Reusing simulation experiment specifications to support developing models by successive extension

Peng, Danhua; Warnke, Tom; Haack, Fiete; Uhrmacher, Adelinde M.

doi:10.1016/j.simpat.2016.07.006

Cited by 16 publications

(10 citation statements)

References 29 publications

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“…For this purpose, SED-ML, an external domain-specific language, has been developed (Bergmann et al 2018). Also, domain-specific languages such as SESSL (Ewald and Uhrmacher 2014) allow for easily replicating simulation experiments (Peng et al 2016), as do model-based approaches such as the one by Teran-Somohano et al (2015). The later are part of recent developments to treat simulation experiments as first-class citizen in simulation, and to make the hypotheses they are based upon explicit (Peng et al 2014;Lorig et al 2017;Agha and Palmskog 2018).…”

Section: Provenance Information About Simulation Data -Workflows Scripting and Domainspecific Languagesmentioning

confidence: 99%

“…Approaches with a focus on how something has been generated, which entail an unambiguous and executable semantics, allow not only for a convenient replication of modeling and simulation results, but also facilitate the generation of new results. For example, the specification of simulation experiments in SESSL does not only allow for replicating simulation data, but supports -as part of the provenance of simulation models -the automatic generation of new simulation experiments for newly extended or composed models to test specific behavior patterns (Peng et al 2016). Thus, provenance, information about the past does not only allow for understanding the present, but also for designing the future, in opening up new avenues for generating and analyzing simulation models.…”

Section: Provenance Beyond Reproducibilitymentioning

confidence: 99%

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CRISIS, WHAT CRISIS – DOES REPRODUCIBILITY IN MODELING & SIMULATION REALLY MATTER?

Taylor¹,

Eldabi²,

Monks³

et al. 2018

2018 Winter Simulation Conference (WSC)

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How important it is to our discipline that we can reproduce the results of Modeling & Simulation (M&S) research? How important is it to be able to (re)use the models, data, and methods described in simulation publications to reproduce published results? Is it really that important or are the lessons and experiences described in a paper enough for us to build on the work of others? At the 2016 Winter Simulation Conference, a panel considered opinions on reproducibility in discrete-event simulation. This article builds on these and asks if there really is a reproducibility crisis in M&S? A diverse range of views on the subject are presented including reflections on the reproducibility in terms of the art and science of simulation, the frustrations of poor reproducibility, perspectives from the industrial production & logistics community, the wider context of open science and artefact sharing, and the role of provenance beyond reproducibility. INTRODUCTIONWhat does reproducibility mean in terms of Modeling & Simulation? Does reproducibility matter? To what degree is reproducibility possible? Could reproducibility policies have a negative impact on publishing in M&S? These questions were amongst some initially posed to our panelists as a starting point to gather a range of views on this potentially important topic. This panel paper presents a range of views on the subject and contributes to the on-going debate about the role of reproducibility in our discipline.

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Section: Provenance Information About Simulation Data -Workflows Scripting and Domainspecific Languagesmentioning

confidence: 99%

Section: Provenance Beyond Reproducibilitymentioning

confidence: 99%

CRISIS, WHAT CRISIS – DOES REPRODUCIBILITY IN MODELING & SIMULATION REALLY MATTER?

Taylor¹,

Eldabi²,

Monks³

et al. 2018

2018 Winter Simulation Conference (WSC)

Self Cite

View full text Add to dashboard Cite

show abstract

“…In general, simulation models are the outcome of extensive as well as interactive model and data generation activities. These include, in addition to executing various simulation experiments and successive model refinements, the adaptation of already existing models, for example, by composition or extension [ 16 – 18 ]. Therefore, the complexity of a model grows over time as researchers add parts to the model or refine it.…”

Section: Introductionmentioning

confidence: 99%

Relating simulation studies by provenance—Developing a family of Wnt signaling models

et al. 2021

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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.

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“…Particularly, larger models are usually not built from scratch [15]. In general, simulation models are the outcome of extensive as well as interactive model and data generation activities that include, in addition to executing various simulation experiments and successive model refinements, the adaptation of already existing models, for example, by composition or extension [15][16][17]. Therefore, the complexity of a model grows over time as researchers add parts to the model or refine it.…”

Section: Introductionmentioning

confidence: 99%

Relating simulation studies by provenance—Developing a family of Wnt signaling models

Budde

Smith

Wilsdorf

et al. 2021

Preprint

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For many cell-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 and look at entities and activities that contributed to the development of a simulation model. Therefore, we exploit the PROV Data Model (PROV-DM) and, building on previous work, continue developing a PROV ontology for simulation models. Based on a concrete 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 of individual simulation studies and relate these forming a family of models. The approach is implemented in WebProv, which allows one to insert and query 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 Simulation Models is small. Making these aspects of developing a model explicit and queryable is a crucial step for assessing and reusing simulation models more effectively. The unambiguous specification of information helps to integrate a new simulation model within the family of existing ones. Our approach opens up a wealth of knowledge that may lead to the development of more robust and valid simulation models. We hope that it becomes part of a standardization effort and that modelers adopt the benefits of provenance when considering or creating simulation models.

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Reusing simulation experiment specifications to support developing models by successive extension

Cited by 16 publications

References 29 publications

CRISIS, WHAT CRISIS – DOES REPRODUCIBILITY IN MODELING & SIMULATION REALLY MATTER?

CRISIS, WHAT CRISIS – DOES REPRODUCIBILITY IN MODELING & SIMULATION REALLY MATTER?

Relating simulation studies by provenance—Developing a family of Wnt signaling models

Relating simulation studies by provenance—Developing a family of Wnt signaling models

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