Injectivity, multiple zeros, and multistationarity in reaction networks

TL;DR

This paper introduces a new algebraic method based on injectivity to determine the number of positive steady states in reaction networks modeled by polynomial dynamical systems, aiding understanding of multistationarity.

Contribution

The paper presents a novel injectivity-based approach to efficiently analyze the number of positive solutions in parameterized polynomial systems, applicable to reaction networks.

Findings

Method successfully precludes or confirms multiple steady states.

Applicable to generalised polynomial systems with linear, parameter-independent integrals.

Tested across diverse reaction network models.

Abstract

Polynomial dynamical systems are widely used to model and study real phenomena. In biochemistry, they are the preferred choice for modelling the concentration of chemical species in reaction networks with mass-action kinetics. These systems are typically parameterised by many (unknown) parameters. A goal is to understand how properties of the dynamical systems depend on the parameters. Qualitative properties relating to the behaviour of a dynamical system are locally inferred from the system at steady state. Here we focus on steady states that are the positive solutions to a parameterised system of generalised polynomial equations. In recent years, methods from computational algebra have been developed to understand these solutions, but our knowledge is limited: for example, we cannot efficiently decide how many positive solutions the system has as a function of the parameters. Even deciding whether there is one or more solutions is non-trivial. We present a new method, based on so-called injectivity, to preclude or assert that multiple positive solutions exist. The results apply to generalised polynomials and variables can be restricted to the linear, parameter-independent first integrals of the dynamical system. The method has been tested in a wide range of systems.