Abstract
For many biological systems, a variety of simulation models exist. A new simulation model is rarely developed from scratch, but rather revises and extends an existing one. A key challenge, however, is to decide which model might be an appropriate starting point for a particular problem and why. To answer this question, we need to identify entities and activities that contributed to the development of a simulation model. Therefore, we exploit the provenance data model, PROV-DM, of the World Wide Web Consortium and, building on previous work, continue developing a PROV ontology for simulation studies. Based on a case study of 19 Wnt/β-catenin signaling models, we identify crucial entities and activities as well as useful metadata to both capture the provenance information from individual simulation studies and relate these forming a family of models. The approach is implemented in WebProv, a web application for inserting and querying provenance information. Our specialization of PROV-DM contains the entities Research Question, Assumption, Requirement, Qualitative Model, Simulation Model, Simulation Experiment, Simulation Data, and Wet-lab Data as well as activities referring to building, calibrating, validating, and analyzing a simulation model. We show that most Wnt simulation models are connected to other Wnt models by using (parts of) these models. However, the overlap, especially regarding the Wet-lab Data used for calibration or validation of the models is small. Making these aspects of developing a model explicit and queryable is an important step for assessing and reusing simulation models more effectively. Exposing this information helps to integrate a new simulation model within a family of existing ones and may lead to the development of more robust and valid simulation models. We hope that our approach becomes part of a standardization effort and that modelers adopt the benefits of provenance when considering or creating simulation models.
Highlights
Mechanistic, biochemical models are implemented and questioned to deepen the understanding of biological systems
Based on our earlier work on provenance of simulation models, we refine a specialization of the PROV Data Model (PROV-DM) and, define a PROV ontology that is capable of both relating simulation models and reporting their generation processes
We examine the level of detail, or granularity, that is necessary to capture relevant information of the provenance of simulation studies
Summary
Mechanistic, biochemical models are implemented and questioned to deepen the understanding of biological systems. These models are usually the results of simulation studies that include phases of refinement and extension of simulation models together with the execution of diverse in silico (simulation) experiments. Depending on the application domain, different modeling approaches have their own documentation guidelines [4,5,6]. When looking at an entire simulation study and at the generation process of the included simulation model, these guidelines provide some indication about what information might be useful for documenting a complete simulation study as well as for establishing relationships between different simulation models
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