Modeling Biology in Modelica: The Human Baroreflex

Schölzel, Christopher; Goesmann, Alexander; Ernst, Gernot; Dominik, Andreas

doi:10.3384/ecp15118367

Cited by 5 publications

(3 citation statements)

References 18 publications

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“…In an attempt to reproduce simulation results, it is unlikely that the goal is to reproduce the full code with the exact same structure as before. This was also the case for us, as we wanted to include the model in a high-level model of the human baroreflex [66,67]. For this task, we also wanted to adhere to our MoDROGH guidelines [23].…”

Section: Problem Descriptionmentioning

confidence: 99%

Countering reproducibility issues in mathematical models with software engineering techniques: A case study using a one-dimensional mathematical model of the atrioventricular node

Schölzel

Blesius

Ernst

et al. 2021

Preprint

Self Cite

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One should assume that in silico experiments in systems biology are less susceptible to reproducibility issues than their wet-lab counterparts, because they are free from natural biological variations and their environment can be fully controlled. However, recent studies show that only half of the published mathematical models of biological systems can be reproduced without substantial effort. In this article we examine the potential causes for failed or cumbersome reproductions in a case study of a one-dimensional mathematical model of the atrioventricular node, which took us four months to reproduce. The model features almost all common types of reproducibility issues including missing information, errors in equations and parameters, a lack in available data files, non-executable code, missing or incomplete experiment protocols, and missing semantic information about the rationale behind equations. Many of these issues seem similar to problems that have already been solved in software engineering using techniques such as unit testing, regression tests, continuous integration, version control, archival services, and a thorough modular design with extensive documentation. Applying these techniques, we reimplement the examined model using the modeling language Modelica. The resulting workflow can be applied to any mathematical model. It guarantees methods reproducibility by executing automated tests in a virtual machine on a server that is physically separated from the development environment. Additionally, it facilitates results reproducibility, because the model is more understandable and because the complete model code, experiment protocols, and simulation data are published and can be accessed in the exact version that was used in this article. While the increased attention to design aspects and documentation required considerable effort, we found it justified, even just considering the immediate benefits during development such as easier and faster debugging, increased understandability of equations, and a reduced requirement for looking up details from the literature.

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Section: Problem Descriptionmentioning

confidence: 99%

Countering reproducibility issues in mathematical models with software engineering techniques: A case study using a one-dimensional mathematical model of the atrioventricular node

Schölzel

Blesius

Ernst

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

Section: Semantics Lost In the Chain Of Referencesmentioning

confidence: 99%

Countering reproducibility issues in mathematical models with software engineering techniques: A case study using a one-dimensional mathematical model of the atrioventricular node

et al. 2021

Self Cite

View full text Add to dashboard Cite

One should assume that in silico experiments in systems biology are less susceptible to reproducibility issues than their wet-lab counterparts, because they are free from natural biological variations and their environment can be fully controlled. However, recent studies show that only half of the published mathematical models of biological systems can be reproduced without substantial effort. In this article we examine the potential causes for failed or cumbersome reproductions in a case study of a one-dimensional mathematical model of the atrioventricular node, which took us four months to reproduce. The model demonstrates that even otherwise rigorous studies can be hard to reproduce due to missing information, errors in equations and parameters, a lack in available data files, non-executable code, missing or incomplete experiment protocols, and missing rationales behind equations. Many of these issues seem similar to problems that have been solved in software engineering using techniques such as unit testing, regression tests, continuous integration, version control, archival services, and a thorough modular design with extensive documentation. Applying these techniques, we reimplement the examined model using the modeling language Modelica. The resulting workflow is independent of the model and can be translated to SBML, CellML, and other languages. It guarantees methods reproducibility by executing automated tests in a virtual machine on a server that is physically separated from the development environment. Additionally, it facilitates results reproducibility, because the model is more understandable and because the complete model code, experiment protocols, and simulation data are published and can be accessed in the exact version that was used in this article. We found the additional design and documentation effort well justified, even just considering the immediate benefits during development such as easier and faster debugging, increased understandability of equations, and a reduced requirement for looking up details from the literature.

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“…It is called a pressure buffer system and the nerves from the baroreceptors are called buffer nerves [18]. A rise in arterial pressure stretches the baroreceptors and causes them to transmit signals into the CNS.…”

Section: Baroreceptor Mechanismmentioning

confidence: 99%

An Electrical Perspective of the Baroreflex Feedback Mechanism for Heart Rate Control

Moin

Chowdhry²,

Uddin

2017

IJBSBT

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Modeling Biology in Modelica: The Human Baroreflex

Cited by 5 publications

References 18 publications

Countering reproducibility issues in mathematical models with software engineering techniques: A case study using a one-dimensional mathematical model of the atrioventricular node

Countering reproducibility issues in mathematical models with software engineering techniques: A case study using a one-dimensional mathematical model of the atrioventricular node

Countering reproducibility issues in mathematical models with software engineering techniques: A case study using a one-dimensional mathematical model of the atrioventricular node

An Electrical Perspective of the Baroreflex Feedback Mechanism for Heart Rate Control

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