[bbctol]

I might just not be sure what you mean by DNN here. A deep neural network is a specific architecture, consisting of input and output layers of discrete nodes, connected through many hidden layers of nodes, with each node able to perform simple operations on the signals passing through it. So I don't see any way you could model the universe or a seed as a literal DNN; I interpreted the main reason for the analogy that they're a system in which storing knowledge (in an NN, as the weights of activation) is an intrinsic part of the way the system is active.

From there, you really can interpret most things as systems with embedded knowledge that both defines their activity and is adjusted by new activity. The position of each atom in an object is intrinsically both the information about that object, and the way that it behaves: as changes are made, the atoms react accordingly, affecting the behavior of the system.

You can view a seed as a "trained model" only insofar as the information on how to become a tree is encoded in the seed. The specific properties of DNNs (hidden layers that increase in abstraction) aren't really present, and anything can encode information: a set of clouds could be considered a trained model on how to make a hurricane, or a single person could be viewed as containing a trained model on how to start a company. Seeds are highly optimized through evolutionary pressure, but that applies to all complex systems, not just DNNs, and optimization is not just non-unique to DNNs but not even necessarily present in the architecture.