Reverse-Robust Computation with Chemical Reaction Networks

2026-04-15

AI Summary

Chemical reaction network (CRN) theory typically assumes irreversible reactions, yet all reactions in real biochemical systems are reversible, potentially undermining the validity of existing computational models in practice. This work proposes a “reverse-robust” CRN model in which reactions proceed bidirectionally when product concentrations remain below a threshold but become effectively unidirectional once the threshold is exceeded, thereby better aligning with realistic chemical kinetics. Leveraging analysis based on linear and mod-m linear invariants, the study establishes—for the first time—that this model can stably compute all semilinear predicates and functions while remaining compatible with established CRN constructions. By formulating a computational framework suited to reversible environments, this research significantly expands the applicability of CRNs in molecular programming and synthetic biology.

Technology Category

• Cognitive Modeling & Cognitive Systems: Computational Creativity
• Knowledge Representation and Reasoning: Computational Complexity of Reasoning
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• Social Networks and Social Media: Computational social science
• Web Mining and Content Analysis: Models for Web evolution
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

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.

Problem

• chemical reaction networks
• reverse-robust computation
• semilinear predicates
• semilinear functions
• reversibility

Innovation

• reverse-robust computation
• chemical reaction networks
• semilinear predicates
• invariants
• reversible reactions