Network translation and steady state properties of chemical reaction systems

TL;DR

This paper explores how network translation can identify structures that lead to absolute concentration robustness in chemical reaction systems and provides a MILP algorithm for practical network translation.

Contribution

It introduces a method to identify network structures causing robustness and offers an improved MILP algorithm for network translation in chemical systems.

Findings

Network translation can reveal structures with absolute concentration robustness.

The MILP algorithm simplifies the identification of translated networks.

The method can derive steady state values of robust species.

Abstract

Network translation has recently been used to establish steady state properties of mass action systems by corresponding the given system to a generalized one which is either dynamically or steady state equivalent. In this work we further use network translation to identify network structures which give rise to the well-studied property of absolute concentration robustness in the corresponding mass action systems. In addition to establishing the capacity for absolute concentration robustness, we show that network translation can often provide a method for deriving the steady state value of the robust species. We furthermore present a MILP algorithm for the identification of translated chemical reaction networks that improves on previous approaches, allowing for easier application of the theory.