6 ms·

Truly fascinating. It makes me wonder how such a thing could be encoded inside a DNA. As in, is there a field of study similar to computer science/information

Truly fascinating. It makes me wonder how such a thing could be encoded inside a DNA. As in, is there a field of study similar to computer science/information theory on certain fundamental biological "laws of transformation"? Say, we have six billion or so pairs in the DNA. How could a series of transformations from those pairs with other proteins produce this complexity especially ones that requires a particular 3d shape, time based mechanisms and reactions all tied together efficiently and passed from generation to generation? Obviously perhaps not a "designed by some magical entity", but six billion pairs/nibbles seem quite magical to encode this complexity... It is certainly hard to study this even over many decades, but I wonder if there are certain fundamental laws or units or theories that help provide pathways to understand such transformations.

core-questions 5y ago
I think we're still a generation or so away from getting to this level. Fundamentally, though, it doesn't seem to be too wild of a suggestion that DNA is going to end up looking like an incredibly complex, incredibly hacky computer program, utilizing every last potential possibility of its mechanical substrate. Think about demoscene or 80s video game programming, tight little assembly routines by developers that did not care anywhere near as much about simple and clear code, security, portability, or anything else as they did about raw performance. Analyzing some of these after the fact (not that I've done it, but I've read the stories of people doing it) seems to be every bit as difficult as being able to do it in the first place. And this is something that _is_ the product of intelligent design; when raw evolution is responsible for the work, it's even less liable to make some kind of logical sense when viewed at the lowest level. The emergent effect of random change (and its impact on reproductive fitness) is the only thing that matters. Now, consider this: scientists analyzing a simple processor can't even tell you how it works, using modern equipment: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005268 https://journals.plos.org/ploscompbiol/article?id=10.1371/jo... > we take a classical microprocessor as a model organism, and use our ability to perform arbitrary experiments on it to see if popular data analysis methods from neuroscience can elucidate the way it processes information. Microprocessors are among those artificial information processing systems that are both complex and that we understand at all levels, from the overall logical flow, via logical gates, to the dynamics of transistors. We show that the approaches reveal interesting structure in the data but do not meaningfully describe the hierarchy of information processing in the microprocessor. This suggests current analytic approaches in neuroscience may fall short of producing meaningful understanding of neural systems, regardless of the amount of data. What do we take from that? It seems to me that our current understanding of DNA is extremely crude, and that we're still a few breakthroughs away from understanding any moderate to large scale 'logical structure' in it.
- wruza 5y ago
  This reminds me a first perceptron tutorial. An author described that to match a digit 9, ideally one layer matches a circle, another sees a lower curve and a result is then combined into output “neurons”. In reality, after you train a network, it has no circle-matching or curve-matching parts. Every part of it is just a random noise that you couldn’t reverse-engineer without pushing various data to that blackbox. We could’t even tell if matching F to 1 was a learning error or an intended behavior.
- mindfulplay 5y ago
  Thanks for the link, interesting. Agree that our current understanding is very crude. I was thinking a mapping between the information theory world to the biology world: say if N DNA pairs can be theoretically shown to only encode a pattern of "blah" complexity (time/space) regardless of future protein interactions, then could a model progressively apply that to find limits to prove/disprove/discover old or new ideas that are correct/incorrect? We have had a cop out of "it's a magical world that we don't fully understand" for such a long time,so wondering if fundamental laws can help pave the way. Probably very amaterish/arrogant coming from computer science people such as myself, but one couldn't help but be in awe of these gears or octopuses or even basic flower patterns emerging out of DNA.
  - core-questions 5y ago
    > say if N DNA pairs can be theoretically shown to only encode a pattern of "blah" complexity (time/space) regardless of future protein interactions I don't think it's going to end up this simple. You're going to end up in the Godelian trap with this one. Consider how simple a Turing machine is, or a tiny cellular atomaton, and yet there's no limit to the complexity that they can produce chaotically from any established starting position. The fundamental laws are going to come from simulating what the DNA does in practice, like running code in an emulator. It needs to be 'decompiled' and 'decompressed', in a sense; our current approach is like trying to understand why a REST endpoint gives a certain response by staring at gates within the processor. It might be doable but it's not going to be the most fruitful approach.
deleted 5y ago
[deleted]

core-questions 5y ago

I think we're still a generation or so away from getting to this level. Fundamentally, though, it doesn't seem to be too wild of a suggestion that DNA is going to end up looking like an incredibly complex, incredibly hacky computer program, utilizing every last potential possibility of its mechanical substrate. Think about demoscene or 80s video game programming, tight little assembly routines by developers that did not care anywhere near as much about simple and clear code, security, portability, or anything else as they did about raw performance. Analyzing some of these after the fact (not that I've done it, but I've read the stories of people doing it) seems to be every bit as difficult as being able to do it in the first place. And this is something that _is_ the product of intelligent design; when raw evolution is responsible for the work, it's even less liable to make some kind of logical sense when viewed at the lowest level. The emergent effect of random change (and its impact on reproductive fitness) is the only thing that matters. Now, consider this: scientists analyzing a simple processor can't even tell you how it works, using modern equipment: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005268 https://journals.plos.org/ploscompbiol/article?id=10.1371/jo... > we take a classical microprocessor as a model organism, and use our ability to perform arbitrary experiments on it to see if popular data analysis methods from neuroscience can elucidate the way it processes information. Microprocessors are among those artificial information processing systems that are both complex and that we understand at all levels, from the overall logical flow, via logical gates, to the dynamics of transistors. We show that the approaches reveal interesting structure in the data but do not meaningfully describe the hierarchy of information processing in the microprocessor. This suggests current analytic approaches in neuroscience may fall short of producing meaningful understanding of neural systems, regardless of the amount of data. What do we take from that? It seems to me that our current understanding of DNA is extremely crude, and that we're still a few breakthroughs away from understanding any moderate to large scale 'logical structure' in it.

wruza 5y ago
This reminds me a first perceptron tutorial. An author described that to match a digit 9, ideally one layer matches a circle, another sees a lower curve and a result is then combined into output “neurons”. In reality, after you train a network, it has no circle-matching or curve-matching parts. Every part of it is just a random noise that you couldn’t reverse-engineer without pushing various data to that blackbox. We could’t even tell if matching F to 1 was a learning error or an intended behavior.
mindfulplay 5y ago
Thanks for the link, interesting. Agree that our current understanding is very crude. I was thinking a mapping between the information theory world to the biology world: say if N DNA pairs can be theoretically shown to only encode a pattern of "blah" complexity (time/space) regardless of future protein interactions, then could a model progressively apply that to find limits to prove/disprove/discover old or new ideas that are correct/incorrect? We have had a cop out of "it's a magical world that we don't fully understand" for such a long time,so wondering if fundamental laws can help pave the way. Probably very amaterish/arrogant coming from computer science people such as myself, but one couldn't help but be in awe of these gears or octopuses or even basic flower patterns emerging out of DNA.
- core-questions 5y ago
  > say if N DNA pairs can be theoretically shown to only encode a pattern of "blah" complexity (time/space) regardless of future protein interactions I don't think it's going to end up this simple. You're going to end up in the Godelian trap with this one. Consider how simple a Turing machine is, or a tiny cellular atomaton, and yet there's no limit to the complexity that they can produce chaotically from any established starting position. The fundamental laws are going to come from simulating what the DNA does in practice, like running code in an emulator. It needs to be 'decompiled' and 'decompressed', in a sense; our current approach is like trying to understand why a REST endpoint gives a certain response by staring at gates within the processor. It might be doable but it's not going to be the most fruitful approach.

deleted 5y ago

[deleted]