The life cycle of HIV in 3D [video](blogs.scientificamerican.com) |
The life cycle of HIV in 3D [video](blogs.scientificamerican.com) |
On the other, I see the human body as a completely unsecured cybernetic system, that can be so easily tricked to pick up any random bit of programming and insert it into it's own code. There is no forethought or design, no rational defense, just good enough systems that have evolved randomly against non-rational adversaries that happened to emerge out of the protein soup surrounding us.
The troubling fact is that mastering the wondrous biomolecular machine necessarily comes with the power to kill every human on the planet. Truly God-like powers.
You can't really design a perfect virus that will wipe out the human race, because anything you do to affect its properties will also affect its ability to spread. I'd be way more concerned about the destructive power of nuclear weapons, still numerous enough to destroy a very large part of humanity and our vital infrastructures.
A highly engineered bioweapon could circumvent such problems by separating the infection phase (which could be completely silent and airborne) from the eradication phase. The payload could be triggered deliberately at a later date when a certain secret artificial protein is released in the environment - and then produced in industrial quantities by infected hosts. Or maybe airdropped over areas that should be cleansed.
And that's just scratching the surface of what's conceptually possible. It could trigger specific ethnic characteristics or individuals, it could set up exotic cyber-hybrids like public key decryption in DNA for commands from its command and control. It could create side channels among infected hosts, for example by triggering minute anatomical modifications in the inner ear and the vocal centers, making them able to send and receive ultra- or infrasounds controlled by the mallware.
As a more subtle cyberattack, an infected individual could grow a whole parasitic subsystem that extracts select visual and auditory data and stores them in DNA memory for later broadcast.
Just design several good-enough viruses.
Harnessing this mechanism directly for neural computation would be grand project.
Just because the GRN contains feedback loops with multiple influences doesn't mean it's suited to NN computation. The GRN is orders of magnitude more complex than computational NNs and it is orders of magnitude slower than signal transduction of axons.
I agree it will be awesome to be able to harness molecular machinery to "do stuff" for us. The "nanotechnology" in our bodies is way beyond any current manufactured nanotechnology.
We currently have only crude control of the cellular functionality -- think point a wind up toy in the direction we want or modify it by taping a flashlight to the top. We are pretty far from being able to use the manufacturing equipment to make cars instead of windup toys. (Not to mention the manufacturer is already making rockets)
1 - http://www.righto.com/2011/07/cells-are-very-fast-and-crowde...
Also, this means that it's too tight for large stuff to move around at all. Hence the specialized machinery within the cell that transports large molecules.
Source for both: I'm 1/3d of th way through The Machinery of Life[0]. Incidentally, I learned about this book from HN. It's absolutely amazing. The biggest selling point are the drawings - David S. Goodsell created a lot of illustrations (like these[1]) that give you a good perspective on how stuff is packed within cells.
EDIT: that blog post you linked covers the diffusion aspect well, I second the recommendation to read it.
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[0] - https://www.goodreads.com/book/show/6601267-the-machinery-of...
[1] - http://mgl.scripps.edu/people/goodsell/illustration/public
It's like stumbling into elevator after elevator, while running the hundred yard dash at top speed, everywhere you go, only to encounter the perfect dance partner to fall in love with at first sight.
This one is about Influenza: https://www.youtube.com/watch?v=7Omi0IPkNpY (not H1N1, but quality is better)
I learned a lot today (At least that's what I felt) LOL
For simulation, typically a molecular dynamics code is used along with features specific to biological analysis (since MD by itself is a physics simulator). NAMD is one of the well-known MD tools used for bio work. Depending on the complexity and features/accuracy sought, a whole host of other tools might be involved. The timeframe of simulation is of the order of weeks to months given massive computational power (e.g., a large allocation on one of the national supercomputing grids).
Once tons of simulation data is generated (as the output of simulation), then 3D visualization tools can be used. NAMD has a sister vis tool called VMD. Blender is another option. Incidentally, Janet Iwasa (the narrator and researcher behind this work) is a visualization expert, so it's likely she only worked on the second step (animation) by using existing simulation data, or collaborating with a simulation group. (Again the computation required for rendering is very high, on the order of days to weeks using a large allocation on a supercomputer).
> Can you answer what was first? information (RNA/DNA) or the first cell?
No I can't. What are you getting at?
It will be nice to know if you can build a Turing complete machine using DNA and the cellular mechanisms. https://www.quora.com/Is-DNA-a-Turing-machine
Those guys did an awesome job because: a) use of brownian motion b) sick af music.
It's still a great video just IMO misleading in its own way.
The main mechanisms of reproduction and selection that you point out have almost no randomness at a population level.
Selection of individuals has some randomness associated with it, as death can come unexpectedly to anyone, but at population levels it is the genes that make it more likely for an individual to survive/pass on their genes that are the ones that get passed on. It's possible that an entire population gets wiped out, or a particular gene gets wiped out due to chance, but as long as the population is reproducing then on average the genes that are passed on are the ones that help the population survive.
Similarly reproduction isn't random at population levels either. Sexually reproducing populations mix their surviving genes evenly within geographical locations over evolutionary timescales (such as trees), or selectively if individuals choose their mates (like humans!). Neither of those things are random. Mutations that happen during reproduction are somewhat random but, as for asexual populations, the amount of mutation at the population level is not random. It's reasonable to expect the reproductive mechanisms to be selected for in such a way to maintain coding errors and the like (at an appropriate level) so that mutations continue to develop and strengthen the gene pool.
While there are lots of single events where randomness and chance come into play, as soon as you have a collection of things that reproduce those things must either get better at reproducing or cease to exist. Chance has nothing to do with it.
I'll stick to long term relationship.
https://www.ted.com/talks/janet_iwasa_why_it_s_so_hard_to_cu...
https://www.seeker.com/will-we-ever-cure-hiv-1792546668.html
https://en.wikipedia.org/wiki/Management_of_HIV/AIDS
Also you may interested:
I remember an early passage from Buckminster Fuller's Grunch of Giants where he tells the reader to visualize fully packed stadium and explains that's what 10,000 people looks like.
However,a second major issue is what’s mentioned in the movie, that the viral genome integrates into the host genome and can lie dormant for years. So even if you kill off every single viral particle, years later new particles can be produced from the dormant genome.
A third major issue is that HIV specifically infects T cells, which are the very immune cells that are supposed to combat infection. This weakens the natural defenses, and also makes it difficult to create a successful vaccine.
even when medicated -- for decades -- viral particles hang out safely in the body, often in the lymph nodes. as soon as the medication stops, they bounce back.
Recurrent neural network is used to model gene regulatory network. It's not a shoehorn.
See for example:
[1]: Reconstruction of Gene Regulatory Networks from Gene Expression Data Using Decoupled Recurrent Neural Network Model https://link.springer.com/chapter/10.1007/978-4-431-54394-7_...
[2]: Gene regulatory networks inference with recurrent neural network models https://ieeexplore.ieee.org/document/1555844/
[3]: Recurrent Neural Network Based Modeling of Gene Regulatory Network Using Bat Algorithm https://arxiv.org/pdf/1509.03221.pdf
> The GRN is orders of magnitude more complex than computational NNs and it is orders of magnitude slower than signal transduction of axons.
It's possible that we can reduce relevant complexity to the RNN subset that it useful. Feedback loop speeds are slower but they can be below second.
In many search and optimization problems the ability to run say 100 trillion large stochastic RNN's in parallel in a 100 liter tank could be huge. Especially if all you need is glucose and few cheap nutrients to power it.
None of the sources claim functional equivalence of the GRN by the RNN or vice versa.
From a "big O" computational complexity perspective the gap between what you are describing and the actual case is the gap between P and NP. Just because we can confirm the results of a GRN with an RNN doesn't mean we can produce those results.
Yes, biological computing could harness very powerful parallelism. We are nowhere close to harnessing that power. (See toy manufacturing analogy)
Been thinking for years about the human body as a cybernetic attack surface with no engineered cyberdefense. Most people seem not able to make that mental leap; no, the human body can't behave like a vulnerable Windows 95 machine giving kernel privileges to any ActiveX control it can download, because reasons.
But once you see the biological world like a hacker and DNA like a programming medium, as opposed to a representation of what evolution produced, an endless array of nefarious possibilities become obvious. The rational power of our minds far exceeds what evolution could ever concoct - or defend against.
think of it this way: every cool feature you add to a living thing has an overhead.
you want your little bacteria to have antibiotic resistance? fine.
but it'll need that much more energy to grow relative to the bacteria which don't have the added burden.
this means that unless there's the selective pressure of antibiotics in the environment, your little antibiotic resistant microbe won't stand a chance -- it's a fraction less efficient under normal conditions, so it's effectively out-competed when in the wild.
as far as massive adaptations like airborne spreading, that's not something that can just mutate overnight. HIV is fragile, so you'd need to engineer an entirely new viral envelope, or, more likely, an entirely new carrier particle that the virus can reconstruct on its own without impacting its infectivity. viruses like the flu have these adaptations by default. but once again, if you decide to turn the flu into HIV, it's going to be at a disadvantage in the wild.
not to say that it is impossible to make virions which are able to out-compete their wild-type cousins when infecting hosts in the real world. far from it. it's just not as easy to make it work as a quick look might find.
It seems like there are plenty of diseases out there where a (apparently) "small" modification could have a dramatic effect on how it spreads. And I'll admit my naivety to the subject and do not know if such small changes are actually small, or the related overhead associated with them.
making HIV airborne isn't a simple change, however. it's more like a massive change of niche. it's a change in the transmission modality of the virus -- for comparison, consider the scale of the changes you'd need to make to turn a car into a plane. or maybe a car into a boat.
it's doable, artificially. but the result won't be as good at being a car, plane, or boat as something which was purpose-built for that application and didn't have to carry the features of something intended for a different purpose.
many of the "small" changes that make a disease spread more easily are actually mutations which don't change the ability of the disease to weather external conditions, but rather change the ability of the disease to survive first contact with the host's immune system.
the flu is a great example here. we need a new flu vaccine every year because the flu mutates constantly and drastically. the flu never becomes capable of surviving outside a host for longer than before, though. it just becomes more effective at evading the immune systems of most hosts.
the mixing and matching of attack characteristics is probably possible under certain circumstances, but i don't know of any specific instances where it has been done. theoretically, it's easy to swap A for B, but making such changes nearly always has unintended downstream problems.
in the lab we used to do all sorts of mixing and matching, but for defensive characteristics (mostly to see if certain isomorphs were more vulnerable than others).
long story short, generating virus and isolating it is a real PITA for a slew of reasons. experimental cycles might be as long as a week for each trial of "mixing and matching".