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2. 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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6. Abstract 457: Immunohistochemistry-informed AI systems for improved characterization of tumor-microenvironment in clinical non-small cell lung cancer H&E samples

Author

Thomas Mrowiec, Sharon Ruane, Simon Schallenberg, Gabriel Dernbach, Rumyana Todorova, Cornelius Böhm, Walter de Back, Blanca Pablos, Roman Schulte-Sasse, Ivana Trajanovska, Adelaida Creosteanu, Emil Barbuta, Marcus Otte, Christian Ihling, Hans Juergen Grote, Juergen Scheuenpflug, Viktor Matyas, Maximilian Alber, and Frederick Klauschen

Subjects
Cancer Research, Oncology
Abstract

Background: Automated cell-level characterization of the tumor microenvironment (TME) at scale is key to data-driven immuno-oncology. Artificial intelligence (AI)-powered analysis of hematoxylin and eosin (H&E) images scales and has recently been translated into diagnostics. However, robust TME analysis solely based on H&E data is bound by the stain's properties and by manual pathologist annotations, both in number and accuracy. In this study, we quantify the error introduced by pathologists' morphological assessment and mitigate this error by training AI-systems without manual pathologist annotations, using labels determined directly from IHC profiles. Methods: The work was carried out on 239 clinical NSCLC cases. CK-KL1, CD3+CD20, and Mum1 were used for defining carcinoma (CA), lymphocyte (LY), and plasma (PL) cells. For evaluation, representative regions were annotated by 3 trained pathologists. The workflow is based on co-registration of same-section H&E and IHC stained images with single cell precision. Cells were detected in H&E and labelled using rule-based algorithms that incorporated IHC information. This H&E data was used to train neural networks (NN). Results: (A) The inter-rater agreement of pathologists annotating on H&E is increased when information from registered IHC images is provided. (B) The concordance of pathologists on H&E-only compared to on H&E+IHC shows that pathologists miss or misclassify cells with a certain error. (C) NNs trained with IHC-based labels achieve similar performance for cell type classification on H&E as pathologists on H&E. Conclusion: This study demonstrates the value of combining histomorphological and IHC data for improved cell annotation. Our novel workflow provides a quantitative benchmark and facilitates training of accurate AI models for quantitative characterization of tumor and TME from H&E sections. A) Inter-rater agreement by metric, stain, and cell type By cell count, Pearson correlation By cell count, Pearson correlation By cell location, Krippendorff’s alpha By cell location, Krippendorff’s alpha Cell type H&E-only H&E+IHC H&E-only H&E+IHC CA 0.86 0.98 0.43 0.90 LY 0.88 0.99 0.21 0.76 PL 0.77 0.96 0.32 0.87 B) Performance of individual pathologists in H&E Against consensus in H&E+IHC Against own annotations in H&E+IHC Against own annotations in H&E+IHC Cell type By cell count, Pearson correlation By cell location, Precision By cell location, Recall CA 0.84 0.76 0.77 LY 0.78 0.70 0.60 PL 0.76 0.69 0.21 C) NN against annotator H&E+IHC consensus Cell Type By cell count, Pearson correlation CA 0.84 LY 0.92 PL 0.75 Citation Format: Thomas Mrowiec, Sharon Ruane, Simon Schallenberg, Gabriel Dernbach, Rumyana Todorova, Cornelius Böhm, Walter de Back, Blanca Pablos, Roman Schulte-Sasse, Ivana Trajanovska, Adelaida Creosteanu, Emil Barbuta, Marcus Otte, Christian Ihling, Hans Juergen Grote, Juergen Scheuenpflug, Viktor Matyas, Maximilian Alber, Frederick Klauschen. Immunohistochemistry-informed AI systems for improved characterization of tumor-microenvironment in clinical non-small cell lung cancer H&E samples [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 457.

Published
2022
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7. 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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8. 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

9. TriPepSVM -de novoprediction of RNA-binding proteins based on short amino acid motifs

Author

Erika C. Urdaneta, Roman Schulte-Sasse, Annkatrin Bressin, Davide Figini, Annalisa Marsico, and Benedikt M. Beckmann

Subjects
Amino Acid Motifs, RNA-binding protein, Plasma protein binding, Computational biology, Biology, Homology (biology), Bacterial protein, 03 medical and health sciences, 0302 clinical medicine, Genetics, Humans, Amino Acid Sequence, 030304 developmental biology, 0303 health sciences, Binding Sites, Computational Biology, RNA-Binding Proteins, RNA, biology.organism_classification, Proteome, RNA-Binding Motifs, Nucleic acid, Nucleic Acid Conformation, 030217 neurology & neurosurgery, Bacteria, Algorithms, Protein Binding
Abstract

In recent years hundreds of novel RNA-binding proteins (RBPs) have been identified leading to the discovery of novel RNA-binding domains (RBDs). Furthermore, unstructured or disordered low-complexity regions of RBPs have been identified to play an important role in interactions with nucleic acids. However, these advances in understanding RBPs are limited mainly to eukaryotic species and we only have limited tools to faithfully predict RNA-binders from bacteria. Here, we describe a support vector machine (SVM)-based method, called TriPepSVM, for the classification of RNA-binding proteins and non-RBPs. TriPepSVM applies string kernels to directly handle protein sequences using tri-peptide frequencies. Testing the method in human and bacteria, we find that several RBP-enriched tripeptides occur more often in structurally disordered regions of RBPs. TriPepSVM outperforms existing applications, which consider classical structural features of RNA-binding or homology, in the task of RBP prediction in both human and bacteria. Finally, we predict 66 novel RBPs inSalmonellaTyphimurium and validate the bacterial proteins ClpX, DnaJ and UbiG to associate with RNA in vivo.

Published
2018
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10. Unsupervised learning of DNA sequence features using a convolutional restricted Boltzmann machine

Author

Roman Schulte-Sasse and Wolfgang Kopp

Subjects
Regulation of gene expression, Restricted Boltzmann machine, business.industry, Promoter, Biology, Machine learning, computer.software_genre, DNA sequencing, DNA binding site, Regulatory sequence, Unsupervised learning, Motif (music), Artificial intelligence, business, computer
Abstract

Transcription factors (TFs) are important contributors to gene regulation. They specifically bind to short DNA stretches known as transcription factor binding sites (TFBSs), which are contained in regulatory regions (e.g. promoters), and thereby influence a target gene’s expression level. Computational biology has contributed substantially to understanding regulatory regions by developing numerous tools, including for discovering de novo motif. While those tools primarily focus on determining and studying TFBSs, the surrounding sequence context is often given less attention. In this paper, we attempt to fill this gap by adopting a so-called convolutional restricted Boltzmann machine (cRBM) that captures redundant features from the DNA sequences. The model uses an unsupervised learning approach to derive a rich, yet interpretable, description of the entire sequence context. We evaluated the cRBM on a range of publicly available ChIP-seq peak regions and investigated its capability to summarize heterogeneous sets of regulatory sequences in comparison with MEME-Chip, a popular motif discovery tool. In summary, our method yields a considerably more accurate description of the sequence composition than MEME-Chip, providing both a summary of strong TF motifs as well as subtle low-complexity features.

Published
2017
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11. High intensity interval training vs. high-volume running training during pre-season conditioning in high-level youth football: a cross-over trial

Author

Oliver Faude, Tim Meyer, Florian Müller, Roman Schulte-Zurhausen, and Reinhard Schnittker

Subjects
medicine.medical_specialty, Adolescent, Anaerobic Threshold, Movement, Physical fitness, Football, Physical Therapy, Sports Therapy and Rehabilitation, Athletic Performance, Interval training, Incremental exercise, Running, Soccer, Medicine, Humans, Orthopedics and Sports Medicine, Cross-Over Studies, business.industry, Lactate threshold, Crossover study, Physical Fitness, Physical therapy, Physical Endurance, Seasons, business, Anaerobic exercise, High-intensity interval training, Physical Conditioning, Human
Abstract

We aimed at comparing the endurance effects of high-intensity interval training (HIIT) with high-volume running training (HVT) during pre-season conditioning in 20 high-level youth football players (15.9 (s 0.8) years). Players either conducted HVT or HIIT during the summer preparation period. During winter preparation they performed the other training programme. Before and after each training period several fitness tests were conducted: multi-stage running test (to assess the individual anaerobic threshold (IAT) and maximal running velocity (Vmax)), vertical jumping height, and straight sprinting. A significant increase from pre- to post-test was observed in IAT velocity (P0.001) with a greater increase after HVT (+0.8 km · h(-1) vs. +0.5 km · h(-1) after HIIT, P = 0.04). Maximal velocity during the incremental exercise test also slightly increased with time (P = 0.09). Forty per cent (HIIT) and 15% (HVT) of all players did not improve IAT beyond baseline variability. The players who did not respond to HIIT were significantly slower during 30 m sprinting than responders (P = 0.02). No further significant differences between responders and non-responders were observed. Jump heights deteriorated significantly after both training periods (P0.003). Both training programmes seem to be promising means to improve endurance capacity in high-level youth football players during pre-season conditioning.

Published
2013

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