Quantitative model refinement as a solution to the combinatorial size explosion of biomodels

A1 Journal article (refereed)


Internal Authors/Editors


Publication Details

List of Authors: Elena Czeizler, Eugen Czeizler, Bogdan Iancu, Ion Petre
Publisher: Elsevier
Publication year: 2012
Journal: Electronic Notes in Theoretical Computer Science
Volume number: 284
Start page: 35
End page: 53


Abstract

Building a large system through a systematic, step-by-step refinement of an initial abstract specification is a well established technique in software engineering, not yet much explored in systems biology. In the case of systems biology, one starts from an abstract, high-level model of a biological system and aims to add more and more details about its reactants and/or reactions, through a number of consecutive refinement steps. The refinement should be done in a quantitatively correct way, so that (some of) the numerical properties of the model (such as the experimental fit and validation) are preserved. In this study, we focus on the data-refinement mechanism where the aim is to increase the level of details of some of the reactants of a given model. That is, we analyse the case when a model is refined by substituting a given species by several types of subspecies. We show in this paper how the refined model can be systematically obtained from the original one. As a case study for this methodology we choose a recently introduced model for the eukaryotic heat shock response, Petre(2011:595- 612). We refine this model by including details about the acetylation of the heat shock factors and its influence on the heat shock response. The refined model has a significantly higher number of kinetic parameters and variables. However, we show that our methodology allows us to preserve the experimental fit/validation of the model with minimal computational effort.


Keywords

heat shock response, quantitative model refinement

Last updated on 2019-15-10 at 03:05