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RunBioSimulations: an extensible web application that simulates a wide range of computational modeling frameworks,
Bilal Shaikh1, Gnaneswara Marupilla2, Mike Wilson2
1Icahn Institute for Data Science & Genomic Technology and Department of Genetics & Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1425 Madison Avenue, New York, NY 10029, USA.
Nucleic Acids Research
|May 21, 2021
Summary
RunBioSimulations simplifies complex biological modeling by integrating diverse simulation tools into one platform. This enhances model reproducibility and collaboration for creating more predictive scientific models.
Area of Science:
- Computational biology
- Bioengineering
- Biomedical science
Background:
- Predictive computational models are crucial for advancing science, bioengineering, and medicine.
- Integrating multiple submodels and frameworks is key for capturing biological complexity across scales.
- Existing simulation resources are numerous and fragmented, posing challenges for users, especially novices.
Purpose of the Study:
- To develop a unified web application, RunBioSimulations, for executing a wide range of computational models.
- To simplify the accessibility and usability of diverse modeling frameworks and simulation algorithms.
- To foster reproducibility and collaboration in computational biology.
Main Methods:
- Developed RunBioSimulations, a web application integrating various simulation tools.
- Leveraged community resources, including BioSimulators, an open registry of simulation tools.
- Enabled execution of nine frameworks and 44 algorithms, with extensibility for more.
Main Results:
- RunBioSimulations provides a single interface for executing diverse computational models.
- The platform currently supports nine frameworks and 44 algorithms.
- Features for sharing simulations and interactive visualization of results are included.
Conclusions:
- RunBioSimulations streamlines the use of computational modeling tools, promoting reproducibility.
- The platform facilitates collaboration among researchers.
- It is expected to accelerate the development of more predictive computational models in science and medicine.
