SBML Level 3: an extensible format for the exchange and reuse of biological models - Inria - Institut national de recherche en sciences et technologies du numérique
Journal Articles (Review Article) Molecular Systems Biology Year : 2020

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

Sarah Keating (1) , Frank Bergmann (2) , Tomáš Helikar (3) , Rahuman Malik‐sheriff (4) , Ion Moraru (5) , Martin Meier‐schellersheim (6) , Richard Adams (7) , Nicholas Allen (8) , Bastian Angermann (6) , Marco Antoniotti (9) , Gary D. Bader (10) , Jan Červený (11) , Mélanie Courtot (12) , Chris Cox (13) , Piero Dalle Pezze (14) , Emek Demir (15) , William Denney (16) , Harish Dharuri (17) , Julien Dorier (18) , Dirk Drasdo (19) , Ali Ebrahim (20) , Johannes Eichner (21) , Johan Elf (22) , Lukas Endler (23) , Chris Evelo (24) , Christoph Flamm (25) , Ronan Mt Fleming (26) , Martina Fröhlich (27) , Mihai Glont (1) , Emanuel Gonçalves (1) , Martin Golebiewski (28) , Hovakim Grabski (29) , Alex Gutteridge (30) , Damon Hachmeister (31) , Leonard Harris (32) , Benjamin Heavner (33) , Ron Henkel (34) , William Hlavacek (35) , Bin Hu (36) , Daniel Hyduke (37) , Hidde De Jong (38) , Nick Juty (1) , Peter Karp (39) , Jonathan Karr (40) , Douglas Kell (41) , Roland Keller (42) , Ilya Kiselev (43) , Steffen Klamt (44) , Edda Klipp (45) , Christian Knüpfer (46) , Fedor Kolpakov (43) , Falko Krause (47) , Martina Kutmon (24) , Camille Laibe (1) , Conor Lawless (48) , Lu Li (49) , Leslie Loew (5) , Rainer Machne (50) , Yukiko Matsuoka (51) , Pedro S. F. Mendes (52) , Huaiyu Mi (53) , Florian Mittag (54) , Pedro Monteiro (55) , Kedar Nath Natarajan (56) , Poul Mf Nielsen (57) , Tramy Nguyen (58) , Alida Palmisano (59) , Jean‐baptiste Pettit (1) , Thomas Pfau (60) , Robert Phair (61) , Tomas Radivoyevitch (62) , Johann Rohwer (63) , Oliver Ruebenacker (64) , Julio Saez‐rodriguez (65) , Martin Scharm (34) , Henning Schmidt (34) , Falk Schreiber (66) , Michael Schubert (67) , Roman Schulte (54) , Stuart Sealfon (68) , Kieran Smallbone (69) , Sylvain Soliman (70) , Melanie I. Stefan (71) , Devin Sullivan (72) , Koichi Takahashi (73) , Bas Teusink (74) , David Tolnay (71) , Ibrahim Vazirabad (75) , Axel Kamp (44) , Ulrike Wittig (28) , Clemens Wrzodek (42) , Dagmar Waltemath (76) , Finja Wrzodek (42) , Ioannis Xenarios (18) , Anna Zhukova (77) , Jeremy Zucker (78) , Matthias König (47) , Fengkai Zhang (79) , Andreas Dräger (80) , Claudine Chaouiya (55) , Frank T Bergmann (65) , Andrew Finney (81) , Colin S. Gillespie (48) , Stefan Hoops (82) , Rahuman S Malik-Sheriff (4) , Stuart L Moodie (83) , Chris Myers (58) , Aurélien Naldi (84) , Brett G Olivier (85) , Sven Sahle (86) , James C Schaff (87) , Lucian P Smith (33) , Maciej J Swat (88) , Denis Thieffry (89) , Leandro Watanabe (90) , Darren Wilkinson (48) , Michael L Blinov (5) , Kimberly Begley (71) , James R Faeder (91) , Harold F Gómez (92) , Thomas M Hamm (42) , Yuichiro Inagaki (93) , Wolfram Liebermeister (94) , Allyson L Lister (95) , Daniel Lucio (96) , Eric Mjolsness (97) , Carole Proctor (48) , Karthik Raman (98) , Nicolas Rodriguez (14) , Clifford A Shaffer (59) , Bruce Shapiro (99) , Joerg Stelling (92) , Neil Swainston (100) , Naoki Tanimura (93) , John Wagner (101) , Martin Meier-Schellersheim (79) , Herbert M Sauro (33) , Bernhard O Palsson (80) , Hamid Bolouri (102) , Hiroaki Kitano (103) , Akira Funahashi (104) , Henning Hermjakob (1) , John C Doyle (71) , Michael Hucka (105)
1 EMBL-EBI - European Bioinformatics Institute [Hinxton]
2 Heidelberg University Hospital [Heidelberg]
3 SIB - Swiss Institute of Bioinformatics [Lausanne]
4 EMBL - European Molecular Biology Laboratory
5 UCONN - University of Connecticut
6 NIH - National Institutes of Health [Bethesda, MD, USA]
7 Chercheur indépendant
8 AWS - Amazon Web Services [Seattle]
9 UNIMIB - Università degli Studi di Milano-Bicocca = University of Milano-Bicocca
10 University of Toronto
11 MUNI - Masaryk University [Brno]
12 Terry Fox Laboratory
13 The University of Tennessee [Knoxville]
14 The Babraham Institute [Cambridge, UK]
15 OHSU - Oregon Health and Science University [Portland]
16 Human Predictions LLC
17 Illumina
18 Swiss-Prot Group
19 MAMBA - Modelling and Analysis for Medical and Biological Applications
20 UC San Diego - University of California [San Diego]
21 Center for Bioinformatics (ZBIT)
22 Uppsala University
23 Institut für Populationsgenetik [Vienna]
24 Maastricht University [Maastricht]
25 Alpen-Adria-Universität Klagenfurt [Klagenfurt, Austria]
26 Medizinische Universität Wien = Medical University of Vienna
27 DKFZ - German Cancer Research Center - Deutsches Krebsforschungszentrum [Heidelberg]
28 HITS - Heidelberg Institute for Theoretical Studies
29 RAU - Russian-Armenian University
30 GSK - GlaxoSmithKline [Stevenage, UK]
31 MTL - Microsoft Technology Licensing
32 Vanderbilt University School of Medicine [Nashville]
33 University of Washington [Seattle]
34 University of Rostock
35 LANL - Los Alamos National Laboratory
36 Lorentz Institute
37 Tegmine Therapeutics
38 IBIS - Modeling, simulation, measurement, and control of bacterial regulatory networks
39 SRI - SRI International [Menlo Park]
40 MSSM - Icahn School of Medicine at Mount Sinai [New York]
41 University of Liverpool
42 Universitätsklinikum Tübingen - University Hospital of Tübingen
43 IICT - Institute of Information and Computational Technologies
44 Max Planck Institute for Dynamics of Complex Technical Systems
45 MPIMG - Max-Planck-Institut für Molekulare Genetik
46 Friedrich-Schiller-Universität = Friedrich Schiller University Jena [Jena, Germany]
47 HU Berlin - Humboldt-Universität zu Berlin = Humboldt University of Berlin = Université Humboldt de Berlin
48 Newcastle University [Newcastle]
49 X - École polytechnique
50 Heinrich Heine Universität Düsseldorf = Heinrich Heine University [Düsseldorf]
51 SBI - The Systems Biology Institute [Tokyo]
52 CQE - Centro de Quimica Estrutural
53 USC - University of Southern California
54 Eberhard Karls Universität Tübingen = University of Tübingen
55 IGC - Instituto Gulbenkian de Ciência [Oeiras]
56 SDU - University of Southern Denmark
57 University of Auckland [Auckland]
58 University of Utah
59 Virginia Tech [Blacksburg]
60 uni.lu - Université du Luxembourg = University of Luxembourg = Universität Luxemburg
61 Integrative Bioinformatics Inc [Mountain View]
62 Cleveland Clinic
63 Stellenbosch University
64 BROAD INSTITUTE - Broad Institute of MIT and Harvard
65 Universität Heidelberg [Heidelberg] = Heidelberg University
66 IPK-Gatersleben - Leibniz Institute of Plant Genetics and Crop Plant Research [Gatersleben]
67 LBDV - Laboratoire de Biologie du Développement de Villefranche sur mer
68 Mount Sinai School of Medicine
69 University of Manchester [Manchester]
70 Lifeware - Computational systems biology and optimization
71 CALTECH - California Institute of Technology
72 Encodia Inc [San Diego]
73 Shinshu University [Nagano]
74 UvA - University of Amsterdam [Amsterdam] = Universiteit van Amsterdam
75 Versiti Blood Center of Wisconsin
76 Greifswald University Hospital
77 Bioinformatique évolutive - Evolutionary Bioinformatics
78 PNNL - Pacific Northwest National Laboratory
79 NIAID-NIH - National Institute of Allergy and Infectious Diseases [Bethesda]
80 Department of Bioengineering
81 ANSYS
82 Virginia Polytechnic Institute and State University [Blacksburg]
83 Eight Pillars Ltd
84 CIG - Center for Integrative Genomics - Institute of Bioinformatics, Génopode
85 Universität Heidelberg
86 Bioquant
87 Applied Biomathematics [New York]
88 SimCYP Ltd
89 IBENS - Institut de biologie de l'ENS Paris
90 School of Medicine [University of Utah, Salt Lake City]
91 University of Pittsburgh School of Medicine
92 ETH Zürich - Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich]
93 Mizuho Information and Research Institute
94 MaIAGE - Mathématiques et Informatique Appliquées du Génome à l'Environnement [Jouy-En-Josas]
95 University of Oxford
96 North Carolina State University - Computer Science
97 UC Irvine - University of California [Irvine]
98 IIT Madras - Indian Institute of Technology Madras
99 CSUN - California State University [Northridge]
100 BBSRC - Biotechnology and Biological Sciences Research Council
101 IBM Research [Melbourne]
102 BRI - Benaroya Research Institute [Seattle]
103 Okinawa Institute of Science and Technology Graduate University
104 Keio University [Tokyo]
105 Department of Computing and Mathematical sciences
Sarah Keating
  • Function : Author
  • PersonId : 950706
Ion Moraru
Harish Dharuri
  • Function : Author
Julien Dorier
  • Function : Author
  • PersonId : 950714
Johan Elf
  • Function : Author
Chris Evelo
Ron Henkel
  • Function : Author
Bin Hu
Martina Kutmon
Lu Li
  • Function : Author
  • PersonId : 4846
  • IdHAL : lu-li-loa
Pedro Monteiro
  • Function : Author
  • PersonId : 950715
Tramy Nguyen
  • Function : Author
Johann Rohwer
Kieran Smallbone
  • Function : Author
  • PersonId : 888747
Koichi Takahashi
Dagmar Waltemath
Ioannis Xenarios
  • Function : Author
  • PersonId : 950717
Jeremy Zucker
Andreas Dräger
  • Function : Author
  • PersonId : 950709
Andrew Finney
  • Function : Author
Chris Myers
  • Function : Author
  • PersonId : 959819
Sven Sahle
  • Function : Author
Maciej J Swat
  • Function : Author
Denis Thieffry
  • Function : Author
  • PersonId : 926019
Daniel Lucio
  • Function : Author
Akira Funahashi

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 multi-scale 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 provides the foundation needed to support this evolution. 3
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hal-02924909 , version 1 (28-08-2020)

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Sarah Keating, Frank Bergmann, Tomáš Helikar, Rahuman Malik‐sheriff, Ion Moraru, et al.. SBML Level 3: an extensible format for the exchange and reuse of biological models. Molecular Systems Biology, 2020, 16 (8), pp.1-21. ⟨10.15252/msb.20199110⟩. ⟨hal-02924909⟩
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