute complexity": the computational complexity of obtaining solutions and solving problems themselves (like civil engineering structure problems, or design of objects) must be fairly low. For life, systems can be amazingly intricate, every tiniest cell a wonder that would probably take thousands of years for civilization to maybe be able to replicate. But it all ranges from about 130kbp to 8Mbp[1], which would be around 16Mbit/2Mbytes at most. So it fits (uncompressed) in a diskette (floppy).
Even now with powerful computers, we're still mostly constrained by cognition (specially human), you see simplicity all around you.
So if you look at the human world, you see (computational) simplicity everywhere, but the natural world has undergone trillions of generations of optimization to arrive at almost perfect (in an almost literal way) little machines, complicated but with a hidden amazing (size) simplicity.
I think there's a connection to be made to algorithmic inference as well. Originally we came up with ideas for Universal Inference (from ideas from by Solomonoff, Kolmogorov among others) [2][3], the most glaring candidate was the "size prior": evidence explainable by the least algorithmic information ought to be most likely (Solomonoff inference). Later, there were promissing ideas around an additional term: the "speed prior" (from Schmidhuber[4]) -- the biological word is one where the "size prior" (simplicity is most likely) works almost perfectly, and human civilization is one where the "speed prior" (computationally easy is most likely) is helpful.
[1] https://en.wikipedia.org/wiki/Bacterial_genome
[2] https://en.wikipedia.org/wiki/Kolmogorov_complexity
[3] https://en.wikipedia.org/wiki/Algorithmic_probability
[4] https://en.wikipedia.org/wiki/Speed_prior
Side note: I think intellectually one of the ways we're really far behind is recognizing Algorithmic Information theory as a foundation for statistics and metaphysics. We're very stuck making little progress on the metaphysical realm (which physics is advancing more into) because of a lack of widespread acceptance of those advanced tools for science. Algorithmic inference gives a solid basis for comparing metaphysical models and deep questions about the cosmos.
I’ve always loved biology. The intricacy of the systems, and how they work together is so fascinating and really presses the same buttons as computing.
https://www.unz.com/isteve/the-new-2018-pisa-school-test-sco...
Or is it something a little more profound?
Being able to read for meaning, extract information, combine knowledge from two texts, distinguish between what is stated and what’s implied, even to figure out something is implied, all of those are the kinds of things we expect an educated person to be able to do. PISA tests them. Trying to make people care about academic subject matter is very difficult because most people do not care and do not find it useful. Thus they forget most of what they learn in school. Insofar as education is forcing the tastes of one class on everyone else it can burn. Most people don’t care, just like most academics don’t care about sports. Forcing sports on them would also be an injustice.
Ah, yes, we should teach less science to everyone because that would be like forcing every academic to play sports. Perhaps physical and science education should be provided for every student? Education does not come at the expense of sports. If anything, the opposite is often true.
People are naturally curious. Shuffling them into the confines of some narrow and often purposeless maze fucks that up. That's a considerable portion of TFA, the institutional curricula stunted their interest in biology.
My curiosity was drugs, drugs lead me to biology, lead me to chemistry, physics - but it was independent study. Political challenges from my partner got me interested in history and anthropology, but it was all independently structured.
I think if the institution gave all these little knobheads enough autonomy to actually derive, from themselves, some real interest, they would ultimately end up intersecting with all the sciences, it's actually inevitable. Instead they're just forcefed a bunch of information they don't have a relationship with.
Sports is biology and mechanics is molecular biology and kinesiology and so on. It doesn't matter where you start, you track into that shit. Passion the latitude it lends to the people possessed by it is what allows us to push deeper and deeper. Not stunting intellectual growth by conditioning people into a state of repulsion at the premise of learning.
Take, for instance, a FT-working non-trad that scores above the mean. The mean who predominately consists of students who are FT-students. Should some respect not be paid to the considerable handicaps suffered by the non-trad? How do you even begin weight that?
Of course this is multiplied a million times over in several dimensions.
-- John Adams in a letter to his wife Abigail
I'm sure many people would love to have their children study the arts and humanities and develop profound insights into human nature and life itself. Unfortunately, many people are stuck studying mathematics and other subjects like it in the hopes of having a decent career.
In undergrad I took a bunch of biology and chemistry classes. It wasn't until I took Biochemistry (a senior level class) that everything came together. The biochemistry class I took was a re-telling of all the stories you learned in molecular biology but with the tools you acquire in organic chemistry.
Equipped with those tools I relearned the Krebs cycle and photosynthesis as real chemical reactions that make sense rather than a chain of facts to be memorized.
The class left me with a deep and profound reverence for life. Every process in a cell has a mechanism that can understood with chemistry. However, the magic of life exists where those processes come together and interact in incredibly complicated ways.
It's seductive to think that we should be able to tease apart this complicated processes and figure out how "life" works, and maybe someday we will. However, it's easy to underestimate the level of complexity and interconnectedness in these systems.
Many of us understand how hard it can be to debug a distributed system. Imagine trying to reverse engineer a distributed system with tens of thousands of interconnected services and messaging queues that all just sort of evolved and were not built with clean engineering practices.
I would go further to describe living systems as not just distributed and so on. Also they are self-assembling and self-repairing. They are redundant - which makes them more damage resistant and 'evolvable'.
Also, these complex assemblies of machines work at (mostly) room temperature and pressure. Except for extremophiles that can work down to freezing or up to boiling temperatures, or in acid or high pressure environments.
Also enzymes catalyse stereospecific reactions, or can use light to drive proton gradients across a lipid membrane, or reduce nitrogen gas. I've always found it funny the sci-fi obsession with 'nanomachines' when living systems are basically composed of exactly that.
Biology's difference from computers is in scope (organisms are whole factories who just happen to have computational abilities by necessity) and origin (organisms aren't designed, and this profoundly and significantly affects everything about them).
This distinction isn't as clear as you think. The active parts of ribosomes (the machines that translate mRNA into proteins) are catalytic RNA. There are organisms that use RNA to store templates (RNA viruses).
There’s 574 amino acids making four separate interlocking chains in a single globin, plus the heme, all just to bind 4 oxygen molecules. It’s simultaneously elegant but hugely complex, far above any discussion of the rna sequencing.
It’s a big part of the “gap” between chemistry and biology IMO.
Not sure if it was from a published paper, but the idea was that early proteins might have been large - say several hundred residues - but mostly disordered.
The smaller, more ordered 'domains' would then have evolved within these larger chains. Recombination and deletion would then have pruned down the disordered parts to leave more efficient smaller proteins.
No idea if that idea makes sense or has any research behind it, but it's quite a neat theory.
[0] https://arxiv.org/abs/1911.13299
Some years back, there seemed an opportunity to create an educational web interactive, a full-scale 3D folding sim, with hands-on direct manipulation, by aiming for plausible-not-correct folding. The simulation literature having built up lots of shortcuts for slashing computation costs, which sacrificed correctness but not plausibility. So one might variously knead a protein, alter it and its environment, and watch it flail. I wonder if anyone ever got around to it?
Humans have the most complicated hemoglobin molecule.
I recall the final exam being only a single question, with a bunch of blank lined pages to write your answer, and the question was something like "You just ate a ham sandwich. What happens to it?" A good answer needed to include everything down to the molecular/chemical level and tie it together all the way up to the macro scale, and I finally felt like that class had prepared me to tell the story.
Maybe toss a line or two in about the complexities of running a large data center and how your response time varies based on some sorcery.
Then you go the extra mile and weave a tale of electrons wrestling with their universe of invisible electromagnetic wave overlords that determine their fate while they embark on a treacherous journey to convey information thousands of kilometers across with blistering speed. Tell them of the aged electron saw a family member get attacked by a stray cosmic ray and the fright of the pack when one simply tunneled out of existence...
Not during an interview, though. The interviewer would see it as trolling (at best), and you would fail the interview. And for a good reason! Because as an engineer (and an intelligent person in general) you must be able to separate what is essential from the non-essential for the subject in question. For instance, the physics or the physiology of the process of pushing a key on a keyboard is probably not what the question was about, nor do those things in fact have much to do with typing, even (which you can do on a touchscreen or using the mouse).
https://aphyr.com/posts/342-typing-the-technical-interview
Oh it was N queens, but the line I remembered was "Summon a linked list from the void."
"You discover a mouse that can sense radiation. How does it do it?"
"You are riding a bicycle. Explain."
We had to do it in 2 pages, NSF grant rules on spacing and margins.
Oh man, where do I even start? Sensory input from the inner ear to balance, the networks that handle feedback from afferent signals from the periphery, efferent pathways to control motor movement. I don't even know all the details but it's mind bogglingly complex. Do I explain the molecular basis of action potentials? The modulating effects of inhibitory feedback within the networks? I feel like all of that barely scratches the surface of the insane complexity of neuronal networks
And how does one even begin to talk about our desire and internal drive to do things like ride a bicycle
It all happens so subtly, and your body does it perfectly with no input from the brain other than "I want to turn".
Assume an experienced rider, as learning is different.
Intention is set, requiring the basal ganglia and fore brain and either a notion of free will, determinism, or whatever you fancy. The area ahead is scanned and mapped for a clear path via retina- optic nerve - visual cortex and particularly the dorsal parietal pathway. Initial organised motor signal sequences originate in pre and pre pre motor areas, hitting motor strip of the brain, particularly those homoncular areas corresponding to legs, arms and torso. Basal ganglia loops prime these circuits into action and help maintain their engagement. Activated motor strip neurons pass through the internal capsule, down the pyramidal tracts, the spinal cord, and meet a lower motor neuron in the anterior horn, which then carries the baton and traverses out if the cord (still the central nervous system) into the body and to the final destination: a muscle. Electrical depolarisation along the axon hops rapidly between nodes of ranvier, enabled by insulation myelination. At the terrminal synaptic bouton lower motor neurons branch across a muscle body. Each neurons innervated patch is a motor unit; multiple combine into a motor pool. Depolarisation triggers fusion of vesicles to the membrane endplate and release of acetylcholine into the thin synapse. Rapid diffusion moves thr snall molecukes to the muscle membrane, the sarcolemma, who then bind to the membrane spanning Nm nicotinic receptors which open and allow a rapid flooding of sodium into the muscle cell syncitium and efflux of potassiun into the extra cellular matrix. Depolarisation of that muscle allows further calcium released from the sarcoplasmic reticulum to activate protein machinery; myosin and actin run across each other and fibres contract. With enough activity concentric movement is achieved across the associated joint. In a manner similar to walking, various spinal reflexes and the spinal locomotor pattern generator create a local, fast framework for actualisation of the impulses. Feed back on state of the musculature ascends the spine via dorsal root ganglia and the dorsal horn. Amongst these are proprioceptive afferents, rapidly feeding back state of tension in muscle fibres from golgi tendon organs along highly myelinated type 1a fibres. These signals pass into the cerebellum where they are co processed with signals from the eyes and vestibular system. The cerebellum modulates the intensity of descending motor activity by comparing expected to perceived muscle state. It also orchestrates balance by integrating general body state, visual cues and vestibular information. In this way the small and large oscillations of riding the bike are maintained and constrained into an orderly process.
Experienced riders can dedicate higher function, i.e Executive frontal areas to other tasks, or to refined modulation of thr task to overcome specific issues. Beginners must use all their frontal powers to focus attention on the task, painstakingly sequence actions, and reflect on the numerous errors and their consequences. Learning is slow, multi system, and largely independent of autobiographical memory.
Think of a even a simple walking protein like Kinesin [1]. What is not shown in the video is that this is all happening in a hurricane of molecules battering it from all sides. Each part of the structure is being pushed, pulled, bent, robot made out of sticks and rubber bands.
https://www.youtube.com/watch?v=y-uuk4Pr2i8
That's one of the best things I read all week.
I became interested in biology as a physicist when I realised that all of the problems, on some level, boil down to putting a load of lego pieces in a box, shaking it up with some energy not terribly different to k_B T, and getting a fully-formed, self-replicating lego models out the other end. It's all physics. It's all utterly incomprehensibly mind-bogglingly complex with layers of complexity wrapped around each other, and far out of the realms of either physics or chemistry to compute completely. It's why I work at the intersection of the two fields.
Another famous paper, often-mentioned, related to this is "How a biologist would fix a transistor radio", essentially armed only with a shotgun. The tools of modern molecular biology may be scalpels rather than shotguns, but still, the idea is arguably the same.
Sounds fascinating. May I ask which field that is/what type of work you do?
In their profile
https://www.youtube.com/results?search_query=Drew+Berry+anim...
Meanwhile, later when you get to higher education, math just kind of explode into this creative problem solving field with loads of interesting problems and ways to reason about them, but you almost have to relearn it/properly learn the basics over again when you get there, because you never learned why or how the basics works, just the input and output of the basics.
In high school I really hated biology and chemistry. It was just a bunch of abstract stuff. What made me (re)discover biology was taking up gardening. To me gardening is like applied biology. After a while you really start to get a sense of how it all works and just how unbelievably complex life systems are: photosynthesis, the carbon cycle, the different water cycles, how soil life affects the plants that grow in it, and how incredibly resourceful plants are in interacting with their environment (not to mention insects and other creatures higher up the food chain...)
Bit by tiny bit, and painstakingly slowly.
Evolution promotes fitness enhancing functionality, so we should expect biological processes to be useful for some purpose and hence not be distributed like a random graph (e.g. Erdos Reyni graphs). Indeed, if we look at biological structures, we can find that the causal networks they form are far from random. Furthermore, there are often repeated motifs present. These motifs are quite simple and seem to map neatly onto human understandable concepts (like XOR gates or autoregulators or feedforward networks etc.)
And often, the overall graphs seem like they're tree like rather than some complicated mess of feedback loops (barring autoregulation). This kind of structure is quite modular, and hence we can leverage our understanding of component parts to understand greater and greater pieces of the organism.
There are two problems with this arguement: one, that a lot of the data used for it is not nearly exhaustive. Maybe the people examining biological circuitry stumbled on the rare areas where there are repeated sub-components. Second, even if there are repeated sub components, why should we get modularity i.e. few connections, mostly local?
The former may not be an issue if there hasn't been a lot of dedicated effort towards finding human comprehensible structure in biological circuits, which there might not have been. These things are big and complicated, with many constituent parts, and teasing out the underlying structure may require loads of computation and statistical analysis, which was hard for most of the history of biology.
The latter is not adressed in the book review, or in the comments, but the review author's work makes me it plausible to me that modularity will be common in biological systems. I don't have a good summary of that, or can clearly articulate why I'm hopeful about this. But read the rest of the work of the writer of the article if you're interested in this kind of stuff (key words: natural abstractions, interfaces, selection theorems).
[1] https://www.lesswrong.com/posts/bNXdnRTpSXk9p4zmi/book-revie...
https://ccsb.scripps.edu/goodsell/
It does gunk up but it takes a few decades.
This continuous ion pumping is a major component of the energy consumption of a living being when it is idle, apparently doing nothing.
In the beginning, ionic pumps had only 2 purposes, both essential for the survival of any cell, the first was expelling the ions that can form precipitates and the intake of the useful ions that are not dangerous, and the second was the energy interconversion between ionic gradients and forms of chemical energy like ATP hydrolysis/condensation.
If you can’t use language like that you’re giving up on getting anything through to over half of the class. Trying to impart information to people who don’t care and aren’t interested is amazingly hard. Not using agentic framing makes it harder.
Almost every lecture brought up and highlighted something really cool and fascinating. Like how RNA sequencing over the last couple of years has gone from expensive to almost free, and what its uses are. Or time-lapse of bacteria adapting to antibiotics. Or just the first lecture showing a video of someone sticking a syringe into a cell. There were even some labs that could be done via a normal web browser.
For me this was so much more engaging than the biology I was thought in high school, where we mostly learned things from outdated books.
[1]: https://ocw.mit.edu/courses/7-016-introductory-biology-fall-...
I can recommend 3 really good books, SICP-level good:
1. Biological Sequence Analysis by Durbin et al.
How to model DNA, RNA and proteins as probabilistic languages / generative models. There is also a companion book with all exercises solved.
2. Physical Biology of the Cell by Phillips et al.
A massive amount of quantitative cell biology models that use simple undergrad physics.
Originated from a Caltech course. After reading this book, all other intro to biology books look like a bag of tricks.
3. An Introduction to Systems Biology: Design Principles of Biological Circuits by Alon.
Bacterial circuits from the perspective of an electrical engineer.
Sadly, NSF discontinued funding for a totally epic Cold Spring Harbor Summer school for postdocs that taught [2,3].
A shame, we need more quantitative biology and less bags of tricks.
Brings me to wonder, could we ever create anything so marvellous as what biology does so effortlessly.
What’s interesting to me, and what follows from this, I think, is that we therefore have a lever for creating more passionate people: create more extremely skilled teachers.
It’s obvious to me that this idea isn’t new; I just wonder why it’s so deprioritized at almost every level of education. (Not least the highest.)
Defund schools and create horrible working and learning conditions for those who stay. Create a problem and then say you are the only one who knows how to fix it. Prey on peoples’s fear, especially fear for their children. Sow mistrust and bigotry in their communities.
Unfortunately far, far too many in power who actively and intentionally do not have the best interests of students at heart.
“There should be no such thing as ‘good schools’ and ‘bad schools.’ All schools should be great.” shouldn’t be be a controversial opinion, but every time I’ve brought it up people get uncomfortable because parents now how precarious their child’s education can be. They’ve seen what has happened in other schools and they don’t want anything bad to happen to their child’s education. So even if things could be better, they fear change and anything that could rock the boat because they don’t won’t it to get worse. And they aren’t wrong to be afraid: how many times has a politician promised to fix education and it turns out the fix is something like one more layer of standardized testing, or cutting art classes, honors classes, special education services to “focus on the fundamentals” while class sizes balloon and the money from those cut classes and services just goes poof?
Further, when a country focuses on CRT and LGBT+-education and Equity-grading at K-12 these days, school is no longer a place to prepare kids for a meaningful career, instead it is a playground to raise future everyone-is-a human rights activist or politician, we will have to rely on skillful immigrants that actually _DO_ things to sustain the economy, this pattern won't last very long obviously.
It's not a teacher's problem, or school's funding problem, it's more of a political problem to me these days(including the recent education-unrelated law changes). I feel lost as an independent.
If anything, schools and educations are harmed by scaremongering and mythmaking like you do.
I think this puts the cart before the horse, and presumes that you can find the right balance of teacher skills to student requirements and that you can maintain that balance year after year.
I honestly think you get more mileage thinking the opposite direction. If you want students with passion and a life long commitment to knowledge, then they're going to need to develop those skills first. At that point, in our modern world, a single oracle of generational knowledge isn't a particularly useful addition.
What you want is generalists that know how to motivate students and to help them find and utilize the resources they need. You might only need a bunch of librarians and proctors and can skip the specialists of the "oral tradition" entirely.
Can I summarize it as: Earlier, biology was "hunt and peck" or "observe" ... and now we're moving to a more "rigor of process & ability to create as seen in the past few decades of computer science now applying to biology" type of world?
[1]: https://news.mit.edu/2022/crispr-based-map-ties-every-human-... [2]: https://www.cell.com/cell/fulltext/S0092-8674(22)00597-9 [3]: https://gwps.wi.mit.edu/ [4]: https://string-db.org/
I think some parts are fixable, others are difficult. A part of the problem is that you need to cover some parts in more depth if you want to really make sense of them. Without some basic chemistry and thermodynamics knowledge the entire metabolic pathways seem very arbitrary. This is probably hard to fit into the amount of time you have in school for those subjects.
There's a huge gap between the fundamental units of biology (biochemistry) and the resulting emergent behaviour (living things). We don't have a good bottom-up system to predict the emergent behaviour so we're mostly left with observing from the top down and poking/prodding sub-systems, hoping to gain some insight.
When so much of biology is observation without deep insight, it shouldn't be a surprise that biology is difficult to teach, and even more difficult to find beauty in for new students.
> There's a huge gap between the fundamental units of biology (biochemistry) and the resulting emergent behaviour (living things). We don't have a good bottom-up system to predict the emergent behaviour so we're mostly left with observing from the top down and poking/prodding sub-systems, hoping to gain some insight.
I think this is just about the last thing that we will ever solve/figure out.
There are just a mind-boggling number of parameters, feedback loops, dynamic modifications, interactions, etc that are effecting cellular state (let alone organism state) - something that I think many CS oriented folks ignore when talking about "DNA as source code" (perhaps if program behavior depended on the size of indents, font, variable names, how many lines of code you wrote, the proximity in source location of different functions, etc).
I think I've seen all of those functionalities implemented in esoteric programming languages! Nice comparison.
Oh, and that is being interpreted by more than one incompatible interpreter at the same time.
And then a very similar gap between the fundamental units of one small branch of biology, ecology, where the fundamental units are living things and the emergent behavior is everything you see outside your window! We have a lot of math that explains how things act and evolve together and it's all just the tiniest little smidge of what actually happens.
In the same way, we know a lot of how biology works. Obviously nowhere near all of it; but we are far beyond just scratching the surface. It just turns out that modifying a working complex system is pretty hard.
Wolfram didn't answer "how much would a solar mass of hydrogen cost" for me, but it did tell me that the solar mass is 1.988435×10^33 grams, and another search found hydrogen prices [1] in the range of US$ 250 to 1350 per MT ... So just the financing on building another sun is going to be tricky.
[1] I know it's not all hydrogen but we'll burn those bridges when we get to them.
An analogy would be like understanding how a computer works. We know how chips are made, the physics behind them. We know how bits are stored and processing steps are executed. We also know generally how operating systems work. We have the full compiled code as assembly instructions. But we don’t have the source code of the OS. We just use crude tools to figure out The details of the OS and how it works on particular subsystems but because of the crude nature of the tools the knowledge we gain is ambiguous at times.
My background is neuro, so take that into account. But in neuro, we've nearly no idea about the larger parts of how it all works. Sure, yeah, electrically active neurons, we have that down. But the non electrically active parts? I mean, we're still debating about how much of the brain is glia. Like, we can't even agree on how to count. Don't get me started on synapses.
And just like in software, we can only hope to come with the right levels of abstraction and disregard the irrelevant parts at each level of understanding.
And the analogy with biology actually goes even further - just like in software, we "know" the code (DNA), but how does that translate to the behavior of the complex system (and business requirements in software), is lost to time and the sheer complexity of these systems.
The "anyone" part comes because there are countless parts of those infinately complex systems that have no documentation and no maintainers. They can individually be reverse engineered if it is needed in an individual case, but nobody is going to do that for most of them.
One question I implore you to ask yourself is how much of this “we don’t fully understand it” attitude comes from indoctrination of that way of thinking that you need to have to write grants and aggrandize your own research topic. As Sydney Brenner said a long time back, (in the context of mol bio) the fundamentals have been discovered, we can let the Americans figure out the details.
If we really understood biology, or even just precisely what aspects of a given phenomenon we need to investigate and how in order to understand it, we wouldn't have wasted a decade trying to reduce CVD by increasing HDL. Billions of dollars wouldn't have been wasted chasing the wrong mechanistic hypotheses in Alzheimer's treatment. Cancer would have already been sorted.
Since 15 years ago, extracellular vesicles went from particles used to export rubbish from cells, perhaps with some vague immune involvement, to one of the fundamental mechanisms involved in intercellular communication, carrying nucleic acids between cells.
The reality is the more we understand in biology, the larger we realise biology is and the relative amount of information that still needs to be figured out doesn't change much - especially when you look at it in terms of labour required, because what left is progressively less-low hanging fruit.
Very much applicable to software as well :-) Modern systems are so complex there're very few people (if at all) who understand everything in them, even though they were man-made over time.
I presume the author now has that foundation, so it's unsurprisingly much easier to approach.
It would be like if we saved learning how to read and write or learning Arabic numerals and basic number sense until early high school.
VR anyone?
Some aspects of the field still fascinate me, but I know if I ever bothered to engage the interest I'd be broke.
I admit that I'm biased, but I don't think this could have evolved through random processes. I'm a believer in Intelligent Design.
https://youtu.be/X_tYrnv_o6A https://youtu.be/7Hk9jct2ozY https://youtu.be/fpHaxzroYxg
It's a logical fallacy that complex processes cannot be created from random events. It certainly can, and evidence is abundant.
Biochemistry of life is an advanced form of brownian ratchet [1]. It started simple, but can get to absurd level of complexity due to selective pressure, and memory via genes. And selective pressure is nothing but maximizing for greatest replication.
There are many interesting philosophical questions inside biochemistry, but a Judeo-Christian Diety is not the most interesting.
[1] https://en.wikipedia.org/wiki/Brownian_ratchet
So goddamn stupid that it's just sad.
[0] https://en.wikipedia.org/wiki/God_of_the_gaps
> However, this argument is premised on the notion that genes and proteins evolve through a process analogous to tossing a coin multiple times. This is untrue because there is nothing analogous to natural selection when you are tossing coins. Natural selection is a non-random process, and this fundamentally affects the probability of evolving a particular gene.
> ... Modern proponents of intelligent design (ID) are usually too sophisticated to make such an error. Instead, they present a superficially more sophisticated probability-based argument. Their idea is best illustrated by example. ... ID proponents argue that it is the combination of improbability and matching a pattern that makes them suspect that something other than chance or purely natural processes are at work. They use the phrase “complex, specified information” to capture this idea. In this context, “complex” just means “improbable,” and “specified” means “matches a pattern.” ...
> The argument likewise founders on the question of complexity. According to ID proponents, establishing complexity requires carrying out a probability calculation, but we have no means for carrying out such a computation in this context. The evolutionary process is affected by so many variables that there is no hope of quantifying them for the purposes of evaluating such a probability.
Back in the 1990s, the newsgroup talk.origins put together a long index of creationist claims. Your example is http://www.talkorigins.org/indexcc/CB/CB010.html
> The calculation of odds assumes that the protein molecule formed by chance. However, biochemistry is not chance, making the calculated odds meaningless. Biochemistry produces complex products, and the products themselves interact in complex ways.
> The calculation of odds assumes that the protein molecule must take one certain form. However, there are innumerable possible proteins that promote biological activity. Any calculation of odds must take into account all possible molecules (not just proteins) that might function to promote life.
> The calculation of odds assumes the creation of life in its present form. The first life would have been very much simpler.
> The calculation of odds ignores the fact that innumerable trials would have been occurring simultaneously.
It links to further discussion at http://www.talkorigins.org/faqs/abioprob/abioprob.html ("Lies, Damned Lies, Statistics, and Probability of Abiogenesis Calculations")
Richard Dawkin's book "Climbing Mount Improbable" "is about probability and how it applies to the theory of evolution. It is designed to debunk claims by creationists about the probability of naturalistic mechanisms like natural selection." (quoting https://en.wikipedia.org/wiki/Climbing_Mount_Improbable ).
Five copies of the book are available to borrow right now for free (with an account) from archive.org, at https://archive.org/search.php?query=%22Climbing+Mount+Impro... .
All of these explain why your probability calculation is not meaningful.
But the funny thing about that is that the genes for say the 'helicase' looks like it was made by a copy machine, churned out by the millions, for every life on earth. But if you look very carefully, it's not a copy made from a master copy, but copied from each other. There are small mistakes made by this 'copy machine', so that you can trace the different generations of the copy of the 'helicase' based on what mistakes have been accumulated. You dig further, and you can map out different generations and make a tree like diagram. The further away from each other the two helicases are, the more mistakes have been accumulated.
You keep doing that for every life on earth, and you get something like this [1].
And then you dig further and realize that there is no Hand of God there, and creationism is a primitive explanation for something people didn't understand, like how lightning was God being angry.
[1] https://www.sciencedirect.com/science/article/pii/S235234091...
> Here’s the central problem: molecules don’t behave that way. What is portrayed is wonderfully precise movement; it looks like the molecules are all directed, purposeful, and smooth. Take for instance the behavior of kinesin, that stalk-like molecule seen marching in a stately way down a tubule, with two “feet” in alternating step, towing a large vesicle. That’s not how it moves! We have experiments in which kinesin is tagged — it’s towing a fluorescent sphere — and far from a steady march, what it does is take one step forward, two steps forward, one step back, two steps forward, one back, one forward … it jitters. On average it progresses in one direction, but moment by moment it’s a shivery little dance. Similarly, the movie shows the monomers of tubulin zooming in to assemble a microtubule. No! What it should show is a wobbly cloud of monomers bouncing about, and when one bumps into an appropriate place in the polymer, then it locks down. I made this same criticism in my review of Mark Haw’s excellent book, Middle World, which does get it right. For purposes of drama and minimizing complexity and confusion, though, the animators of that video have stripped out one of the most essential properties of systems at that scale: noise, variability, and the stochastic nature of chemical interactions.
> That’s particularly unfortunate, because it is the seeming purposefulness of the activity of the cell that has made that clip so popular with creationists. It fits with their naive notions of directed activity at every level of the cell, and of their denial of the central role of chance in chemistry and biology.
It did seem like just a bunch of memorization to me then. Much, much later, I took an Extension course in Molecular Biology, and what a difference! I still think the diagrams they draw for biological processes in Molecular Biology of the Cell are just stunningly beautiful, and completely blow away anything you'll ever see in a CS text.
But expecting to have those revelations when you don't know how anything works yet is foolish. You have to pay your dues.
Bonus points for pictures!