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As a technologist, I have mixed feelings when I see the fascinating details of life. On one hand, mastering this molecular machine would give us literally God-l

As a technologist, I have mixed feelings when I see the fascinating details of life. On one hand, mastering this molecular machine would give us literally God-like powers: we could fabricate, grow or heal anything. We could solve all current problems, we could terraform planets using a few milligrams of DNA and literally redefine what it means to be human. 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.

nabla9 8y ago
Interestingly the Gene Regulatory Network (GRN) - the interaction of genes and proteins that control the prosess inside the cell is computationally very similar to recurrent neural network. Gene expression levels is controlled by proteins that are produced by active genes. https://en.wikipedia.org/wiki/Gene_regulatory_network https://en.wikipedia.org/wiki/Gene_regulatory_network Harnessing this mechanism directly for neural computation would be grand project.
- daveguy 8y ago
  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 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)
  - nabla9 8y ago
    > 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_8 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/ 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 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.
    - daveguy 8y ago
      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)
ekianjo 8y ago
>　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.
- telchar 8y ago
  To be fair, that argument depends on the properties of evolved pathogens. A designed pathogen, in the context of the GP post, would not necessarily be bound by that dilemma.
- cornholio 8y ago
  Are you sure you aren't still thinking in the conventional, evolutionary paradigm? 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.
  - sizzle 8y ago
    Wow these are novel concepts, do you read a lot of sci fi? How did you come up with these interesting scenarios?
    - 8y ago

nabla9 8y ago

Interestingly the Gene Regulatory Network (GRN) - the interaction of genes and proteins that control the prosess inside the cell is computationally very similar to recurrent neural network. Gene expression levels is controlled by proteins that are produced by active genes. https://en.wikipedia.org/wiki/Gene_regulatory_network https://en.wikipedia.org/wiki/Gene_regulatory_network Harnessing this mechanism directly for neural computation would be grand project.

daveguy 8y ago
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 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)
- nabla9 8y ago
  > 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_8 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/ 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 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.
  - daveguy 8y ago
    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)

ekianjo 8y ago

>　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.

telchar 8y ago
To be fair, that argument depends on the properties of evolved pathogens. A designed pathogen, in the context of the GP post, would not necessarily be bound by that dilemma.
cornholio 8y ago
Are you sure you aren't still thinking in the conventional, evolutionary paradigm? 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.
- sizzle 8y ago
  Wow these are novel concepts, do you read a lot of sci fi? How did you come up with these interesting scenarios?
  - 8y ago