Entropy enters the equation when you frame all three in terms of information theory, dynamical systems, and chaos theory. I can only put this into terms on my own conjectures, but the question for me was how can you represent, encode, and operate algebraically on information and state of a system? Or in other words, can you represent dynamical information (work) over time. More specifically, you want to know when the homological representational model becomes chaotic and to do that you need a way to measure its entropy.
My conjecture was along the lines, that higher ranking primality of the knot simplex of a dynamical model of some datum of the system implies less entropy, but only if state dictates the representation, then the operations that take the ambient isotopy (let's call it a "polymer") from state k to k' that doesn't result in any new topology (but doesn't violate it either) is the unknot and represents no new or "surprise" entropy, and there no new information. Or at least that's how far I was at as of the other day, but the question was always how to model this entropy in terms of homology to start.
The practical implications of this paper are profound, but probably won't see application in software and networking until a number of conjectures in prime number theory are answered. But still, this was long over due. HN is certainly more interesting at night, ha.