I'm curious about your thoughts (if you have any) on the boolean modeling formalism. Basically, you represent bio-molecules as having two states: active and inactive. Their states change according to boolean logic update rules that are determined by the state of other molecules in the system. You end up with a very simple dynamical system. Theoretical biologists have been working with Boolean models for >15 years [1]. Boolean circuits also have some pretty deep connections to theoretical computer science [2]. The goal of this very simple formalism is to get the structure of a system, while retaining much of the quantitative behavior. How productive this is depends on your perspective, I guess.
Also, that's a pretty uncharitable view of systems biology. It seems clear to me that understanding even moderately complex phenotypes practically requires models of biology that include many molecules, with significant feedback loops. Further we see emergent biological behavior across multiple scales of time and space, from milli-second long protein-protein interactions to multi-year developmental processes. Systems biology is basically just studying biology while taking all of that into account. That seems worth studying to me, especially given the ineffectiveness of our current therapies in managing most diseases.
So, I'm interested in what parts of systems biology you would describe as "smoke and mirrors".
[1] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6388622/pdf/nih...
[2] https://www.quantamagazine.org/mathematician-solves-computer...