Finally :) I have a degree in electrical engineering and a degree in immunology (in the early 90s) and was 100% sure that control systems math should be applied to biological systems, like the immune system.
Most biologists tend to take very little math (calculus and stats) and dont even realize that control systems are a thing.
We definitely need more of that cross pollination.
Or as the only existing example of working nanotechnology. This realization was what made me interested in life sciences. Before, biology in my mind was mostly cataloging various peculiarities of animals and plants, which I found utterly boring. I think the switch flipped when I stopped looking at life sciences as studying an art piece, and started looking at it as reverse engineering of an incredibly complex family of machines, with full intent of using gained knowledge for practical purposes.
Cybernetics is the original "control systems math", which was then forgotten/ignored in the West (the Soviets messed with it some I think) and re-invented as "Control Theory" by people trying to control factories and such.
As the article states, Cybernetics as originally conceived makes no distinctions about the physical (or otherwise) nature of the underlying processes it models, and it was explicitly expected to unify the math of feedback and control in biological as well and mechanical systems.
The fictional "Bionic Man", et. al., was an exploration in the popular imagination of how this might result in e.g. artificial limbs and organs that worked like the real thing and, in the stories at least, even improved on them.
In any event, what this article describes could be called cellular bionics.
Nit pick - The "original control systems math" is Bode, Black, Nyquist.. 1930's. Cybernetics is the application of control theory (branch of mathematics) to biology; Norbert Wiener, Ashby, etc, 1950's.
Nit pick to a nit pick: from what I can gather from the title of Wiener's book[0], it was never about biology. It was just about applications of control theory to physical mechanisms, be it biological or man-made.
Looks like wikipedia is the source of confusion in this case. "The book laid the theoretical foundation for servomechanisms (whether electrical, mechanical or hydraulic), automatic navigation, analog computing .."
Servomechanisms existed way earlier than 1948, so did analog computers - navy ships in WWII extensively used both in gun control, to move the guns and calculate balistic trajectories, etc. Wiener merely wrote down existing knowledge, and suggested it could also be applied to humans.
> navy ships in WWII extensively used both in gun control, to move the guns and calculate balistic trajectories, etc.
> Wiener merely wrote down existing knowledge, and suggested it could also be applied to humans.
A little more than that.
> During World War II, his work on the automatic aiming and firing of anti-aircraft guns caused Wiener to investigate information theory independently of Claude Shannon and to invent the Wiener filter.
To be fair cybernetics is directly name checked in the article though: “Norbert Wiener proposed that regulatory systems in both animals and machines should be studied together, in a field he named cybernetics”
Wow, really? That's surprising to me. The hardest course I ever took was one I took for my MSCS in bioinformatics. It seemed very math-heavy, and math-heavy in ways that I couldn't lean on my past experience to "muscle" my way through.
Both you and the OP are right in your own ways. It's worth remembering that bioinformatics, computational biology and other inter-disciplinary fields like these (mathematical oncology comes to mind) are relatively new and the proportion of biological sciences graduates doing these are dwarfed by the number that go through a "conventional" (as probably alluded to by the OP) biological sciences curriculum. Mathematical modeling in biological sciences is incredibly hard because of the sheer complexity in the many layers that comprise our knowledge of biology (cell layer. molecular biology layer, tissue layer etc) and I generally agree with the sentiment that more investments in research at the math-biology intersection will further our ability to understand biology better.
In my experience, a lot of biologists learning bioinformatics/genomics tend to learn a specific set of general tools (HMMs, multiple hypothesis testing, PCA, etc.) and then learn some specific statistical tools that apply to their data (e.g negative binomial GLMs for RNAseq).
>> To minimize the effects of noise, the duo realized, the two controller molecules must have a very specific relationship: They have to bind to each other and neutralize each other’s biological activity. One must be the antithesis of the other.
One more thing they may want to add. A way to break down all these neutralized pairs and clear them out. Depending that these molecules look like there may already be something in place to do that, then it's more a matter of ensuring compatibility rather than making a new clearing mechanism.
There are two major ways that happens. In growing populations of cells, they are diluted as the cells grow and divide, and there is ongoing recycling of proteins in the cell. Some are more labile (likely to be grabbed by the recycling machinery) than others, but it's an ongoing background process.
The article talks about negative feedback in the circuitry to maintain stability. Why can’t the same be achieved using positive feedback in the biological and psychological realms? Only the sign is different. In the psychological system the negative feedback back can be thought of as pain. Why can’t achieve stability through pleasure instead?
Just to be clear, negative feedback is not giving 'bad feedback', it's taking an action that 'dampens' a signal as it gets outside of a zone of control to bring it back in line. Positive feedback would not be pleasure or reward, positive feedback would be something more akin to a runaway chain reaction or an avalanche, where the system takes an action that emphasizes a signal as it gets outside the zone of control.
I believe the answer to your question is that stability is maintained by a balance of pain and pleasure.
By analogy, imagine a tower with guy wires only on one side. The tower will remain upright only as long as the “right” magnitude of force comes from the “right” direction to balance the force of the wires. But stability is achieved by balanced opposing forces, such that any reasonably expected deviation (in any direction) is countered and stability is maintained.
The sign is the crucial part of feedback. Negative feedback is what maintains stability. The more you do something, the harder the pushback. Think of e.g. friction. Positive feedback is what makes a system go boom. Think of a chain reaction in a nuclear bomb.
> The work is backed by a mathematical proof that no simpler answer exists — a good indication that natural feedback systems probably work the same way.
On the contrary. It means that you probably won't find anything simpler, but you will certainly find more complex ones as exaptation drags part of the system off in a new direction.
In a very general sense, feedback loops seem important in many areas.
A couple of potential examples: software engineering and animal intelligence.
In software there are loops between the programmer and various levels of the development process, from compilers to test runners all the way to user testing and feedback.
A large function of animal intelligence may be to integrate (in a very broad sense, not mathematically as used in the article) information about the environment in order to regulate the organism's place in it.
My understanding, as someone who is rather scientifically oriented but not a biologist, is that a living organism is basically an elaborate, multi-level set of interconnected negative feedback loops. Have I got that right?
Most biologists tend to take very little math (calculus and stats) and dont even realize that control systems are a thing.
We definitely need more of that cross pollination.
edit: D'oh! I commented before reading the article. Sorry. It does mention Cybernetics.
As the article states, Cybernetics as originally conceived makes no distinctions about the physical (or otherwise) nature of the underlying processes it models, and it was explicitly expected to unify the math of feedback and control in biological as well and mechanical systems.
The fictional "Bionic Man", et. al., was an exploration in the popular imagination of how this might result in e.g. artificial limbs and organs that worked like the real thing and, in the stories at least, even improved on them.
In any event, what this article describes could be called cellular bionics.
--
[0] - https://en.wikipedia.org/wiki/Cybernetics:_Or_Control_and_Co...
Servomechanisms existed way earlier than 1948, so did analog computers - navy ships in WWII extensively used both in gun control, to move the guns and calculate balistic trajectories, etc. Wiener merely wrote down existing knowledge, and suggested it could also be applied to humans.
> Wiener merely wrote down existing knowledge, and suggested it could also be applied to humans.
A little more than that.
> During World War II, his work on the automatic aiming and firing of anti-aircraft guns caused Wiener to investigate information theory independently of Claude Shannon and to invent the Wiener filter.
https://en.wikipedia.org/wiki/Norbert_Wiener#During_and_afte...
But you're absolutely right that "original control systems math" is older than cybernetics, well met.
Wow, really? That's surprising to me. The hardest course I ever took was one I took for my MSCS in bioinformatics. It seemed very math-heavy, and math-heavy in ways that I couldn't lean on my past experience to "muscle" my way through.
https://www.amazon.com/gp/product/1439837171/ref=dbs_a_def_r...
One more thing they may want to add. A way to break down all these neutralized pairs and clear them out. Depending that these molecules look like there may already be something in place to do that, then it's more a matter of ensuring compatibility rather than making a new clearing mechanism.
By analogy, imagine a tower with guy wires only on one side. The tower will remain upright only as long as the “right” magnitude of force comes from the “right” direction to balance the force of the wires. But stability is achieved by balanced opposing forces, such that any reasonably expected deviation (in any direction) is countered and stability is maintained.
On the contrary. It means that you probably won't find anything simpler, but you will certainly find more complex ones as exaptation drags part of the system off in a new direction.
A couple of potential examples: software engineering and animal intelligence.
In software there are loops between the programmer and various levels of the development process, from compilers to test runners all the way to user testing and feedback.
A large function of animal intelligence may be to integrate (in a very broad sense, not mathematically as used in the article) information about the environment in order to regulate the organism's place in it.