# Complete Characterization for the Fit-Preserving Data Refinement of Mass-Action Reaction Networks

D4 Published development or research report or study

Internal Authors/Editors

Publication Details

List of Authors: Cristian Gratie, Ion Petre

Publisher: Turku Centre for Computer Science (TUCS)

Place: Turku

Publication year: 2015

Start page: 1

End page: 21

ISBN: 978-952-12-3172-8

Abstract

The systematic development of large biological models often relies on iteratively adding more details to an initial, simplified, abstraction of the modeled system. In data refinement, species of the original model are substituted with several variants (subspecies) in the refined one, each with its own individual behavior. In this context, one can distinguish between structural refinement, where the aim is to generate meaningful refined reactions, and quantitative refinement, where one looks for a refined model that better fits available experimental data. The latter is generally a computationally expensive process, as it requires refitting the model and sometimes additional data.

It is possible to reuse parameter values from the previous iteration by constructing a fit-preserving refinement, i.e. one that captures the same species dynamics as the original model, but accounts for the newly introduced subspecies. We showed in previous work that fit-preserving refinements can be computed efficiently by providing simple linear constraints on the rate constants of the refined model, which are sufficient for fit-preservation.

In this paper, we extend our result and provide a complete characterization of fit-preserving refinement, in the form of a necessary and sufficient condition, applicable for mass-action reaction networks with uniquely identifiable rate constants. Furthermore, we discuss the refinement of a simple model, the Brusselator, and show that, while constrained to capture the same species dynamics as the original model, fit-preserving refinements can still be very different with respect to the behavior of subspecies. Furthermore, our examples provide an intuition about how experimental data can be used to guide model fitting by identifying classes of qualitatively distinct behaviors.

Keywords

biomodeling, canonical refinement, fit-preserving refinement, model fit, parameter estimation, quantitative model refinement