Simplifying Biochemical Models With Intermediate Species

TL;DR

This paper introduces a systematic algebraic framework to classify and analyze biochemical models with intermediate species, revealing how these intermediates influence model properties like stability and steady states.

Contribution

It provides a rigorous mathematical classification of models with intermediates, offering guidelines for model selection and comparison based on core model properties.

Findings

Intermediates often do not alter steady-state concentrations if no conservation laws exist.

Models can be grouped into classes characterized by simple canonical models.

The framework helps distinguish model properties and robustness to intermediates.

Abstract

Mathematical models are increasingly being used to understand complex biochemical systems, to analyze experimental data and make predictions about unobserved quantities. However, we rarely know how robust our conclusions are with respect to the choice and uncertainties of the model. Using algebraic techniques we study systematically the effects of intermediate, or transient, species in biochemical systems and provide a simple, yet rigorous mathematical classification of all models obtained from a core model by including intermediates. Main examples include enzymatic and post-translational modification systems, where intermediates often are considered insignificant and neglected in a model, or they are not included because we are unaware of their existence. All possible models obtained from the core model are classified into a finite number of classes. Each class is defined by a mathematically simple canonical model that characterizes crucial dynamical properties, such as mono- and multistationarity and stability of steady states, of all models in the class. We show that if the core model does not have conservation laws, then the introduction of intermediates does not change the steady-state concentrations of the species in the core model, after suitable matching of parameters. Importantly, our results provide guidelines to the modeler in choosing between models and in distinguishing their properties. Further, our work provides a formal way of comparing models that share a common skeleton.