Piecewise linear and Boolean models of chemical reaction networks

TL;DR

This paper provides a mathematical justification for reducing complex biochemical network models with Hill functions to simpler piecewise linear and Boolean models, especially when the Hill coefficient is small, facilitating analysis of their dynamics.

Contribution

It introduces a rigorous reduction method from nonlinear biochemical models to piecewise linear and Boolean systems for small Hill constants, supported by geometric singular perturbation theory.

Findings

Piecewise linear models closely match nonlinear models in dynamics.

Boolean networks effectively capture qualitative behavior.

Reduction is justified mathematically using geometric singular perturbation theory.

Abstract

Models of biochemical networks are frequently high-dimensional and complex. Reduction methods that preserve important dynamical properties are therefore essential in their study. Interactions between the nodes in such networks are frequently modeled using a Hill function, $x^n/(J^n+x^n)$. Reduced ODEs and Boolean networks have been studied extensively when the exponent $n$ is large. However, the case of small constant $J$ appears in practice, but is not well understood. In this paper we provide a mathematical analysis of this limit, and show that a reduction to a set of piecewise linear ODEs and Boolean networks can be mathematically justified. The piecewise linear systems have closed form solutions that closely track those of the fully nonlinear model. On the other hand, the simpler, Boolean network can be used to study the qualitative behavior of the original system. We justify the reduction using geometric singular perturbation theory and compact convergence, and illustrate the results in networks modeling a genetic switch and a genetic oscillator.