Interaction-motif-based classification of self-organizing metabolic cycles

TL;DR

This paper develops a classification scheme for the stability of metabolic cycles formed by chemotactic particles interacting through substrate and product concentration fields, revealing how specific interaction motifs influence collective behavior.

Contribution

It introduces a novel motif-based classification framework for the stability of self-organizing metabolic cycles with chemotactic interactions.

Findings

Derived parameter-free stability conditions for substrate-only chemotactic cycles.

Extended the framework to classify stability in complex substrate- and product-chemotactic networks.

Provided a systematic approach to analyze large interaction networks in active matter systems.

Abstract

Particles that are catalytically-active and chemotactic can interact through the concentration fields upon which they act, which in turn may lead to wide-scale spatial self-organization. When these active particles interact through several fields, these interactions gain an additional structure, which can result in new forms of collective behaviour. Here, we study a mixture of active species which catalyze the conversion of a substrate chemical into a product chemical, and chemotax in concentration gradients of both substrate and product. Such species develop non-reciprocal, specific interactions that we coarse-grain into attractive and repulsive, which can lead to a potentially complex interaction network. We consider the particular case of a metabolic cycle of three species, each of which interacts with itself and both other species in the cycle. We find that the stability of a cycle of species that only chemotax in gradients of their substrate is piloted by a set of two parameter-free conditions, which we use to classify the low number of corresponding interaction networks. In the more general case of substrate- and product-chemotactic species, we can derive a set of two high-dimensional stability conditions, which can be used to classify the stability of all the possible interaction networks based on the self- and pair-interaction motifs they contain. The classification scheme that we introduce can help guide future studies on the dynamics of complex interaction networks and explorations of the corresponding large parameter spaces in such metabolically active complex systems.