BioSimulators: a central registry of simulation engines and services for recommending specific tools

Shaikh, Bilal; Smith, Lucian P.; Vasilescu, Dan; Marupilla, Gnaneswara; Wilson, Michael; Agmon, Eran; Agnew, Henry; Andrews, Steven S.; Anwar, Azraf; Beber, Moritz Emanuel; Bergmann, Frank; Brooks, David J.; Brusch, Lutz; Calzone, Laurence; Choi, Kiri; Cooper, Joshua M.; Detloff, John; Drawert, Brian; Dumontier, Michel; Ermentrout, Bard; Faeder, James R.; Freiburger, Andrew Philip; Fröhlich, Fabian; Funahashi, Akira; Garny, Alan; Gennari, John H.; Gleeson, Padraig; Goelzer, Anne; Haiman, Zachary B.; Hasenauer, Jan; Hellerstein, Joseph L.; Hermjakob, Henning; Hoops, Stefan; Ison, Jon C.; Jahn, Diego; Jakubowski, Henry V.; Jordan, Ryann; Kalaš, Matúš; König, Matthias; Liebermeister, Wolfram; Sheriff, Rahuman S. Malik; Mandal, Synchon; McDougal, Robert A.; Medley, Kyle; Mendes, Pedro; Mendes, Pedro; Myers, Chris J.; Naldi, Aurélien; Nguyen, Tung V. N.; Nickerson, David; Olivier, Brett G.; Patoliya, Drashti; Paulevé, Loı̈c; Petzold, Linda R.; Priya, Ankita; Rampadarath, Anand; Rohwer, Johann M.; Saglam, Ali S.; Singh, Dilawar; Sinha, Ankur; Snoep, Jacky L.; Sorby, Hugh; Spangler, Ryan; Starruß, Jörn; Thomas, Payton J.; Niekerk, David van; Weindl, Daniel; Zhang, Fengkai; Zhukova, Anna; Goldberg, Arthur P.; Schaff, James C.; Blinov, Michael L.; Sauro, Herbert M.; Moraru, Ion I.; Karr, Jonathan R.

doi:10.1093/nar/gkac331

Cited by 16 publications

(10 citation statements)

References 16 publications

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“…BioSimulators relies heavily on systems biology community standards, such as CellML, SBML, SED-ML and the COMBINE archive format, as well as validation tools to ensure proper implementation and use of these standards. The registry also offers recommendation services, in an effort to help modellers select the best way to share, reproduce and combine simulations [ 60 ].…”

Section: Interoperability Through Standards’ Implementation In Tools ...mentioning

confidence: 99%

Addressing barriers in comprehensiveness, accessibility, reusability, interoperability and reproducibility of computational models in systems biology

Niarakis

Waltemath

Glazier

et al. 2022

Briefings in Bioinformatics

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Computational models are often employed in systems biology to study the dynamic behaviours of complex systems. With the rise in the number of computational models, finding ways to improve the reusability of these models and their ability to reproduce virtual experiments becomes critical. Correct and effective model annotation in community-supported and standardised formats is necessary for this improvement. Here, we present recent efforts toward a common framework for annotated, accessible, reproducible and interoperable computational models in biology, and discuss key challenges of the field.

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Section: Interoperability Through Standards’ Implementation In Tools ...mentioning

confidence: 99%

Addressing barriers in comprehensiveness, accessibility, reusability, interoperability and reproducibility of computational models in systems biology

Niarakis

Waltemath

Glazier

et al. 2022

Briefings in Bioinformatics

Self Cite

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“…To reconstruct a wide range of cellular functions in a single model, it is essential to develop theories and techniques for integrating multiple methods consistently (Takahashi et al, 2004). In addition, improved modeling and simulation standards are required to share these comprehensive models (Hucka et al, 2003; Waltemath et al, 2016; Shaikh et al, 2022).…”

Section: Simulation Technologies Underpinning Whole‐cell Modelingmentioning

confidence: 99%

Technologies for whole‐cell modeling: Genome‐wide reconstruction of a cell in silico

Kaizu,

Takahashi

2023

Dev Growth Differ

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With advances in high‐throughput, large‐scale in vivo measurement and genome modification techniques at the single‐nucleotide level, there is an increasing demand for the development of new technologies for the flexible design and control of cellular systems. Computer‐aided design is a powerful tool to design new cells. Whole‐cell modeling aims to integrate various cellular subsystems, determine their interactions and cooperative mechanisms, and predict comprehensive cellular behaviors by computational simulations on a genome‐wide scale. It has been applied to prokaryotes, yeasts, and higher eukaryotic cells, and utilized in a wide range of applications, including production of valuable substances, drug discovery, and controlled differentiation. Whole‐cell modeling, consisting of several thousand elements with diverse scales and properties, requires innovative model construction, simulation, and analysis techniques. Furthermore, whole‐cell modeling has been extended to multiple scales, including high‐resolution modeling at the single‐nucleotide and single‐amino acid levels and multicellular modeling of tissues and organs. This review presents an overview of the current state of whole‐cell modeling, discusses the novel computational and experimental technologies driving it, and introduces further developments toward multihierarchical modeling on a whole‐genome scale.This article is protected by copyright. All rights reserved.

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“…Parameter estimation can be performed using SBML-supporting tools such as COPASI, MATLAB, D2D, dMOD, parPE, pyABC and pyPESTO (32). Most of the tools described in this article are also freely available through BioSimulators (https://biosimulators.org), a central registry of biosimulation tools to facilitate reproducible simulation (33). Hence, adapting SBML for epidemiological modeling will provide an added advantage in addition to tackling several of the issues which are impediments to model reproducibility and repurposing.…”

Section: Improving Model Interoperability and Reusabilitymentioning

confidence: 99%

FAIR Sharing of Reproducible Models of Epidemic and Pandemic Forecast

Ramachandran¹,

König²,

Scharm³

et al. 2022

Preprint

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A major challenge for the dissemination, replication, and reuse of epidemiological forecasting studies during COVID-19 pandemics is the lack of clear guidelines and platforms to exchange models in a Findable, Accessible, Interoperable, and Reusable (FAIR) manner, facilitating reproducibility of research outcomes. During the beginning of pandemics, models were developed in diverse tools that were not interoperable, opaque without traceability and semantics, and scattered across various platforms - making them hard to locate, infer and reuse. In this work, we demonstrate that implementing the standards developed by the systems biology community to encode and share COVID-19 epidemiological models can serve as a roadmap to implement models as a tool in medical informatics, in general. As a proof-of-concept, we encoded and shared 24 epidemiological models using the standard format for model exchange in systems biology, annotated them with cross-references to data resources, packed up all associated files in COMBINE archives for easy sharing, and finally, disseminated the models through BioModels repository to significantly enhance their reproducibility and repurposing potential. We recommend the use of systems biology standards to encode and share models of epidemic and pandemic forecasts to improve their findability, accessibility, interoperability, reusability, and reproducibility.

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BioSimulators: a central registry of simulation engines and services for recommending specific tools

Cited by 16 publications

References 16 publications

Addressing barriers in comprehensiveness, accessibility, reusability, interoperability and reproducibility of computational models in systems biology

Addressing barriers in comprehensiveness, accessibility, reusability, interoperability and reproducibility of computational models in systems biology

Technologies for whole‐cell modeling: Genome‐wide reconstruction of a cell in silico

FAIR Sharing of Reproducible Models of Epidemic and Pandemic Forecast

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