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.
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...
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.
"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
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.
It all happens so subtly, and your body does it perfectly with no input from the brain other than "I want to turn".
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."
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.
Humans have the most complicated hemoglobin molecule.
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?
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).
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...)
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...
Bit by tiny bit, and painstakingly slowly.
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.