I think the question and article is fascinating. I do think that one should not only look at the genetic representation, and the algorithm a cell runs, but also

I think the question and article is fascinating. I do think that one should not only look at the genetic representation, and the algorithm a cell runs, but also the formalized environment that runs the simulation. I’ve been thinking about this for a few years and I truly believe these cells need to be able to “move” in either a 3d lattice similar to a cellular automata or become nodes in some cyclic graph (or maybe both). For the genetic representation, I had an insight that DNA/RNA looks strangely similar to SKI calculus (combinators) and have been using a Church encoding to translate the SKI program to an “action” per turn (move, connect, spike, divide, etc.). It has a nice property that the cell’s “program” and the spikes between them, and the input/output to the simulation itself can be the same thing (just a string of combinators). Either way the author is spot on when it comes to this sort of thing having the properties 1) “always on” (no separate training mode), 2) local interactions, 3) everything stems from a genome, 4) each cell is its own little mini-program with the same genetics as everyone else. All I can say is that evolutionary program is hard and takes either massive compute or large timescales to run the simulations. Fascinating stuff though