Biochemical and gene regulatory dynamics as reservoir-like substrates for natural computing

This perspective argues that biochemical reaction systems and gene regulatory systems can be interpreted not only as architectures of homeostatic control and adaptation, but also as reservoir-like dynamical substrates for natural computing. The claim is deliberately moderate: biological regulation is not denied, but some control-like functions are re-read as readouts implemented on top of richer input-driven, recurrent, dissipative, history-dependent, and often stochastic state dynamics. The distinctive contribution is not a new reservoir algorithm, nor the generic observation that dynamical systems can serve as reservoirs; it is a substrate-oriented reframing for biochemical and gene-regulatory modeling that places biochemical reaction dynamics, BST/S-systems and dBST, stochastic process descriptions, Boolean abstractions, and emerging live-cell observability within one experimentally testable perspective. To keep the argument operational rather than metaphorical, the manuscript outlines a minimal evaluation protocol using delayed-input reconstruction, information-processing capacity, nonlinear separation, and perturbation-aware prediction. The perspective is timely because recent cell-based reservoir studies, topology-aware reservoir designs, living-cell signaling studies, and live-cell visualization technologies increasingly make substrate-level signal propagation experimentally accessible rather than purely metaphorical.