Generative processes in biology are complex and fascinating. A lot of modelling builds simpler models which can be analyzed but don't explicitly simulate the f

Generative processes in biology are complex and fascinating. A lot of modelling builds simpler models which can be analyzed but don't explicitly simulate the full process of growth and development (which would require extreme memory and CPU). There is an open question in the field about how closely models need to recapitulate the underlying biology to be useful (in terms of generalized predictive ability). Some interesting reading on development and math modelling: https://en.wikipedia.org/wiki/Reaction%E2%80%93diffusion_system https://en.wikipedia.org/wiki/Reaction%E2%80%93diffusion_sys... https://en.wikipedia.org/wiki/Multi-state_modeling_of_biomolecules https://en.wikipedia.org/wiki/Multi-state_modeling_of_biomol... https://en.wikipedia.org/wiki/Pattern_formation https://en.wikipedia.org/wiki/Pattern_formation When I was growing up, and until not too much longer ago, I assumed it would be practical to build full molecular dynamic simulations with atomic or quantum details, simulating large systems like groups of cells. Now I appreciate that this would be a lot of work that could be better handled by a well-trained deep neural net whose model does not recapitulate the underlying mechanics.