Reverse-Robust Computation with Chemical Reaction Networks
For wet-lab implementations of CRNs, this work ensures that theoretical results for irreversible reactions hold despite unavoidable reversibility, bridging theory and practice.
Chemical reaction networks (CRNs) can stably compute all semilinear predicates and functions even when reactions are reversible up to a cutoff point, showing that existing irreversible constructions remain valid under reverse-robust computation.
Chemical reaction networks, or CRNs, are known to stably compute semilinear Boolean-valued predicates and functions, provided that all reactions are irreversible. However, this property does not hold for wet-lab implementations, as all chemical reactions are reversible, even at very slow rates. We study the computational power of CRNs under the reverse-robust computation model, where reactions are permitted to occur either in forward or in reverse up to a cutoff point, after which they may only occur in forward. Our main results show that all semilinear predicates and all semilinear functions can be computed reverse-robustly, and in fact, that existing constructions continue to hold under the reverse-robust computational model. A key tool used to prove correctness under the reverse-robust computation model is invariants: linear (or linear modulo some $m$) combinations of the counts of the species that are preserved by all reactions.