There was an early paper on the MWC molecule that showed that you could get different logical gates by changing binding energies. At the same time, we all know that the Izhikevich neuron can yield many different types of neural behavior that all have different computational properties.
At root, gene regulatory networks and neural networks must perform computations on inputs-- really, arbitrary computations. How do they succeed? I think it has something to do with the presence of different apparent activation functions in the biophysical network. Take a gene regulatory network, with its first layer being production of mRNA and its second layer being production of protein. The first layer is full of different apparent activation functions-- same thermodynamic model underlying, but different apparent logical functions based on changes in binding energy, leading to literally any computation you want. Or take a neural network, with its many layers. Every layer might have different apparent activation functions from the same underlying dynamical system, with a few parameters changed.
The weird part about this is that it seems easy to analyze if you view it as: there is some underlying hidden state dynamics that describes everything, and the only thing you're changing are parameters of the hidden state dynamics to get different apparent activation functions.