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Well, DNA might not have programming structures, but it acts like a neural network. See "Gene regulatory network" > Some proteins though serve only to activate

Well, DNA might not have programming structures, but it acts like a neural network. See "Gene regulatory network" > Some proteins though serve only to activate other genes, and these are the transcription factors that are the main players in regulatory networks or cascades. By binding to the promoter region at the start of other genes they turn them on, initiating the production of another protein, and so on. Some transcription factors are inhibitory. [1] These networks are similar to neural networks in that they process information through a series of interconnected nodes (genes and proteins, in the case of GRNs) that influence each other's activity. The activation or inhibition of one gene by a transcription factor can trigger a cascade of events, much like the way neurons activate each other in a neural networks. [1] https://en.m.wikipedia.org/wiki/Gene_regulatory_network https://en.m.wikipedia.org/wiki/Gene_regulatory_network

forceIsStr0ng 3y ago
Yes this because DNA is a data structure not a physical force. If DNA of some sequence, then interaction with electromagnetic fields, gravity, strong/weak nuclear force of xyz properties will probably result in mutation along path abc, a theory the speaker must provide replicable statistical analysis to back up or it goes in the bin. IT obsession with data models has pushed IT workers into pseudoscience that ignores entropy, Lindy effects; structure is mutable. Physical forces are not. Data structures are not physical forces. They’re leaky mental models our biology garbage collects due to entropy and Lindy effects. Anyway just the take of an EE who finds the obsession with software development bizarre, full of statistical mirages, hallucinated accomplishments.
stultissimus 3y ago
Synthetic biologist here. This is a good answer, but looking at man-made genetic programs can give us a simplified perspective. We've made significant progress engineering programs that enable human T cells to kill target (cancer) cells _conditionally_ based on their surface marker expression [1]. This is in contrast to conventional CAR-T cells which are already on the market (see Kymriah, etc.). A simple AND gate T cell circuit is currently being tested in humans [2]. Numerous strategies now exist to write cellular decision making programs using synthetic circuits. We are entering an era where we can write DNA programs and put them into people; I wonder how we can interest more CS-minded people in this kind of "synthetic biology as programming", especially as we move from proof of concept studies in bacteria to real trials in humans [2]. [1]: https://pubmed.ncbi.nlm.nih.gov/33243890/ https://pubmed.ncbi.nlm.nih.gov/33243890/ [2]: https://arsenalbio.com/2023/05/16/arsenalbio-announces-presentations-highlighting-preclinical-data-on-ab-1015-and-ab-2100-at-asgct-annual-meeting/ https://arsenalbio.com/2023/05/16/arsenalbio-announces-prese...
- mcnnowak 3y ago
  Any tips or "get your foot in the door" advice on how someone currently in a CS career, but interested in biology could make this career transition? As a simple CS-minded person, I don't see any LeetCode for synthetic biology sites :)
  - stultissimus 3y ago
    Reading life science textbooks and substacks is a great way to get started (see [1], [2]). Also, there is a major deficit of software engineering talent in biology research (probably because pro SWEs are too expensive for academic labs, and those labs are where much of the foundational research is done). If you have the bandwidth, part time / volunteer work with an academic lab in your area could be a great way in the door. [1] https://centuryofbio.com/ https://centuryofbio.com/ [2] https://substack.com/profile/11154869-niko-mccarty https://substack.com/profile/11154869-niko-mccarty
- FLT8 3y ago
  Sounds exciting, but also a little scary. I'm imagining a biological variant of eg. Verilog or VHDL, as this seems like the kind of domain where tools that allow for formal verification would be highly desirable.
  - stultissimus 3y ago
    This has actually been done for genetic circuits in E coli (bacteria) by Chris Voight's lab at MIT. Their platform is called Cello [1], and it enables interoperation between Verilog/HDL and genetic code. This kind of thing has not yet reached practical utility in human cell engineering, but companies like Asimov [2] are pushing hard in that direction [1] https://www.cidarlab.org/cello https://www.cidarlab.org/cello [2] https://www.asimov.com/ https://www.asimov.com/
- visarga 3y ago
  I was hoping someone who knows more that me to correct/expand my comment. Thanks
huytersd 3y ago
So it sounds like event based architecture. You send out events in response to other events and the event sender is “regulated” by other events.
samuell 3y ago
In systems biology textbooks, these networks are typically described more as alike logic gates than as neural networks, although some similarities are there for sure. The GRNs are a bit more dynamic than strictly on/off, but the fact they have multiple types of interaction, such as enhancers and inhibitors, means they can model logical gates better than I understand a typical neural network can.
Garlef 3y ago
Before anyone jumps to esoteric conclusions: This is more likely a result of neural networks being very general: General in the sense that they are able to approximate/model a lot of dynamic systems that allow operadic composition.
- petsfed 3y ago
  Just a sidebar here, I think the urge to jump to esoteric conclusions is very strong because we are (rightly) trained to not expect that nature at large uses the same tools that we have developed. So its very validating (and also kind of unnerving) when we do find the suggestion that we're "right" at a more fundamental level. Consider that the uncertainty relations in physics fall neatly out of Fourier Analysis, which could suggest that the universe also does Fourier Analysis to figure out where things are and how fast they're going. But really, its that classical interaction is a sort of mechanical Fourier transform. I think that moment where it feels like we brush against the real clockwork of the universe has an eldritch, cosmic-horror quality to it, which is both addictive and maddening.
- namaria 3y ago
  Sure, nothing profound about trillion-strong fleets of self replicating nanobots controlled by chemical computers processing high dimension data about their environments with complex entanglements giving rise to specialized clusters of information processing neural masses, capable of then communicating with other similar entities, building knowledge about data processing and storage over millennia, starting with clay tablets and ending up with advanced machinery that can be used to create elaborate patterns of data flows that can help us peer through the universe, inside our bodies, understand in intricate detail everything that happens across billions of light years and over billions of years. Nothing profound at all about any of that. Just very general structures.
- fasterik 3y ago
  It's still an interesting fact that evolution seems to have figured out that neural networks are good at approximating arbitrary continuous functions. We could ask whether intelligent alien species are likely to use neural nets. In other words, are neural nets universal in some sense, or are they contingent to how life evolved on Earth?

forceIsStr0ng 3y ago

Yes this because DNA is a data structure not a physical force. If DNA of some sequence, then interaction with electromagnetic fields, gravity, strong/weak nuclear force of xyz properties will probably result in mutation along path abc, a theory the speaker must provide replicable statistical analysis to back up or it goes in the bin. IT obsession with data models has pushed IT workers into pseudoscience that ignores entropy, Lindy effects; structure is mutable. Physical forces are not. Data structures are not physical forces. They’re leaky mental models our biology garbage collects due to entropy and Lindy effects. Anyway just the take of an EE who finds the obsession with software development bizarre, full of statistical mirages, hallucinated accomplishments.

stultissimus 3y ago

Synthetic biologist here. This is a good answer, but looking at man-made genetic programs can give us a simplified perspective. We've made significant progress engineering programs that enable human T cells to kill target (cancer) cells _conditionally_ based on their surface marker expression [1]. This is in contrast to conventional CAR-T cells which are already on the market (see Kymriah, etc.). A simple AND gate T cell circuit is currently being tested in humans [2]. Numerous strategies now exist to write cellular decision making programs using synthetic circuits. We are entering an era where we can write DNA programs and put them into people; I wonder how we can interest more CS-minded people in this kind of "synthetic biology as programming", especially as we move from proof of concept studies in bacteria to real trials in humans [2]. [1]: https://pubmed.ncbi.nlm.nih.gov/33243890/ https://pubmed.ncbi.nlm.nih.gov/33243890/ [2]: https://arsenalbio.com/2023/05/16/arsenalbio-announces-presentations-highlighting-preclinical-data-on-ab-1015-and-ab-2100-at-asgct-annual-meeting/ https://arsenalbio.com/2023/05/16/arsenalbio-announces-prese...

mcnnowak 3y ago
Any tips or "get your foot in the door" advice on how someone currently in a CS career, but interested in biology could make this career transition? As a simple CS-minded person, I don't see any LeetCode for synthetic biology sites :)
- stultissimus 3y ago
  Reading life science textbooks and substacks is a great way to get started (see [1], [2]). Also, there is a major deficit of software engineering talent in biology research (probably because pro SWEs are too expensive for academic labs, and those labs are where much of the foundational research is done). If you have the bandwidth, part time / volunteer work with an academic lab in your area could be a great way in the door. [1] https://centuryofbio.com/ https://centuryofbio.com/ [2] https://substack.com/profile/11154869-niko-mccarty https://substack.com/profile/11154869-niko-mccarty
FLT8 3y ago
Sounds exciting, but also a little scary. I'm imagining a biological variant of eg. Verilog or VHDL, as this seems like the kind of domain where tools that allow for formal verification would be highly desirable.
- stultissimus 3y ago
  This has actually been done for genetic circuits in E coli (bacteria) by Chris Voight's lab at MIT. Their platform is called Cello [1], and it enables interoperation between Verilog/HDL and genetic code. This kind of thing has not yet reached practical utility in human cell engineering, but companies like Asimov [2] are pushing hard in that direction [1] https://www.cidarlab.org/cello https://www.cidarlab.org/cello [2] https://www.asimov.com/ https://www.asimov.com/
visarga 3y ago
I was hoping someone who knows more that me to correct/expand my comment. Thanks

huytersd 3y ago

So it sounds like event based architecture. You send out events in response to other events and the event sender is “regulated” by other events.

samuell 3y ago

In systems biology textbooks, these networks are typically described more as alike logic gates than as neural networks, although some similarities are there for sure. The GRNs are a bit more dynamic than strictly on/off, but the fact they have multiple types of interaction, such as enhancers and inhibitors, means they can model logical gates better than I understand a typical neural network can.

Garlef 3y ago

Before anyone jumps to esoteric conclusions: This is more likely a result of neural networks being very general: General in the sense that they are able to approximate/model a lot of dynamic systems that allow operadic composition.

petsfed 3y ago
Just a sidebar here, I think the urge to jump to esoteric conclusions is very strong because we are (rightly) trained to not expect that nature at large uses the same tools that we have developed. So its very validating (and also kind of unnerving) when we do find the suggestion that we're "right" at a more fundamental level. Consider that the uncertainty relations in physics fall neatly out of Fourier Analysis, which could suggest that the universe also does Fourier Analysis to figure out where things are and how fast they're going. But really, its that classical interaction is a sort of mechanical Fourier transform. I think that moment where it feels like we brush against the real clockwork of the universe has an eldritch, cosmic-horror quality to it, which is both addictive and maddening.
namaria 3y ago
Sure, nothing profound about trillion-strong fleets of self replicating nanobots controlled by chemical computers processing high dimension data about their environments with complex entanglements giving rise to specialized clusters of information processing neural masses, capable of then communicating with other similar entities, building knowledge about data processing and storage over millennia, starting with clay tablets and ending up with advanced machinery that can be used to create elaborate patterns of data flows that can help us peer through the universe, inside our bodies, understand in intricate detail everything that happens across billions of light years and over billions of years. Nothing profound at all about any of that. Just very general structures.
fasterik 3y ago
It's still an interesting fact that evolution seems to have figured out that neural networks are good at approximating arbitrary continuous functions. We could ask whether intelligent alien species are likely to use neural nets. In other words, are neural nets universal in some sense, or are they contingent to how life evolved on Earth?