A parameter-free model discrimination criterion based on steady-state coplanarity

TL;DR

This paper introduces a parameter-free method for testing the compatibility of mass-action models with steady-state data by assessing coplanarity in a transformed variable space, avoiding nonlinear optimization.

Contribution

The authors develop a novel coplanarity-based criterion that does not require parameter estimation, simplifying model rejection using steady-state data.

Findings

Successfully applied to phosphorylation and signaling models

Avoids nonlinear optimization in model validation

Provides a new framework for data-driven model selection

Abstract

We describe a novel procedure for deciding when a mass-action model is incompatible with observed steady-state data that does not require any parameter estimation. Thus, we avoid the difficulties of nonlinear optimization typically associated with methods based on parameter fitting. The key idea is to use the model equations to construct a transformation of the original variables such that any set of steady states of the model under that transformation lies on a common plane, irrespective of the values of the model parameters. Model rejection can then be performed by assessing the degree to which the transformed data deviate from coplanarity. We demonstrate our method by applying it to models of multisite phosphorylation and cell death signaling. Although somewhat limited at present, our work provides an important first step towards a parameter-free framework for data-driven model selection.