Those guys did an awesome job because: a) use of brownian motion b) sick af music.
The life cycle of HIV in 3D [video]
31–40 of 72 posts
Re: The life cycle of HIV in 3D [video]
#32I think articles like Ken Shirriff's "Cells are very fast and crowded places"[1] are good companions for visualizations like this. It helps to keep in mind that the things moving around in cells are flying around at break-neck speeds if you scale them up linearly. 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.
--
[0] - https://www.goodreads.com/book/show/6601267-the-machinery-of...
[1] - http://mgl.scripps.edu/people/goodsell/illustration/public
Re: The life cycle of HIV in 3D [video]
#33Mindblowing. To think that chance+time produced something like that is staggering.
+reproduction +selection
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.
Re: The life cycle of HIV in 3D [video]
#34The amazing part for me was that virus "code" automatically finds the unrelated host "code" in what is effectively an equivalent of space travel inside a host body, does copy and paste to insert itself somewhere in host code and everything just works! I can't think of any of our computing models that is capable of doing this and so robust at errors.
Re: The life cycle of HIV in 3D [video]
#35I think articles like Ken Shirriff's "Cells are very fast and crowded places"[1] are good companions for visualizations like this. It helps to keep in mind that the things moving around in cells are flying around at break-neck speeds if you scale them up linearly. 1 - http://www.righto.com/2011/07/cells-are-very-fast-and-crowde...
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.
Re: The life cycle of HIV in 3D [video]
#36It 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
Re: The life cycle of HIV in 3D [video]
#37Question. How is that even knowing the lifecycle of HIV at this level of detail, there is not a cure for it?
I Googled it for you: 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: https://en.wikipedia.org/wiki/The_Berlin_Patient
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.
Re: The life cycle of HIV in 3D [video]
#38On 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.
Re: The life cycle of HIV in 3D [video]
#39I would love to know how this animation was made. Is there any sort of simulation happening?
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).