A Computational Approach to Steady State Correspondence of Regular and Generalized Mass Action Systems

TL;DR

This paper advances the theory of network translation in biochemical reaction networks, providing conditions for steady state correspondence and an MILP algorithm to identify such translations.

Contribution

It develops conditions for improper network translations to preserve steady states and introduces a MILP method to find these translations.

Findings

Derived conditions for improper translations to preserve steady states.

Developed a MILP algorithm to identify network translations.

Enhanced understanding of steady state correspondence in biochemical networks.

Abstract

It has been recently observed that the dynamical properties of mass action systems arising from many models of biochemical reaction networks can be derived by considering the corresponding properties of a related generalized mass action system. The correspondence process known as network translation in particular has been shown to be useful in characterizing a system's steady states. In this paper, we further develop the theory of network translation with particular focus on a subclass of translations known as improper translations. For these translations, we derive conditions on the network topology of the translated network which are sufficient to guarantee the original and translated systems share the same steady states. We then present a mixed-integer linear programming (MILP) algorithm capable of determining whether a mass action system can be corresponded to a generalized system through the process of network translation.