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

Author

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 Smith, Maciej 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, John C Doyle, Michael Hucka, SBML Level 3 Community members, Richard R Adams, Nicholas A Allen, Bastian R Angermann, Marco Antoniotti, Gary D Bader, Jan Červený, Mélanie Courtot, Chris D Cox, Piero Dalle Pezze, Emek Demir, William S Denney, Harish Dharuri, Julien Dorier, Dirk Drasdo, Ali Ebrahim, Johannes Eichner, Johan Elf, Lukas Endler, Chris T Evelo, Christoph Flamm, Ronan MT Fleming, Martina Fröhlich, Mihai Glont, Emanuel Gonçalves, Martin Golebiewski, Hovakim Grabski, Alex Gutteridge, Damon Hachmeister, Leonard A Harris, Benjamin D Heavner, Ron Henkel, William S Hlavacek, Bin Hu, Daniel R Hyduke, Hidde de Jong, Nick Juty, Peter D Karp, Jonathan R Karr, Douglas B Kell, Roland Keller, Ilya Kiselev, Steffen Klamt, Edda Klipp, Christian Knüpfer, Fedor Kolpakov, Falko Krause, Martina Kutmon, Camille Laibe, Conor Lawless, Lu Li, Leslie M Loew, Rainer Machne, Yukiko Matsuoka, Pedro Mendes, Huaiyu Mi, Florian Mittag, Pedro T Monteiro, Kedar Nath Natarajan, Poul MF Nielsen, Tramy Nguyen, Alida Palmisano, Jean‐Baptiste Pettit, Thomas Pfau, Robert D Phair, Tomas Radivoyevitch, Johann M Rohwer, Oliver A Ruebenacker, Julio Saez‐Rodriguez, Martin Scharm, Henning Schmidt, Falk Schreiber, Michael Schubert, Roman Schulte, Stuart C Sealfon, Kieran Smallbone, Sylvain Soliman, Melanie I Stefan, Devin P Sullivan, Koichi Takahashi, Bas Teusink, David Tolnay, Ibrahim Vazirabad, Axel von Kamp, Ulrike Wittig, Clemens Wrzodek, Finja Wrzodek, Ioannis Xenarios, Anna Zhukova, and Jeremy Zucker

Subjects
computational modeling, file format, interoperability, reproducibility, systems biology, Biology (General), QH301-705.5, Medicine (General), R5-920
Abstract

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.

Published
2020
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2. Influence of Feature Encoding and Choice of Classifier on Disease Risk Prediction in Genome-Wide Association Studies.

Author

Florian Mittag, Michael Römer, and Andreas Zell

Subjects
Medicine, Science
Abstract

Various attempts have been made to predict the individual disease risk based on genotype data from genome-wide association studies (GWAS). However, most studies only investigated one or two classification algorithms and feature encoding schemes. In this study, we applied seven different classification algorithms on GWAS case-control data sets for seven different diseases to create models for disease risk prediction. Further, we used three different encoding schemes for the genotypes of single nucleotide polymorphisms (SNPs) and investigated their influence on the predictive performance of these models. Our study suggests that an additive encoding of the SNP data should be the preferred encoding scheme, as it proved to yield the best predictive performances for all algorithms and data sets. Furthermore, our results showed that the differences between most state-of-the-art classification algorithms are not statistically significant. Consequently, we recommend to prefer algorithms with simple models like the linear support vector machine (SVM) as they allow for better subsequent interpretation without significant loss of accuracy.

Published
2015
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3. Integrative pathway-based approach for genome-wide association studies: identification of new pathways for rheumatoid arthritis and type 1 diabetes.

Author

Finja Büchel, Florian Mittag, Clemens Wrzodek, Andreas Zell, Thomas Gasser, and Manu Sharma

Subjects
Medicine, Science
Abstract

Genome-wide association studies (GWAS) led to the identification of numerous novel loci for a number of complex diseases. Pathway-based approaches using genotypic data provide tangible leads which cannot be identified by single marker approaches as implemented in GWAS. The available pathway analysis approaches mainly differ in the employed databases and in the applied statistics for determining the significance of the associated disease markers. So far, pathway-based approaches using GWAS data failed to consider the overlapping of genes among different pathways or the influence of protein-interactions. We performed a multistage integrative pathway (MIP) analysis on three common diseases--Crohn's disease (CD), rheumatoid arthritis (RA) and type 1 diabetes (T1D)--incorporating genotypic, pathway, protein- and domain-interaction data to identify novel associations between these diseases and pathways. Additionally, we assessed the sensitivity of our method by studying the influence of the most significant SNPs on the pathway analysis by removing those and comparing the corresponding pathway analysis results. Apart from confirming many previously published associations between pathways and RA, CD and T1D, our MIP approach was able to identify three new associations between disease phenotypes and pathways. This includes a relation between the influenza-A pathway and RA, as well as a relation between T1D and the phagosome and toxoplasmosis pathways. These results provide new leads to understand the molecular underpinnings of these diseases. The developed software herein used is available at http://www.cogsys.cs.uni-tuebingen.de/software/GWASPathwayIdentifier/index.htm.

Published
2013
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4. Linking the epigenome to the genome: correlation of different features to DNA methylation of CpG islands.

Author

Clemens Wrzodek, Finja Büchel, Georg Hinselmann, Johannes Eichner, Florian Mittag, and Andreas Zell

Subjects
Medicine, Science
Abstract

DNA methylation of CpG islands plays a crucial role in the regulation of gene expression. More than half of all human promoters contain CpG islands with a tissue-specific methylation pattern in differentiated cells. Still today, the whole process of how DNA methyltransferases determine which region should be methylated is not completely revealed. There are many hypotheses of which genomic features are correlated to the epigenome that have not yet been evaluated. Furthermore, many explorative approaches of measuring DNA methylation are limited to a subset of the genome and thus, cannot be employed, e.g., for genome-wide biomarker prediction methods. In this study, we evaluated the correlation of genetic, epigenetic and hypothesis-driven features to DNA methylation of CpG islands. To this end, various binary classifiers were trained and evaluated by cross-validation on a dataset comprising DNA methylation data for 190 CpG islands in HEPG2, HEK293, fibroblasts and leukocytes. We achieved an accuracy of up to 91% with an MCC of 0.8 using ten-fold cross-validation and ten repetitions. With these models, we extended the existing dataset to the whole genome and thus, predicted the methylation landscape for the given cell types. The method used for these predictions is also validated on another external whole-genome dataset. Our results reveal features correlated to DNA methylation and confirm or disprove various hypotheses of DNA methylation related features. This study confirms correlations between DNA methylation and histone modifications, DNA structure, DNA sequence, genomic attributes and CpG island properties. Furthermore, the method has been validated on a genome-wide dataset from the ENCODE consortium. The developed software, as well as the predicted datasets and a web-service to compare methylation states of CpG islands are available at http://www.cogsys.cs.uni-tuebingen.de/software/dna-methylation/.

Published
2012
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8. JSBML 1.0: providing a smorgasbord of options to encode systems biology models.

Author

Nicolas Rodriguez 0001, Alex Thomas, Leandro H. Watanabe, Ibrahim Y. Vazirabad, Victor Kofia, Harold F. Gómez, Florian Mittag, Jakob Matthes, Jan Rudolph, Finja Wrzodek, Eugen Netz, Alexander Diamantikos, Johannes Eichner, Roland Keller, Clemens Wrzodek, Sebastian Fröhlich, Nathan E. Lewis, Chris J. Myers, Nicolas Le Novère, Bernhard ø. Palsson, Michael Hucka, and Andreas Dräger

Published
2015
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12. SBML Level 3: an extensible format for the exchange and reuse of biological models

Author

Edda Klipp, Marco Antoniotti, Frank Bergmann, James C. Schaff, Peter D. Karp, Daniel Lucio, Kedar Nath Natarajan, Thomas M. Hamm, Leandro Watanabe, Henning Hermjakob, David Tolnay, John Wagner, Joerg Stelling, Alida Palmisano, Falk Schreiber, Yukiko Matsuoka, Harold F. Gómez, Huaiyu Mi, Carole J. Proctor, Ulrike Wittig, Neil Swainston, Jan Červený, Denis Thieffry, Piero Dalle Pezze, Julio Saez-Rodriguez, Maciej J. Swat, Bin Hu, Martina Kutmon, Thomas Pfau, Bas Teusink, Sarah M. Keating, Fedor A. Kolpakov, Andreas Dräger, Pedro Mendes, Martin Scharm, Emek Demir, Ioannis Xenarios, Christoph Flamm, Axel von Kamp, Darren J. Wilkinson, Nick Juty, Fengkai Zhang, Leonard A. Harris, Michael Schubert, Dagmar Waltemath, Lucian P. Smith, Steffen Klamt, Herbert M. Sauro, Ali Ebrahim, Wolfram Liebermeister, Christian Knüpfer, Nicolas Rodriguez, Tramy Nguyen, Naoki Tanimura, Christopher Cox, Stuart C. Sealfon, Nicholas Alexander Allen, Clemens Wrzodek, Bastian R. Angermann, Martin Meier-Schellersheim, Anna Zhukova, Jean-Baptiste Pettit, Hovakim Grabski, Devin P. Sullivan, Claudine Chaouiya, Michael L. Blinov, John Doyle, Ilya Kiselev, Roman Schulte, Alex Gutteridge, Mélanie Courtot, Eric Mjolsness, Finja Wrzodek, Rahuman S Malik-Sheriff, Ronan M. T. Fleming, Bruce E. Shapiro, Kimberly Begley, Leslie M. Loew, Colin S. Gillespie, Ibrahim Vazirabad, Michael Hucka, Akira Funahashi, Bernhard O. Palsson, Hamid Bolouri, Tomáš Helikar, Camille Laibe, William S. Denney, Chris T. Evelo, Florian Mittag, William S. Hlavacek, Ron Henkel, Harish Dharuri, Julien Dorier, Karthik Raman, Martina Fröhlich, Conor Lawless, Rainer Machné, Falko Krause, Damon Hachmeister, Matthias König, Clifford A. Shaffer, Benjamin D. Heavner, Douglas B. Kell, Jonathan R. Karr, Mihai Glont, Lukas Endler, Melanie I. Stefan, Robert Phair, Lu Li, Henning Schmidt, Dirk Drasdo, Johan Elf, Allyson L. Lister, Hiroaki Kitano, Richard R. Adams, Oliver A. Ruebenacker, Roland Keller, Sven Sahle, Ion I. Moraru, Gary D. Bader, Poul M. F. Nielsen, Johann M. Rohwer, Johannes Eichner, Daniel R. Hyduke, James R. Faeder, Stefan Hoops, Emanuel Gonçalves, Yuichiro Inagaki, Aurélien Naldi, Koichi Takahashi, Sylvain Soliman, Brett G. Olivier, Kieran Smallbone, Stuart L. Moodie, Pedro T. Monteiro, Chris J. Myers, Martin Golebiewski, Tomas Radivoyevitch, Jeremy Zucker, Hidde de Jong, Andrew Finney, Keating, S, Waltemath, D, König, M, Zhang, F, Dräger, A, Chaouiya, C, Bergmann, F, Finney, A, Gillespie, C, Helikar, T, Hoops, S, Malik-Sheriff, R, Moodie, S, Moraru, I, Myers, C, Naldi, A, Olivier, B, Sahle, S, Schaff, J, Smith, L, Swat, M, Thieffry, D, Watanabe, L, Wilkinson, D, Blinov, M, Begley, K, Faeder, J, Gómez, H, Hamm, T, Inagaki, Y, Liebermeister, W, Lister, A, Lucio, D, Mjolsness, E, Proctor, C, Raman, K, Rodriguez, N, Shaffer, C, Shapiro, B, Stelling, J, Swainston, N, Tanimura, N, Wagner, J, Meier-Schellersheim, M, Sauro, H, Palsson, B, Bolouri, H, Kitano, H, Funahashi, A, Hermjakob, H, Doyle, J, Hucka, M, Adams, R, Allen, N, Angermann, B, Antoniotti, M, Bader, G, Červený, J, Courtot, M, Cox, C, Dalle Pezze, P, Demir, E, Denney, W, Dharuri, H, Dorier, J, Drasdo, D, Ebrahim, A, Eichner, J, Elf, J, Endler, L, Evelo, C, Flamm, C, Fleming, R, Fröhlich, M, Glont, M, Gonçalves, E, Golebiewski, M, Grabski, H, Gutteridge, A, Hachmeister, D, Harris, L, Heavner, B, Henkel, R, Hlavacek, W, Hu, B, Hyduke, D, Jong, H, Juty, N, Karp, P, Karr, J, Kell, D, Keller, R, Kiselev, I, Klamt, S, Klipp, E, Knüpfer, C, Kolpakov, F, Krause, F, Kutmon, M, Laibe, C, Lawless, C, Li, L, Loew, L, Machne, R, Matsuoka, Y, Mendes, P, Mi, H, Mittag, F, Monteiro, P, Natarajan, K, Nielsen, P, Nguyen, T, Palmisano, A, Jean-Baptiste, P, Pfau, T, Phair, R, Radivoyevitch, T, Rohwer, J, Ruebenacker, O, Saez-Rodriguez, J, Scharm, M, Schmidt, H, Schreiber, F, Schubert, M, Schulte, R, Sealfon, S, Smallbone, K, Soliman, S, Stefan, M, Sullivan, D, Takahashi, K, Teusink, B, Tolnay, D, Vazirabad, I, Kamp, A, Wittig, U, Wrzodek, C, Wrzodek, F, Xenarios, I, Zhukova, A, Zucker, J, European Bioinformatics Institute [Hinxton] (EMBL-EBI), EMBL Heidelberg, Heidelberg University Hospital [Heidelberg], Swiss Institute of Bioinformatics [Lausanne] (SIB), Université de Lausanne = University of Lausanne (UNIL), European Molecular Biology Laboratory (EMBL), University of Connecticut (UCONN), National Institutes of Health [Bethesda] (NIH), Chercheur indépendant, Amazon Web Services [Seattle] (AWS), Università degli Studi di Milano-Bicocca = University of Milano-Bicocca (UNIMIB), University of Toronto, Masaryk University [Brno] (MUNI), Terry Fox Laboratory, BC Cancer Agency (BCCRC)-British Columbia Cancer Agency Research Centre, The University of Tennessee [Knoxville], The Babraham Institute [Cambridge, UK], Oregon Health and Science University [Portland] (OHSU), Human Predictions LLC, Illumina, Swiss-Prot Group, Swiss Institute of Bioinformatics [Genève] (SIB), Modelling and Analysis for Medical and Biological Applications (MAMBA), Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jacques-Louis Lions (LJLL (UMR_7598)), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), University of California [San Diego] (UC San Diego), University of California (UC), Center for Bioinformatics (ZBIT), Eberhard Karls Universität Tübingen = Eberhard Karls University of Tuebingen, Uppsala University, Institut für Populationsgenetik [Vienna], Veterinärmedizinische Universität Wien, Maastricht University [Maastricht], Alpen-Adria-Universität Klagenfurt [Klagenfurt, Austria], Medizinische Universität Wien = Medical University of Vienna, German Cancer Research Center - Deutsches Krebsforschungszentrum [Heidelberg] (DKFZ), Heidelberg Institute for Theoretical Studies (HITS ), Russian-Armenian University (RAU), GlaxoSmithKline [Stevenage, UK] (GSK), GlaxoSmithKline [Headquarters, London, UK] (GSK), Microsoft Technology Licensing (MTL), Microsoft Corporation [Redmond, Wash.], Vanderbilt University School of Medicine [Nashville], University of Washington [Seattle], University of Rostock, Los Alamos National Laboratory (LANL), Lorentz Institute, Universiteit Leiden, Tegmine Therapeutics, Modeling, simulation, measurement, and control of bacterial regulatory networks (IBIS), Laboratoire Adaptation et pathogénie des micro-organismes [Grenoble] (LAPM), Université Joseph Fourier - Grenoble 1 (UJF)-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Centre National de la Recherche Scientifique (CNRS)-Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut Jean Roget, SRI International [Menlo Park] (SRI), Icahn School of Medicine at Mount Sinai [New York] (MSSM), University of Liverpool, Universitätsklinikum Tübingen - University Hospital of Tübingen, Institute of Information and Computational Technologies (IICT), Max Planck Institute for Dynamics of Complex Technical Systems, Max-Planck-Gesellschaft, Max-Planck-Institut für Molekulare Genetik (MPIMG), Friedrich-Schiller-Universität = Friedrich Schiller University Jena [Jena, Germany], Humboldt University Of Berlin, Newcastle University [Newcastle], École polytechnique (X), Heinrich Heine Universität Düsseldorf = Heinrich Heine University [Düsseldorf], The Systems Biology Institute [Tokyo] (SBI), Centro de Quimica Estrutural (CQE), Instituto Superior Técnico, Universidade Técnica de Lisboa (IST), University of Southern California (USC), Instituto Gulbenkian de Ciência [Oeiras] (IGC), Fundação Calouste Gulbenkian, University of Southern Denmark (SDU), University of Auckland [Auckland], University of Utah, Virginia Tech [Blacksburg], University of Luxembourg [Luxembourg], Integrative Bioinformatics Inc [Mountain View], Cleveland Clinic, Stellenbosch University, Broad Institute of MIT and Harvard (BROAD INSTITUTE), Harvard Medical School [Boston] (HMS)-Massachusetts Institute of Technology (MIT)-Massachusetts General Hospital [Boston], Universität Heidelberg [Heidelberg] = Heidelberg University, Leibniz Institute of Plant Genetics and Crop Plant Research [Gatersleben] (IPK-Gatersleben), Laboratoire de Biologie du Développement de Villefranche sur mer (LBDV), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut de la Mer de Villefranche (IMEV), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Mount Sinai School of Medicine, Department of Psychiatry-Icahn School of Medicine at Mount Sinai [New York] (MSSM), University of Manchester [Manchester], Computational systems biology and optimization (Lifeware), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), California Institute of Technology (CALTECH), Encodia Inc [San Diego], Shinshu University [Nagano], University of Amsterdam [Amsterdam] (UvA), Versiti Blood Center of Wisconsin, Greifswald University Hospital, Bioinformatique évolutive - Evolutionary Bioinformatics, Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS), Pacific Northwest National Laboratory (PNNL), National Institute of Allergy and Infectious Diseases [Bethesda] (NIAID-NIH), Department of Bioengineering, University of California (UC)-University of California (UC), ANSYS, Virginia Polytechnic Institute and State University [Blacksburg], Eight Pillars Ltd, Center for Integrative Genomics - Institute of Bioinformatics, Génopode (CIG), Université de Lausanne = University of Lausanne (UNIL)-Université de Lausanne = University of Lausanne (UNIL), Universität Heidelberg, Bioquant, Applied Biomathematics [New York], SimCYP Ltd, Institut de biologie de l'ENS Paris (IBENS), Département de Biologie - ENS Paris, École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), University of Utah School of Medicine [Salt Lake City], University of Pittsburgh School of Medicine, Pennsylvania Commonwealth System of Higher Education (PCSHE), Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich] (ETH Zürich), Mizuho Information and Research Institute, Mathématiques et Informatique Appliquées du Génome à l'Environnement [Jouy-En-Josas] (MaIAGE), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), University of Oxford, Computer Science (North Carolina State University), North Carolina State University [Raleigh] (NC State), University of North Carolina System (UNC)-University of North Carolina System (UNC), University of California [Irvine] (UC Irvine), Indian Institute of Technology Madras (IIT Madras), California State University [Northridge] (CSUN), Biotechnology and Biological Sciences Research Council (BBSRC), IBM Research [Melbourne], Benaroya Research Institute [Seattle] (BRI), Okinawa Institute of Science and Technology Graduate University, Keio University, Department of Computing and Mathematical sciences, members, SBML Level 3 Community, Université de Lausanne (UNIL), Università degli Studi di Milano-Bicocca [Milano] (UNIMIB), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP), University of California, Universiteit Leiden [Leiden], Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Inria Grenoble - Rhône-Alpes, Humboldt University of Berlin, Universität Heidelberg [Heidelberg], Institut Pasteur [Paris]-Centre National de la Recherche Scientifique (CNRS), Humboldt-Universität zu Berlin, University of California-University of California, Université de Lausanne (UNIL)-Université de Lausanne (UNIL), Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Département de Biologie - ENS Paris, École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), University of Oxford [Oxford], University of California [Irvine] (UCI), Biotechnology and Biological Sciences Research Council, Computer Science, Institut de biologie de l'ENS Paris (UMR 8197/1024) (IBENS), and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS Paris)

Subjects
computational modeling, Medicine (General), Markup language, [SDV.BIO]Life Sciences [q-bio]/Biotechnology, INFORMATION, Interoperability, interoperability, Review, [SDV.BC.BC]Life Sciences [q-bio]/Cellular Biology/Subcellular Processes [q-bio.SC], ANNOTATION, 0302 clinical medicine, Software, file forma, Models, Biology (General), 0303 health sciences, Computational model, Applied Mathematics, Systems Biology, systems biology, File format, 3. Good health, Networking and Information Technology R&D, Networking and Information Technology R&D (NITRD), Computational Theory and Mathematics, SIMULATION, General Agricultural and Biological Sciences, STANDARDS, REPOSITORY, Information Systems, QH301-705.5, Bioinformatics, Systems biology, Software ecosystem, Reviews, Bioengineering, Methods & Resources, Biology, MARKUP LANGUAGE, Models, Biological, SBML Level 3 Community members, General Biochemistry, Genetics and Molecular Biology, 03 medical and health sciences, R5-920, Animals, Humans, SBML, reproducibility, 030304 developmental biology, ENVIRONMENT, General Immunology and Microbiology, file format, business.industry, Computational Biology, Biological, ONTOLOGY, Metabolism, Logistic Models, Biochemistry and Cell Biology, Other Biological Sciences, Software engineering, business, 030217 neurology & neurosurgery
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., Over the past two decades, scientists from different fields have been developing SBML, a standard format for encoding computational models in biology and medicine. This article summarizes recent progress and gives perspectives on emerging challenges.

Published
2020
Full Text
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13. SBML Level 3: an extensible format for the exchange and reuse of biological models

Author

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, Smith, Maciej 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, John C, Doyle, Michael, Hucka, Richard R, Adams, Nicholas A, Allen, Bastian R, Angermann, Marco, Antoniotti, Gary D, Bader, Jan, Červený, Mélanie, Courtot, Chris D, Cox, Piero, Dalle Pezze, Emek, Demir, William S, Denney, Harish, Dharuri, Julien, Dorier, Dirk, Drasdo, Ali, Ebrahim, Johannes, Eichner, Johan, Elf, Lukas, Endler, Chris T, Evelo, Christoph, Flamm, Ronan MT, Fleming, Martina, Fröhlich, Mihai, Glont, Emanuel, Gonçalves, Martin, Golebiewski, Hovakim, Grabski, Alex, Gutteridge, Damon, Hachmeister, Leonard A, Harris, Benjamin D, Heavner, Ron, Henkel, William S, Hlavacek, Bin, Hu, Daniel R, Hyduke, Hidde, Jong, Nick, Juty, Peter D, Karp, Jonathan R, Karr, Douglas B, Kell, Roland, Keller, Ilya, Kiselev, Steffen, Klamt, Edda, Klipp, Christian, Knüpfer, Fedor, Kolpakov, Falko, Krause, Martina, Kutmon, Camille, Laibe, Conor, Lawless, Lu, Li, Leslie M, Loew, Rainer, Machne, Yukiko, Matsuoka, Pedro, Mendes, Huaiyu, Mi, Florian, Mittag, Pedro T, Monteiro, Kedar Nath, Natarajan, Poul MF, Nielsen, Tramy, Nguyen, Alida, Palmisano, Jean‐Baptiste, Pettit, Thomas, Pfau, Robert D, Phair, Tomas, Radivoyevitch, Johann M, Rohwer, Oliver A, Ruebenacker, Julio, Saez‐Rodriguez, Martin, Scharm, Henning, Schmidt, Falk, Schreiber, Michael, Schubert, Roman, Schulte, Stuart C, Sealfon, Kieran, Smallbone, Sylvain, Soliman, Melanie I, Stefan, Devin P, Sullivan, Koichi, Takahashi, Bas, Teusink, David, Tolnay, Ibrahim, Vazirabad, Axel, Kamp, Ulrike, Wittig, Clemens, Wrzodek, Finja, Wrzodek, Ioannis, Xenarios, Takahiro G, Yamada, Anna, Zhukova, Jeremy, Zucker, 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, Smith, Maciej 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, John C, Doyle, Michael, Hucka, Richard R, Adams, Nicholas A, Allen, Bastian R, Angermann, Marco, Antoniotti, Gary D, Bader, Jan, Červený, Mélanie, Courtot, Chris D, Cox, Piero, Dalle Pezze, Emek, Demir, William S, Denney, Harish, Dharuri, Julien, Dorier, Dirk, Drasdo, Ali, Ebrahim, Johannes, Eichner, Johan, Elf, Lukas, Endler, Chris T, Evelo, Christoph, Flamm, Ronan MT, Fleming, Martina, Fröhlich, Mihai, Glont, Emanuel, Gonçalves, Martin, Golebiewski, Hovakim, Grabski, Alex, Gutteridge, Damon, Hachmeister, Leonard A, Harris, Benjamin D, Heavner, Ron, Henkel, William S, Hlavacek, Bin, Hu, Daniel R, Hyduke, Hidde, Jong, Nick, Juty, Peter D, Karp, Jonathan R, Karr, Douglas B, Kell, Roland, Keller, Ilya, Kiselev, Steffen, Klamt, Edda, Klipp, Christian, Knüpfer, Fedor, Kolpakov, Falko, Krause, Martina, Kutmon, Camille, Laibe, Conor, Lawless, Lu, Li, Leslie M, Loew, Rainer, Machne, Yukiko, Matsuoka, Pedro, Mendes, Huaiyu, Mi, Florian, Mittag, Pedro T, Monteiro, Kedar Nath, Natarajan, Poul MF, Nielsen, Tramy, Nguyen, Alida, Palmisano, Jean‐Baptiste, Pettit, Thomas, Pfau, Robert D, Phair, Tomas, Radivoyevitch, Johann M, Rohwer, Oliver A, Ruebenacker, Julio, Saez‐Rodriguez, Martin, Scharm, Henning, Schmidt, Falk, Schreiber, Michael, Schubert, Roman, Schulte, Stuart C, Sealfon, Kieran, Smallbone, Sylvain, Soliman, Melanie I, Stefan, Devin P, Sullivan, Koichi, Takahashi, Bas, Teusink, David, Tolnay, Ibrahim, Vazirabad, Axel, Kamp, Ulrike, Wittig, Clemens, Wrzodek, Finja, Wrzodek, Ioannis, Xenarios, Takahiro G, Yamada, Anna, Zhukova, and Jeremy, Zucker

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., source:https://www.embopress.org/doi/full/10.15252/msb.20199110

Published
2020

14. Path2Models: large-scale generation of computational models from biochemical pathway maps.

Author

Finja Büchel, Nicolas Rodriguez 0001, Neil Swainston, Clemens Wrzodek, Tobias Czauderna, Roland Keller, Florian Mittag, Michael Schubert, Mihai Glont, Martin Golebiewski, Martijn P. van Iersel, Sarah M. Keating, Matthias Rall, Michael Wybrow, Henning Hermjakob, Michael Hucka, Douglas B. Kell, Wolfgang Müller 0001, Pedro Mendes 0001, Andreas Zell, Claudine Chaouiya, Julio Saez-Rodriguez, Falk Schreiber, Camille Laibe, Andreas Dräger, and Nicolas Le Novère

Published
2013
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15. Qualitative translation of relations from BioPAX to SBML qual

Author

Andreas Dräger, Clemens Wrzodek, Andreas Zell, Florian Mittag, Johannes Eichner, Nicolas Rodriguez, Nicolas Le Novère, Finja Büchel, Université Pierre et Marie Curie - Paris 6 (UPMC), European Bioinformatics Institute [Hinxton] (EMBL-EBI), and EMBL Heidelberg

Subjects
Statistics and Probability, Theoretical computer science, Databases, Factual, Computer science, Systems biology, computer.software_genre, Biochemistry, Biological pathway, 03 medical and health sciences, 0302 clinical medicine, Qualitative analysis, BioPAX : Biological Pathways Exchange, Systems Biology Ontology, Humans, SBML, Molecular Biology, 030304 developmental biology, 0303 health sciences, Internet, Programming language, [CHIM.ORGA]Chemical Sciences/Organic chemistry, Systems Biology, BioModels Database, Original Papers, Computer Science Applications, Computational Mathematics, Computational Theory and Mathematics, Data exchange, 030220 oncology & carcinogenesis, Programming Languages, computer, Software
Abstract

Motivation: The biological pathway exchange language (BioPAX) and the systems biology markup language (SBML) belong to the most popular modeling and data exchange languages in systems biology. The focus of SBML is quantitative modeling and dynamic simulation of models, whereas the BioPAX specification concentrates mainly on visualization and qualitative analysis of pathway maps. BioPAX describes reactions and relations. In contrast, SBML core exclusively describes quantitative processes such as reactions. With the SBML qualitative models extension (qual), it has recently also become possible to describe relations in SBML. Before the development of SBML qual, relations could not be properly translated into SBML. Until now, there exists no BioPAX to SBML converter that is fully capable of translating both reactions and relations. Results: The entire nature pathway interaction database has been converted from BioPAX (Level 2 and Level 3) into SBML (Level 3 Version 1) including both reactions and relations by using the new qual extension package. Additionally, we present the new webtool BioPAX2SBML for further BioPAX to SBML conversions. Compared with previous conversion tools, BioPAX2SBML is more comprehensive, more robust and more exact. Availability: BioPAX2SBML is freely available at http://webservices.cs.uni-tuebingen.de/ and the complete collection of the PID models is available at http://www.cogsys.cs.uni-tuebingen.de/downloads/Qualitative-Models/. Contact: finja.buechel@uni-tuebingen.de Supplementary Information: Supplementary data are available at Bioinformatics online.

Published
2012
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16. Path2Models: large-scale generation of computational models from biochemical pathway maps

Author

Finja Büchel, Nicolas Rodriguez, Neil Swainston, Clemens Wrzodek, Tobias Czauderna, Roland Keller, Florian Mittag, Michael Schubert, Mihai Glont, Martin Golebiewski, Martijn van Iersel, Sarah Keating, Matthias Rall, Michael Wybrow, Henning Hermjakob, Michael Hucka, Douglas B Kell, Wolfgang Müller, Pedro Mendes, Andreas Zell, Claudine Chaouiya, Julio Saez-Rodriguez, Falk Schreiber, Camille Laibe, Andreas Dräger, Nicolas Le Novère

Subjects
SBGN, Modular rate law, Constraint based models, Logical models, SBML
Abstract

Background Systems biology projects and omics technologies have led to a growing number of biochemical pathway models and reconstructions. However, the majority of these models are still created de novo, based on literature mining and the manual processing of pathway data. Results To increase the efficiency of model creation, the Path2Models project has automatically generated mathematical models from pathway representations using a suite of freely available software. Data sources include KEGG, BioCarta, MetaCyc and SABIO-RK. Depending on the source data, three types of models are provided: kinetic, logical and constraint-based. Models from over 2 600 organisms are encoded consistently in SBML, and are made freely available through BioModels Database at http://www.ebi.ac.uk/biomodels-main/path2models webcite. Each model contains the list of participants, their interactions, the relevant mathematical constructs, and initial parameter values. Most models are also available as easy-to-understand graphical SBGN maps. Conclusions To date, the project has resulted in more than 140 000 freely available models. Such a resource can tremendously accelerate the development of mathematical models by providing initial starting models for simulation and analysis, which can be subsequently curated and further parameterized.

Published
2013

17. ProDGe: investigating protein-protein interactions at the domain level

Author

Finja Büchel, Clemens Wrzodek, Florian Mittag, Andreas Dräger, Adrian Schröder, and Andreas Zell

Subjects
Domain level, Bioinformatics, Systems biology, Protein domain, General Materials Science, Computational biology, UniProt, Biology, Gene, Domain (software engineering), Protein–protein interaction, Interaction information
Abstract

An important goal of systems biology is the identification and investigation of known and predicted protein-protein interactions to obtain more information about new cellular pathways and processes. Proteins interact via domains, thus it is important to know which domains a protein contains and which domains interact with each other. Here we present the Java^TM^ program ProDGe (Protein Domain Gene), which visualizes existing and suggests novel domain-domain interactions and protein-protein interactions at the domain level. The comprehensive dataset behind ProDGe consists of protein, domain and interaction information for both layers, collected and combined appropriately from UniProt, Pfam, DOMINE and IntAct. Based on known domain interactions, ProDGe suggests novel protein interactions and assigns them to four confidence classes, depending on the reliability of the underlying domain interaction. Furthermore, ProDGe is able to identify potential homologous interaction partners in other species, which is particularly helpful when investigating poorly annotated species. We further evaluated and compared experimentally identified protein interactions from IntAct with domain interactions from DOMINE for six species and noticed that 31.13% of all IntAct protein interactions in all six species can be mapped to the actual interacting domains. ProDGe and a comprehensive documentation are freely available at http://www.cogsys.cs.uni-tuebingen.de/software/ProDGe.

Published
2011
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