Saturation-Based Atom Provenance Tracing in Chemical Reaction Networks

TL;DR

This paper introduces a saturation-based computational framework for atom provenance tracing in chemical reaction networks, enabling exhaustive, customizable, and efficient enumeration of labeled molecular configurations without combinatorial explosion.

Contribution

The authors present a novel saturation-based method that models atom tracing using Kleisli morphisms, allowing for scalable and exact enumeration of atom labels in reaction networks.

Findings

Successfully reproduces known labeling patterns in biochemical pathways.

Automatically discovers steady-state labeling behaviors.

Reduces network size by focusing on relevant isotopomers.

Abstract

Atom tracing is essential for understanding the fate of labeled atoms in biochemical reaction networks, yet existing computational methods either simplify label correlations or suffer from combinatorial explosion. We introduce a saturation-based framework for enumerating labeling patterns that directly operates on atom-atom maps without requiring flux data or experimental measurements. The approach models reaction semantics using Kleisli morphisms in the powerset monad, allowing for compositional propagation of atom provenance through reaction networks. By iteratively saturating all possible educt combinations of reaction rules, the method exhaustively enumerates labeled molecular configurations, including multiplicities and reuse. Allowing arbitrary initial labeling patterns - including identical or distinct labels - the method expands only isotopomers reachable from these inputs, keeping the configuration space as small as necessary and avoids the full combinatorial growth characteristic of previous approaches. In principle, even every atom could carry a distinct identifier (e.g., tracing all carbon atoms individually), illustrating the generality of the framework beyond practical experimental limitations. The resulting template instance hypergraph captures the complete flow of atoms between compounds and supports projections tailored to experimental targets. Customizable labeling sets significantly reduce generated network sizes, providing efficient and exact atom traces focused on specific compounds or available isotopes. Applications to the tricarboxylic acid cycle, and glycolytic pathways demonstrate that the method fully automatically reproduces known labeling patterns and discovers steady-state labeling behavior. The framework offers a scalable, mechanistically transparent, and generalizable foundation for isotopomer modeling and experiment design.