Imagine that every one of people who designed computers, from the chip makers, to the ones making the instruction set, to the firmware coders etc. had no concept of "abstraction", adherence to standards or even a logical, coherent design. Would computers be more like cells then?
I could imagine a CPU which does not have a clock to keep everything syncronized - where the die is not separated into logical areas of decoders ALUs etc but just a jumped mess of wires and logic gates that somehow works to do computation. There is no "assembly language" for this CPU, but give it the right binary inputs at the right time and it will probably do what you ask it to (depending on its complicated internal state).
You could create this situation explicitly using FPGAs.
>no concept of "abstraction", adherence to standards or even a logical, coherent design
There's no concept of abstraction, but there is remarkable consistency in the genetic code for all that. The fact that these tiny machines are understandable at all is remarkable. Our biosphere is effectively a single process with an uptime of ~4 billion years, including the ability to recover from meteor strikes that killed off 99% of species, multiple times (albeit with different species!)
https://en.wikipedia.org/wiki/Permian-Triassic_extinction_ev...
Logically, I know this can’t be worse than the K-T boundary, where a whole continent of forests simultaneously caught fire, and a hypercane formed over the Yucatán. On the first day. But it sure feels that bad. We live in a world people flip out because one wolf was reintroduced to a broken ecosystem yet no one gives a shit about the million people a year who die from fossil fuel air pollution. That one’s real: https://arstechnica.com/science/2021/12/fossil-fuel-combusti...
There can high levels of inequality, yet this can still be true.
[0] https://en.wikipedia.org/wiki/Asynchronous_circuit#Asynchron...
Honestly that sounds like basically what we got. The "design coherence" on the scale of the entire industry has been pretty low for decades.
I am sure things could be more optimal somehow, but the industry generally operates on a principle of interfaces - and it's true, the higher level you go (all the way up to the people using the technology) the messier it gets, but it certainly doesn't feel like an organism with no concept of "abstraction" as the parent describes.
Like everyone uses NixOS/Nixpkgs and also it's more ergonomic.
> But the industry generally operates on a principle of interfaces
Aha! Actually I would say it does not, and there in is the problem. Most interfaces only have a few implementations, and as much as I love FOSS it has made that problem worse (in the context of the economy we've got). The interfaces we do have tend to become monotonically more complex over time as there is no systematic culling, and the result is we increasingly drown in accidental complexity.
Basically, accumulated implementations are viewed as big scary problems were maintaince to simplify is a cost no one wants to bare.
A lot of this is the same critique as http://langsec.org/, generalized to areas other than security.
I think your thought experiment makes the mistake that such an abstraction-less computer would still be designed and built by humans. That won't work, because no human can understand the whole of it all at once, hence the need for abstractions. A computer "designed" by an incredibly complex computer program could push that barrier but probably not break it. If you really want an analogy to actual life, such a computer would have to be the outcome of some evolution-like process. And no, not what people like to call "evolutionary process" or something like that today, but actual evolution that mirrors biology -- and quickly becomes too complex to understand by humans.
3 DNA base pairs abstract to an amino acid which has chemical properties. It's just theres some redundancy which 3 base pairs get converted to an amino acid. And if there's a single mistake somewhere with just one amino acid, the structure of the thousand amino acid long protein probably has some fault tolerance there.
Those amino acids are arranged in a way in the DNA code to make bigger motifs, alpha helices, beta sheets, active sites, coordinating complexes with inorganic ions.
It's all in the DNA code, though I suspect this train of thoughts is influenced by epigenetics. Parts of the DNA code also responsible for the regulation of how much the protein itself is expressed. That can still abstract to conditional and loop structures.
Yes some of the base pairs strongly bind together in the double helice which can influence stability of the DNA strand. The GC pairing has 3 hydrogen bonds, where AT has 2, but I think this influences the distribution of junk DNA, as you still need codons to make proteins which is the central dogma of biology.
The first level of problems is that what you're describing applies to only a tiny percentage of the DNA in a cell, and even for that DNA, it may overlap/exist in parallel with completely different "codings" that are more to do with structure, packing and replication machinery than protein coding.
I don't mean to distress you, but you are a biological computer.
Just rejoice that you needn't read the release notes.
“Thanks for being human. This update changes your DNA and may contain bug fixes and performance improvements. Keep on humaning!”
I see your point. But I would like to make a counterargument.
If you consider the time scale with which biological systems have to work, you probably won't call their design illogical or incoherent. Computer programs have been around for not even a century. Billion years is a whole different ballgame. Can anyone even imagine writing a computer program with any guarantee that it would be readable by someone 1000 years from now? We have a tough time figuring out what our our own code does a few months after writing them.
And wrt fault tolerance, resilience, I don't know, the design language of nature definitely seems far more evolved..
[0] https://bioone.org/journals/acta-palaeontologica-polonica/vo...
Nature works on a "just good enough" principle, in general.
Life is, at its core, an informational phenomenon. It's not a coincidence, or a "rough and oversimplified analogy", that we have computer viruses to mirror biological viruses - they are both consequences of the same underlying principles.
But doesn't the software mindset, again, have better mental models for things like that than most other disciplines? Think "relying on undefined behavior", or that old question of bug or feature. Sure, there are many pitfalls and possibilities for wrong conclusions to be drawn, but I'd dare to say that the "coding mind" is better equipped to become aware of them than others and not worse.
edit, an example: we can imagine a piece of code that works both when compiled as java and when interpreted as groovy, but has a different outcome depending on what language it is. We might even have written some for entertainment (reminds me of that ambiguous-on-Apple PNG that made the rounds a few days ago). And we could easily imagine a system that would collapse unless it occasionally gets both, the "java" outcome and the "groovy" outcome. We certainly wouldn't deliberately build a system like that, but we could easily do if there was, say, an obfuscated gradle contest. And "deliberate" is out of scope anyways. Enter eons of dicerolls, enter a system that mostly controls itself via balancing (as we know full well from high level pharmaceutical science). Yeah, it's unpredictably complex, and the coding mind should be the last to jump from "GATC is essentially digital" to underestimating that complexity and unpredictability.
edit2: I think the core of what I wanted to say (but never even tried) is that someone not used to code will likely try to wrap their head around the "ifs" and "comments" and be so occupied by that that task that the result will be a mental model of predictability that won't map well to observed reality, whereas the coder, with years of "the daily wtf", won't for a moment see any contradiction between observed reality and "lots of kind of code-like things"
I think it does make sense to deal with DNA from an information science perspective. It might not be sufficient, there might be higher levels of emergence to be taken into account.
But I think that without having a bit of an understanding of information science, it's really really hard to understand anything about how DNA works at all.
That said, the article actually seems to be pretty good starting point for programmers who want to get an initial intro to molecular genetics, and the author doesn't take themself TOO seriously.
I think the coding analogy is more quantum computing than anything we currently know, which is why the binary analogy seems to fail. It is computing but on a whole 'notha level. Quibit instead of Binary, or maybe something we are yet to understand.
The chief complaint I have about that article is that it does not even get into the fact that we actually have two genomes that are symbiotic; Our Nuclear Genome and out Mitochondrial Genome. Glad he did touch on the epigenetics however.
I do know that understanding your genetics at the level that I have can help you manage any unknown chronic condition. It turns out my mitochondrial genetics were much more important for me (I am pretty sure I have a Complex I deficiency, thought really a trait) and that a sort of mismatch with my Nuclear Genome (mostly Calcium, Potassium, and Sodium signalling) makes me very sensitive to my environment through oxidative stress signalling.
If you can see this complex dance you will appreciate just how wonderful and complex of a machine we are that we will never fully understand. In the end my cure is more art than science. More feeling than data.
Some examples that might bother you: - Long Chain Omega 3 PUFAs are crucial to my health and I cannot eat short chain PUFAs for any extended period. This is due o my FADS1 and FADS2 genetics
- I am Electro-Hypersensitive. Exposure to household low frequency EMFs from wiring increases oxidative stress ion my body and causes me pain, insomnia, and mood disturbance. This is mainly from how EMFs trigger the Volatage Gated Ion Channels.
- Exposure to chemicals, which includes many medicines, triggers an immune reaction (drug induced Lupus) from the oxidative stress which is created through their metabolism by CYP450 enzymes.
I am able to remediate these issues by diet and environment changes and mostly through the supplements; Asocrbic Acid, Manganese, Zinc/Copper and P5P.
Wow, I've never heard that before - do you have any good papers you can link to so I can read some more?
The most studied ion channels are the Voltage Gated Calcium Ion Channels
https://onlinelibrary.wiley.com/doi/full/10.1111/jcmm.12088
https://www.spandidos-publications.com/ijo/59/5/92
https://www.sciencedirect.com/science/article/abs/pii/S01434...
https://emmind.net/openpapers_repos/Applied_Fields-Hazads/Va...
https://www.youtube.com/watch?v=0RIskTMLV40
I feel the research is lacking on this because there is a large genetic component. I do not feel LF-EMFs are a danger to everyone, but certain people, like me, with gene changes in CACNA1C, might be more affected by LF-EMFs. It is no coincidence that CACNA1C is also linked to pretty much all the mood disorders.
And I am in no way saying that LF-EMFs are the cause of all mood disorders.
I sat in on classes at UNC Chapel Hill and I only use peer reviews studies. If you want to challenge my knowledge please do so. I have spent 20 years doing this.
I have a livelong disability and it runs in my family. NO one knows the cause but I am pretty sure it is mitochondrial and is largely affecting oxidative stress.
A good place to start is uploading any data you have into https://promethease.com/. I would ignore most of the "good" and "bad" results, I only used it to find rare SNPs that might be linked to my disorder.
The best thing you can do is get your whole genome run. You can do it now for about $200.
That is just a start, it is useful but incomplete. I usually end up searching for SNPs in my text file based on research I read. I made a bash program which I can use to run a bunch of SNPS of a specific gene.
Oh, also:
https://www.ncbi.nlm.nih.gov/snp/
Eh, I think you are correct re: being a rough and oversimplified analogy, but those same assumptions of how digital tech works break down when doing digital tech at large scale as well (FAANG).
A well-known example + cases I've seen repeat over the past few years include: IO Latency variations because of noise (someone yelling at a disk array), bit flips on the network just right to evade TCP error checking, ICMP packets bit flipped just right so they crash network control plane software. ASICs with die defects such that the brick if you try to load a specific set of subnets into TCAM n times, CPUs that wear out such that specific instructions return erroneous results after n years of use, and so on.
Basically, with warehouse-scale computing you encounter the consequences of very unlikely events every day, and many unlikely events happen at the interface of digital to analog (which is defined by... different chemical and physical properties).
At some point you accept that the details will always be fuzzy, but if the model works well enough most of the time, it's good enough for most practitioners. You just need to socialize that there are exceptions and make sure people know where to turn when something doesn't behave as expected.
Weak sectors on optical media and the classic https://en.wikipedia.org/wiki/Lace_card are similar analogies in computing.
Also it seems part of the bootstrapping lies in the female womb hosting the foetus.
I think the author's "digital" explanation for DNA is more helpful than problematic because we can just understand where that knowledge can be appropriately applied. Focusing on the higher abstraction of DNA as "information" or "data" is important in bioinformatics: https://en.wikipedia.org/wiki/Bioinformatics
And analyzing DNA as "information" and not just analog chemistry is also important in CRISPR gene editing. Yes, the engineers creating machines used CRISPR editing need to know more "analog" aspects of biology but other scientists can focus on the "digital data" perspective.
I think a similar digital-vs-analog split happens in computers. Consider the following different physical (analog) realizations of computation devices:
- wood gates (https://www.youtube.com/watch?v=GcDshWmhF4A)
- vacuum tubes
- silicon wafers of transistors
- optical gates using light
Can we extract a useful "information data" perspective that transcends all of those vastly different devices? Yes, we call it "computer science". We're discovering that many "computer software" ideas also apply to DNA. Scientists can look at the human genome as 750 megabytes of code and try to "reverse engineer" it. It's like forensic hackers using IDAPro/Ghidra to reverse engineer a malware binary to understand how it works.
But that doesn't mean the "analog" can be ignored. Compsci telling us about an algorithm being O(1) vs O(log n) doesn't reveal that vacuum tubes give off more heat and fail more often than silicon transistors. Or the thinness of a wire trace on a circuit board means that deploying a computer in space orbit makes it susceptible to cosmic rays flipping a random bit causing weird errors. All those analog difficulties in the real world doesn't mean that looking at computers via lens of Computer Science is problematic. It's just a different perspective that lets us make progress in that specific area. Every knowledge specialization has inherent limitations.
(I notice that my comment supplements dTal's comment: https://news.ycombinator.com/item?id=29649339)
Does it? In computers, digital computation is the ideal that we try to mimic via analog circuits (or vacuum tubes, relays, or whatever). We are actually glad that we can chain two CMOS inverters and the result is that anything close to Vcc becomes Vcc, and anything close to GND becomes GND.
As far as I understand, in biology "improving signal quality" like that actually disturbs the system because you are removing an "error" that actually has a useful function somewhere else.
Or for a software analogy: Consider a program that breaks memory safety, but you can't fix it because some other part relies on that "bug". Now consider a program that doesn't just contain lots of weird quirks like that, but has grown around them and makes them an integral part.
At least that is how I imagine chemistry inside a cell as a layman.
But... end of day, if you squint just a little, most everything is done with a standard set of amino acids that chain together in standard ways. In some AA's The Amino and Acid groups used to daisy-chain them together dwarf the actual active part! And data is stored using a standard set of 4 ( ok 5, ok... exceptions exist) bases.
(Just to be sure, eg. Hemoglobin does suddenly have an Iron atom in there, but it's still _mostly_ made of standard "lego blocks" == amino acids)
If we cant use programming metaphors to understand how to patch it, at least maybe to explain exactly what we are fundamentally this is interesting no?
We are software running on a hardware (the environment, the interpretation precursors in the womb on startup, the rest of the machinery ALSO created by the software), are we not ? I feel it's cool we can encode some useful messages as RNA for vaccines with headers, commands, checksums even if ofc it's not exactly like electronics.
And important aspect of biology is that it is really much more analog than digital in many areas. Many interactions are not entirely on/off, and they are influenced by a lot of other factors in the environment of the cell. This is a part that really doesn't match well to how people understand coding. The digital aspects are also inherently coupled to their physical representations. That are not just four letters A, C, T, G in the DNA, each of them also has different chemical and physical properties.
Some analogies to coding concepts work quite well, but you really have to be aware of the limitations. And I often see computer scientists trying to use these analogies to understand certain parts of biology, but inevitably they will move out of the bounds of where the anlogy works unless they actually know the underlying biology.