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.
- quantitative model refinement
- heat shock response