Chemical Transformation Motifs - Modelling Pathways as Integer Hyperflows

TL;DR

This paper introduces a formal mathematical framework for modeling chemical reaction pathways as integer hyperflows in directed hypergraphs, enabling detailed mechanistic analysis and pathway optimization beyond traditional methods.

Contribution

It presents a novel integer hyperflow model for chemical pathways, allowing mechanistic queries and automatic pathway enumeration, differing from flux-based approaches.

Findings

Optimized non-oxidative glycolysis pathways.

Demonstrated pathway enumeration in autocatalytic processes.

Validated the framework with real chemical reaction networks.

Abstract

We present an elaborate framework for formally modelling pathways in chemical reaction networks on a mechanistic level. Networks are modelled mathematically as directed multi-hypergraphs, with vertices corresponding to molecules and hyperedges to reactions. Pathways are modelled as integer hyperflows and we expand the network model by detailed routing constraints. In contrast to the more traditional approaches like Flux Balance Analysis or Elementary Mode analysis we insist on integer-valued flows. While this choice makes it necessary to solve possibly hard integer linear programs, it has the advantage that more detailed mechanistic questions can be formulated. It is thus possible to query networks for general transformation motifs, and to automatically enumerate optimal and near-optimal pathways. Similarities and differences between our work and traditional approaches in metabolic network analysis are discussed in detail. To demonstrate the applicability of the mathematical framework to real-life problems we first explore the design space of possible non-oxidative glycolysis pathways and show that recent manually designed pathways can be further optimised. We then use a model of sugar chemistry to investigate pathways in the autocatalytic formose process. A graph transformation-based approach is used to automatically generate the reaction networks of interest.