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I have a degree in electrical engineering and a degree in medical microbiology and immunology. It was one of my (many) dreams to model the immune system in sof

I have a degree in electrical engineering and a degree in medical microbiology and immunology. It was one of my (many) dreams to model the immune system in software, using control systems to create a reference model that you could challenge and get legitimate responses (like climate models today). This was 30 years ago. One of the challenges is that most immunologists have never even heard of the concept of "control systems" and likely have no concept of the math involved. 1) The immune system (and really the entire body) is a giant control system with many feedback loops that arent understood at all. I for example was studying something very simplistic which was the IL-2 vs Il-4 pathway in response to tuberculosis infection. I havent kept up on the research so I have no idea what state of the art is in immunology 30 years later. 2) The immune system is a giant random number generator. Your body randomly generates antibody patterns in the womb, so everyone is different. The system is analog instead of digital creating a tremendous amount of variation. It is unlikely you could collect data sets large enough to start training an AI to model the immune system except maybe at the grossest level. It sounds to me that AI is being used as a buzzword to get more attention. This is a summary article aggregating papers that are attempting to model biological systems as control systems https://www.frontiersin.org/articles/10.3389/fbioe.2021.677976/full https://www.frontiersin.org/articles/10.3389/fbioe.2021.6779...

loldk 3y ago
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philsnow 3y ago
> The immune system is a giant random number generator. Your body randomly generates antibody patterns in the womb, so everyone is different. I read about this in the book "Immune" [0], and it blew my mind. I just want to call attention to this part of your comment because people might breeze past it. Your immune system is tasked with what might seem an impossible problem: be able to kill anything that doesn't match "you" / your genome. But it can't encode everything that isn't "you" in the DNA that your immune system came from, partly because DNA transcription is generative: any arbitrarily-long sequence of codons creates a protein. So rather than try to write down all the things that could kill you (even ones that have never been encountered in all of human history) and remember how to kill those things, it generates random proteins, checks whether they look like you, and if they don't, figures out how to bind to those and produces proteins (antibodies) that bind to those things. (I'm heavily paraphrasing and I don't have the book handy to refer to, sorry) [0] https://www.amazon.com/Immune-Journey-Mysterious-System-Keeps/dp/0593241312 https://www.amazon.com/Immune-Journey-Mysterious-System-Keep...
- ASalazarMX 3y ago
  > it generates random proteins, checks whether they look like you, and if they don't, figures out how to bind to those What is the antonym of "machine learning"? because this looks like machine learning to me.
  - marcosdumay 3y ago
    AFAIK, it looks a lot like a simulated annealing evolutive search.
nunuvit 3y ago
I'm an electrical engineer, I don't know much about biology. Electrical is well-suited to modeling with ordinary differential equations. By making design decisions such that linear time-invariance is a valid assumption, we unlock really simple, powerful, and broadly applicable techniques for modeling, measurement, and control design. Most biology isn't designed, which is probably why those papers had to jump right to much more advanced and specific techniques for modeling and control. With no useful on-ramp for most biologists, modeling and control will have to be a deliberate collaboration between two specialized disciplines.

loldk 3y ago

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philsnow 3y ago

> The immune system is a giant random number generator. Your body randomly generates antibody patterns in the womb, so everyone is different. I read about this in the book "Immune" [0], and it blew my mind. I just want to call attention to this part of your comment because people might breeze past it. Your immune system is tasked with what might seem an impossible problem: be able to kill anything that doesn't match "you" / your genome. But it can't encode everything that isn't "you" in the DNA that your immune system came from, partly because DNA transcription is generative: any arbitrarily-long sequence of codons creates a protein. So rather than try to write down all the things that could kill you (even ones that have never been encountered in all of human history) and remember how to kill those things, it generates random proteins, checks whether they look like you, and if they don't, figures out how to bind to those and produces proteins (antibodies) that bind to those things. (I'm heavily paraphrasing and I don't have the book handy to refer to, sorry) [0] https://www.amazon.com/Immune-Journey-Mysterious-System-Keeps/dp/0593241312 https://www.amazon.com/Immune-Journey-Mysterious-System-Keep...

ASalazarMX 3y ago
> it generates random proteins, checks whether they look like you, and if they don't, figures out how to bind to those What is the antonym of "machine learning"? because this looks like machine learning to me.
- marcosdumay 3y ago
  AFAIK, it looks a lot like a simulated annealing evolutive search.

nunuvit 3y ago

I'm an electrical engineer, I don't know much about biology. Electrical is well-suited to modeling with ordinary differential equations. By making design decisions such that linear time-invariance is a valid assumption, we unlock really simple, powerful, and broadly applicable techniques for modeling, measurement, and control design. Most biology isn't designed, which is probably why those papers had to jump right to much more advanced and specific techniques for modeling and control. With no useful on-ramp for most biologists, modeling and control will have to be a deliberate collaboration between two specialized disciplines.