The life cycle of HIV in 3D [video]

Post by mikeash » Thu, May 03, 2018, 9:38 PM UTC

cryoshon wrote: post #70

mikeash wrote: post #66

Earlier quoted context omitted.

What about starting with the flu and giving it an HIV-like ability to wreck your immune system? Is the “attack” part too intertwined with everything else to be able to do that sort of mix-and-match operation?

the flu gaining deadlier characteristics via engineering is more realistic. unfortunately, i believe that is well within the scope of our present capability. the exact magnitude of how dangerous such engineering could make a virus based on the flu is unclear to me, but i'd estimate somewhere between "globally apocalyptic" and "continentally destabilizing". the mixing and matching of attack characteristics is probably…

How comforting! Thanks for all the info.

Post by daveguy » Fri, May 04, 2018, 12:35 PM UTC

nabla9 wrote: post #63

daveguy wrote: post #60

Earlier quoted context omitted.

I think it's a bit of a stretch to say GRN is "computationally similar" to neural networks. That seems to be a shoehorn of the most popular technology of one field into another field. 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 transd…

> That seems to be a shoehorn of the most popular technology of one field into another field. 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 infere…

The articles you cite start with experimental data about Gene regulatory networks (eg from dna microarray) and then use rnn to characterize or produce the networks known or elucidated experimentally.

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)

Re: The life cycle of HIV in 3D [video]

Re: The life cycle of HIV in 3D [video]