SBML Level 3: an extensible format for the exchange and reuse of biological models

Sarah M. Keating, Dagmar Waltemath, Matthias König, Fengkai Zhang, Andreas Dräger, Claudine Chaouiya, Frank T. Bergmann, Andrew Finney, Colin S. Gillespie, Tomáš Helikar, Stefan Hoops, Rahuman S. Malik-Sheriff, Stuart L. Moodie, Ion I. Moraru, Chris J. Myers, Aurélien Naldi, Brett G. Olivier, Sven Sahle, James C. Schaff, Lucian P. SmithMaciej J. Swat, Denis Thieffry, Leandro Watanabe, Darren J. Wilkinson, Michael L. Blinov, Kimberly Begley, James R. Faeder, Harold F. Gómez, Thomas M. Hamm, Yuichiro Inagaki, Wolfram Liebermeister, Allyson L. Lister, Daniel Lucio, Eric Mjolsness, Carole J. Proctor, Karthik Raman, Nicolas Rodriguez, Clifford A. Shaffer, Bruce E. Shapiro, Joerg Stelling, Neil Swainston, Naoki Tanimura, John Wagner, Martin Meier-Schellersheim, Herbert M. Sauro, Bernhard Palsson, Hamid Bolouri, Hiroaki Kitano, Akira Funahashi, Henning Hermjakob

Research output: Contribution to journalReview articlepeer-review

21 Citations (Scopus)

Abstract

Systems biology has experienced dramatic growth in the number, size, and complexity of computational models. To reproduce simulation results and reuse models, researchers must exchange unambiguous model descriptions. We review the latest edition of the Systems Biology Markup Language (SBML), a format designed for this purpose. A community of modelers and software authors developed SBML Level 3 over the past decade. Its modular form consists of a core suited to representing reaction-based models and packages that extend the core with features suited to other model types including constraint-based models, reaction-diffusion models, logical network models, and rule-based models. The format leverages two decades of SBML and a rich software ecosystem that transformed how systems biologists build and interact with models. More recently, the rise of multiscale models of whole cells and organs, and new data sources such as single-cell measurements and live imaging, has precipitated new ways of integrating data with models. We provide our perspectives on the challenges presented by these developments and how SBML Level 3 provides the foundation needed to support this evolution.

Original languageEnglish
Article numbere9110
JournalMolecular Systems Biology
Volume16
Issue number8
DOIs
Publication statusPublished - 1 Aug 2020

Bibliographical note

Funding Information:
A.G. was supported by the Japanese Society for the Promotion of Science.

Funding Information:
D.K. was supported by Novo Nordisk Foundation grant NNF10CC1016517.

Funding Information:
M.G. was supported by funding to HITS from the German Federal Ministry of Education and Research (BMBF) as part of the Liver Systems Medicine Network LiSyM (FKZ 031L0056) and through the MulticellML project (FKZ 01ZX1707B), as well as from the European Union Horizon2020 framework programme of the European Commission under Grant Agreement 825843 and from the Klaus Tschira Foundation (KTS).

Funding Information:
D.D. was supported by grant iLite (Innovations for LIver and Tissue Engineering) from Agence nationale de la recherche (ANR, France) to INRIA, Paris, France; by grant LiSyM (Liver Systems Medicine) from Bundesministerium für Bildung und Forschung (BMBF, Germany) to IfADo, Dortmund, Germany; by grant VLN (Virtual Liver Network) from Bundesministerium für Bildung und Forschung (BMBF, Germany) to IfADo, Dortmund, Germany; by grant Physicancer from Institut national de la santé et de la recherche médicale (INSERM, France) to INRIA, Paris, France; and by grant NOTOX (Predicting long‐term toxic effects using computer models based on systems characterisations of organotypic cultures) from EU 7 Framework Programme to INRIA, Paris, France. th

Funding Information:
L.M.L. was supported by grant P41‐GM103313 from the NIH (US) to the University of Connecticut School of Medicine.

Funding Information:
M.L.B. was supported by grant P41‐GM103313 and R01‐GM095485 from the NIH (US) to the University of Connecticut School of Medicine.

Funding Information:
E.K. was supported by Deutsche Forschungsgemeinschaft (DFG, GRK1772, GRK 2290 and TRR175) and by People Programme (Marie Skłodowska‐Curie Actions H2020‐MSCA‐ITN‐2015‐675585).

Funding Information:
B.A.'s work on SBML Level 3 was supported by the National Institutes of Health (NIH, US) and he is currently employed by AstraZeneca.

Funding Information:
F.K. was supported by the Russian Foundation for Basic Research, 17‐00‐00296, Institute of Computational Technologies of SB RAS, Novosibirsk, 630090, Russian Federation (PI: Fedor Kolpakov).

Funding Information:
W.S.H. was supported by grant R01‐GM111510 from the National Institute of General Medical Sciences (NIGMS, US) of the NIH.

Funding Information:
C.E. was supported by CHARME, COST, Action CA15110 MoU (PI: Chris Evelo) to Maastricht University.

Funding Information:
We sincerely thank all current and past SBML users, developers, contributors, supporters, advisors, administrators, and community members. We give special thanks to the following people for contributions and support: Jim Anderson, Nadia Anwar, Gordon Ball, Duncan B?renguier, Upinder Bhalla, Fr?d?ric Y. Bois, Benjamin Bornstein, Richard Boys, Ann Chasson, Thomas Cokelaer, Marco Donizelli, Alexander D?rr, Marine Dumousseau (Sivade), Lisa Falk, David Fange, Ed Frank, Ralph Gauges, Martin Ginkel, Nail Gizzatkulov, Victoria Gor, Igor Goryanin, Ryan N. Gutenkunst, Arnaud Henry, Stefanie Hoffmann, Duncan Hull, Dagmar Iber, Gael Jalowicki, Henrik Johansson, Akiya Jouraku, Devesh Khandelwal, Thomas B. L. Kirkwood, Victor Kofia, Benjamin L. Kovitz, Bryan Kowal, Andreas Kremling, Ursula Kummer, Hiroyuki Kuwahara, Anuradha Lakshminarayana, Nicolas Le Nov?re, Thomas S. Ligon, Adrian Lopez, Timo Lubitz, Peter Lyster, Natalia Maltsev, Jakob Matthes, Joanne Matthews, Tommaso Mazza, Eric Minch, Sebastian Nagel, Maki Nakayama, Poul M. F. Nielsen, German Nudelman, Anika Oellrich, Nobuyuki Ohta, Michel Page, Victoria Petri, Ranjit Randhawa, Veerasamy Ravichandran, Elisabeth Remy, Isabel Rojas, Ursula Rost, Jan D. Rudolph, Takayuki Saito, Takeshi Sakurada, Howard Salis, Maria J. Schilstra, Marvin Schulz, Shalin Shah, Daryl Shanley, Tom Shimizu, Jacky Snoep, Hugh D. Spence, Yves Sucaet, Linda Taddeo, Jose Juan Tapia, Alex Thomas, Jannis Uhlendorf, Martijn P. van Iersel, Marc Vass, Jonathan Webb, Katja Wengler, Benjamin Wicks, Sarala Wimalaratne, Haoran Yu, Thomas J. Zajac, W. Jim Zheng, and Jason Zwolak. The principal authors thank many funding agencies for their support of this work. F.B., A.D., M.H., T.M.H., S.M.K., B.O., and L.S., as well as SBML.org and its online resources, were supported by the National Institute of General Medical Sciences (NIGMS, US), grant R01-GM070923 (PI: Hucka). In addition, F.B. has been supported by the Bundesministerium f?r Bildung und Forschung (BMBF, DE), grant de.NBI ModSim1, 031L0104A (PI: Ursula Kummer). M.L.B. has been supported by NIH (US) grant P41-GM103313 and R01-GM095485. A.D. has been supported by infrastructural funding from the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), Cluster of Excellence EXC 2124 Controlling Microbes to Fight Infections, and by the German Center for Infection Research (DZIF). A.F. was supported by the Grant-in-Aid for Young Scientists (B), grant 21700328 from JSPS KAKENHI (JP) to Keio University. J.F. was supported by National Institutes of Health (NIH, US) grant P41-GM103712 to the National Center for Multiscale Modeling of Biological Systems (MMBioS). H.H. was supported by the Biotechnology and Biological Sciences Research Council (BBSRC, UK) ?MultiMod? project (grant BB/N019482/1). T.H. was supported by NIH (US) grant 5R35-GM119770-03 to the University of Nebraska?Lincoln. S.H. was supported by NIGMS (US) grant R01-GM080219. M.K. was supported by the Federal Ministry of Education and Research (BMBF, DE), research network Systems Medicine of the Liver (LiSyM), grant 031L0054, Humboldt-University Berlin (PI: K?nig). A.L. was supported by the BBSRC (UK) while working at the Centre for Integrated Systems Biology of Ageing and Nutrition (CISBAN), Newcastle University. C.M. was supported by the National Science Foundation (NSF, USA) under grant CCF-1748200 and CCF-1856740. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the NSF. I.M. was supported by NIH grant P41-EB023912 and P41-GM103313. K.R. was supported by the Department of Biotechnology, Government of India (grant BT/PR4949/BRB/10/1048/2012). M.M.-S. was supported by the Intramural Research Program of NIAID, NIH (US). R.M.-S. was supported by the BBSRC (UK) ?MultiMod? project (grant BB/N019482/1). B.P.'s was supported by NIH (US) grant GM57089 to the University of California, San Diego, and by the Novo Nordisk Foundation Grant NNF10CC1016517. H.M.S. was supported by NIGMS (US) grant R01-GM123032 (PI: Sauro) and by the National Institute of Biomedical Imaging and Bioengineering (NIBIB, US) grant P41-EB023912 (PI: Sauro). J.C.S. was supported by NIGMS (US) grant P41-GM103313. M.S. was supported by the DDMoRe program (EU), Innovative Medicines Initiative Joint Undertaking under grant agreement 115156. N.S. was supported by BBSRC (UK) grant ?Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM) ?, grant BB/M017702/1 (PI: Nigel S. Scrutton). F.Z. was supported by the Intramural Research Program of NIAID, NIH (US). We also thank the Google Summer of Code program for support of SBML software development. B.A.'s work on SBML Level 3 was supported by the National Institutes of Health (NIH, US) and he is currently employed by AstraZeneca. G.B. was supported by National Resource for Network Biology (NRNB, US), award 5P41-GM103504. M.L.B. was supported by grant P41-GM103313 and R01-GM095485 from the NIH (US) to the University of Connecticut School of Medicine. J.?. was supported by the Czech Research Infrastructure Infrastructure for Systems Biology C4SYS, project LM2015055; GA ?R, grant 18-24397S; and the MEYS of the Czech Republic under OP RDE grant CZ.02.1.01/0.0/0.0/16_026/0008413 ?Strategic Partnership for Environmental Technologies and Energy Production?. D.D. was supported by grant iLite (Innovations for LIver and Tissue Engineering) from Agence nationale de la recherche (ANR, France) to INRIA, Paris, France; by grant LiSyM (Liver Systems Medicine) from Bundesministerium f?r Bildung und Forschung (BMBF, Germany) to IfADo, Dortmund, Germany; by grant VLN (Virtual Liver Network) from Bundesministerium f?r Bildung und Forschung (BMBF, Germany) to IfADo, Dortmund, Germany; by grant Physicancer from Institut national de la sant? et de la recherche m?dicale (INSERM, France) to INRIA, Paris, France; and by grant NOTOX (Predicting long-term toxic effects using computer models based on systems characterisations of organotypic cultures) from EU 7th Framework Programme to INRIA, Paris, France. C.E. was supported by CHARME, COST, Action CA15110 MoU (PI: Chris Evelo) to Maastricht University. R.F. was supported by the U.S. Department of Energy, Offices of Advanced Scientific Computing Research and the Biological and Environmental Research as part of the Scientific Discovery Through Advanced Computing program, grant DE-SC0010429. A.G. was supported by the Japanese Society for the Promotion of Science. M.G. was supported by funding to HITS from the German Federal Ministry of Education and Research (BMBF) as part of the Liver Systems Medicine Network LiSyM (FKZ 031L0056) and through the MulticellML project (FKZ 01ZX1707B), as well as from the European Union Horizon2020 framework programme of the European Commission under Grant Agreement 825843 and from the Klaus Tschira Foundation (KTS). M.G.G. was supported by European Molecular Biology Laboratory (EMBL) core funding. W.S.H. was supported by grant R01-GM111510 from the National Institute of General Medical Sciences (NIGMS, US) of the NIH. D.K. was supported by Novo Nordisk Foundation grant NNF10CC1016517. E.K. was supported by Deutsche Forschungsgemeinschaft (DFG, GRK1772, GRK 2290 and TRR175) and by People Programme (Marie Sk?odowska-Curie Actions H2020-MSCA-ITN-2015-675585). F.K. was supported by the Russian Foundation for Basic Research, 17-00-00296, Institute of Computational Technologies of SB RAS, Novosibirsk, 630090, Russian Federation (PI: Fedor Kolpakov). J.K. was supported by grants R35-GM119771 and P41-EB023912 from the NIH (US) to the Icahn School of Medicine. P.K. was supported by NIH grant GM075742 from the NIH (US) to SRI International. S.K. was supported by grant 031L104B (de.NBI partner project ?ModSim?) by the German Federal Ministry of Education and Research to MPI Magdeburg. L.M.L. was supported by grant P41-GM103313 from the NIH (US) to the University of Connecticut School of Medicine. P.M. was supported by grant GM080219 from the NIH (US). P.T.M was supported by grant PTDC/EEI-CTP/2914/2014 from Funda??o para a Ci?ncia e a Tecnologia (FCT) to INESC-ID. A.P.'s contribution to this manuscript is a follow-up of the work done while employed at Virginia Tech. (VA, US). The opinions expressed in this manuscript are the authors? own and do not reflect the view of the National Institutes of Health, the Department of Health and Human Services, or the United States Government. S.C.S. was supported by grant U19-AI117873 from the NIH (US).

Funding Information:
G.B. was supported by National Resource for Network Biology (NRNB, US), award 5P41‐GM103504.

Funding Information:
S.C.S. was supported by grant U19‐AI117873 from the NIH (US).

Funding Information:
J.Č. was supported by the Czech Research Infrastructure Infrastructure for Systems Biology C4SYS, project LM2015055; GA ČR, grant 18‐24397S; and the MEYS of the Czech Republic under OP RDE grant CZ.02.1.01/0.0/0.0/16_026/0008413 “Strategic Partnership for Environmental Technologies and Energy Production”.

Funding Information:
J.K. was supported by grants R35‐GM119771 and P41‐EB023912 from the NIH (US) to the Icahn School of Medicine.

Funding Information:
P.K. was supported by NIH grant GM075742 from the NIH (US) to SRI International.

Funding Information:
P.T.M was supported by grant PTDC/EEI‐CTP/2914/2014 from Fundação para a Ciência e a Tecnologia (FCT) to INESC‐ID.

Funding Information:
P.M. was supported by grant GM080219 from the NIH (US).

Funding Information:
R.F. was supported by the U.S. Department of Energy, Offices of Advanced Scientific Computing Research and the Biological and Environmental Research as part of the Scientific Discovery Through Advanced Computing program, grant DE‐SC0010429.

Funding Information:
S.K. was supported by grant 031L104B (de.NBI partner project “ModSim”) by the German Federal Ministry of Education and Research to MPI Magdeburg.

Funding Information:
M.G.G. was supported by European Molecular Biology Laboratory (EMBL) core funding.

Publisher Copyright:
© 2020 California Institute of Technology Published under the terms of the CC BY 4.0 license

Other keywords

  • computational modeling
  • file format
  • interoperability
  • reproducibility
  • systems biology

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