Do we understand what the fundamental computational operation is in biology yet? Eg, in computers it's boolean logic, transistors with on/off gates, representing true/false or 1/0, from which all other more complex logical operations can be composed.
Does biology have an equivalent? Or is your comment essentially explaining that it probably does, but is more complex and we don't understand it yet?
There's no one model. How your immune cells pick their targets, or how your cells know which side is left or right during development. Neither is anything like how neurons work, or blood ion homeostasis is acieved. There are also (I believe) transistor like elements in metalloproteins that gate voltages in elecroactive systems like nitrogen fixation or photosynthesis (though that process is markedly dumber than the immune system).
Evolution is opportunistic and will reach for whatever computational method might be available and easy to fall into.
I'm the Wrong Person to Answer This - I'm a hobbyist and a dilettante, not a scientist, but here's how I understand it:
At its core, what you're seeing in all of these steps are molecular interactions - neurons fire to the rate they do because they build up sodium, potassium, or calcium ions; different chemical signaling chains are what prompt the transcription of given genes; charge affinities between the amino acids and with their environment are what create the shapes of proteins and give them their properties and capabilities. Effectively, each atom - each electron on each atom - is affecting these interactions, and that's what's driving the whole system.
At the molecular level, the properties of a molecule are (effectively) determined by their structure and charge distribution - the atomic composition of the molecule, where are the electrons likely to congregate, which bonds are stronger or weaker, and where are atoms likely to be able to be added or removed. These affect how the molecule reacts with other molecules, and each reaction that changes either the structure or the charge distribution changes how the molecule will react going forward.
So, the computation model is effectively a physical/structural one - how do these structures meet, compare, and combine, played out over trillions of interactions and interaction chains.
(I'm consciously ignoring the quantum side, because A) I don't understand it well enough and B) the structure/charge lens seems to be basically sufficient.)
It's worth taking a look at some of the cell chemical pathways to see how some of this plays out - take a look at things like the Krebs cycle, which are basically steps of interactions in which a molecule or several are modified step by step in a series of splits, joins, adds, and subtractions that allow for the next step.
Part of what makes this tricky is that, while you can "zoom out" and focus on larger systems like neurons or genomes, the molecular interaction model shows up all over the place - neurons fire to the degree they do because of charge accumulation, DNA & RNA transcription are strongly affected by Weird Molecular Interactions, protein folding is at least partially a product of charge affinities, enzymes work as they do because of structure and charges. This is why a lot of these problems are enormously computationally difficult - it's a physical system, not a logical one - there's no way to isolate any layer from any other layer.
(I deeply welcome corrections on any of the above, by the way - I've spent time reading on all of this, but I'm not a professional by any stretch. The above is the model I've acquired over time, not reality.)
Not as a correction but more of an into the weeds clarification; neurons at rest have a pretty stable cytosolic ionic composition, they hover around -80mV resting potential due to leak channels.
The firing of an action potential, on the other hand, only happens when they become depolarized enough to reach their threshold potential. If they reach the threshold (generally due to ligand-receptor binding) then voltage gated sodium channels open up and the neuron gets flooded with positive charge - this is the electrical impulse that moves down the axon.
To be honest I'm not someone who knows much about about computer science, but in terms of a boolean type operator the closest thing that comes to mind is the threshold potential? It's an all or nothing process, either T or F, and if it's true then an action potential is generated.
In terms of neural computation modeling or in terms of morphogenetic models that are fully distributed, we are not even close, in my non-expert opinion.
Does biology have an equivalent? Or is your comment essentially explaining that it probably does, but is more complex and we don't understand it yet?