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1. Teaming up main group metals with metallic iron to boost hydrogenation catalysis

Author

Christian Färber, Philipp Stegner, Ulrich Zenneck, Christian Knüpfer, Georg Bendt, Stephan Schulz, and Sjoerd Harder

Subjects
Science
Abstract

Elemental iron turns alkaline-earth metal complexes into highly active catalysts for the hydrogenation of alkenes, alkynes, imines and arenes. The proposed mechanism combines homogeneous catalysis by a soluble main group metal hydride complex with heterogeneous catalysis at the iron surface.

Published
2022
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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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3. Controlled vocabularies and semantics in systems biology

Author

Mélanie Courtot, Nick Juty, Christian Knüpfer, Dagmar Waltemath, Anna Zhukova, Andreas Dräger, Michel Dumontier, Andrew Finney, Martin Golebiewski, Janna Hastings, Stefan Hoops, Sarah Keating, Douglas B Kell, Samuel Kerrien, James Lawson, Allyson Lister, James Lu, Rainer Machne, Pedro Mendes, Matthew Pocock, Nicolas Rodriguez, Alice Villeger, Darren J Wilkinson, Sarala Wimalaratne, Camille Laibe, Michael Hucka, and Nicolas Le Novère

Subjects
dynamics, kinetics, model, ontology, simulation, Biology (General), QH301-705.5, Medicine (General), R5-920
Abstract

Abstract The use of computational modeling to describe and analyze biological systems is at the heart of systems biology. Model structures, simulation descriptions and numerical results can be encoded in structured formats, but there is an increasing need to provide an additional semantic layer. Semantic information adds meaning to components of structured descriptions to help identify and interpret them unambiguously. Ontologies are one of the tools frequently used for this purpose. We describe here three ontologies created specifically to address the needs of the systems biology community. The Systems Biology Ontology (SBO) provides semantic information about the model components. The Kinetic Simulation Algorithm Ontology (KiSAO) supplies information about existing algorithms available for the simulation of systems biology models, their characterization and interrelationships. The Terminology for the Description of Dynamics (TEDDY) categorizes dynamical features of the simulation results and general systems behavior. The provision of semantic information extends a model's longevity and facilitates its reuse. It provides useful insight into the biology of modeled processes, and may be used to make informed decisions on subsequent simulation experiments.

Published
2011
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5. Lithium Aluminium Hydride and Metallic Iron: A Powerful Team in Alkene and Arene Hydrogenation Catalysis

Author

Christian Knüpfer, Christian Färber, Jens Langer, and Sjoerd Harder

Subjects
ddc:540, General Medicine, General Chemistry, Catalysis
Abstract

Alkenes that normally do not react with LiAlH4 (3‐hexene, cyclohexene, 1‐Me‐cyclohexene), can be reduced to the corresponding alkanes by a mixture of LiAlH4 and Fe0 (the iron was activated by Metal‐Vapour‐Synthesis). This alkene‐to‐alkane conversion with a stoichiometric quantity of LiAlH4/Fe0 does not need quenching with water or acids, implying that both H's originate from LiAlH4. The LiAlH4/Fe0 combination is also a remarkably potent cooperative catalyst for hydrogenation of multi‐substituted alkenes and benzene or toluene. An induction period of circa two hours and the minimally required temperature of 120 °C, suggests that the actual catalyst is a combination of Fe0 and the decomposition product of LiAlH4 (LiH and Al0). A thermally pre‐activated LiAlH4/Fe0 catalyst did not need an induction time and is also active at room temperature and 1 bar H2. A combination of AliBu3 and Fe0 is an even more active hydrogenation catalyst. Without pre‐activation, tetra‐substituted alkenes like Me2C=CMe2 and toluene could be fully hydrogenated.

Published
2023
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6. Toward Community Standards and Software for Whole-Cell Modeling.

Author

Dagmar Waltemath, Jonathan R. Karr, Frank T. Bergmann, Vijayalakshmi Chelliah, Michael Hucka, Marcus Krantz, Wolfram Liebermeister, Pedro Mendes 0001, Chris J. Myers, Pinar Pir, Begum Alaybeyoglu, Naveen K. Aranganathan, Kambiz Baghalian, Arne T. Bittig, Paulo E. Pinto Burke, Matteo Cantarelli, Yin Hoon Chew, Rafael S. Costa, Joseph Cursons, Tobias Czauderna, Arthur P. Goldberg, Harold F. Gómez, Jens Hahn, Tuure Hameri, Daniel F. Hernandez Gardiol, Denis Kazakiewicz, Ilya Kiselev, Vincent Knight-Schrijver, Christian Knüpfer, Matthias König 0003, Daewon Lee, Audald Lloret-Villas, Nikita Mandrik, J. Kyle Medley, Bertrand Moreau, Hojjat Naderi-Meshkin, Sucheendra K. Palaniappan, Daniel Priego-Espinosa, Martin Scharm, Mahesh Sharma, Kieran Smallbone, Natalie J. Stanford, Je-Hoon Song, Tom Theile, Milenko Tokic, Namrata Tomar, Vasundra Touré, Jannis Uhlendorf, Thawfeek M. Varusai, Leandro H. Watanabe, Florian Wendland, Markus Wolfien, James T. Yurkovich, Yan Zhu 0006, Argyris Zardilis, Anna Zhukova, and Falk Schreiber

Published
2016
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7. Carbon‐Halogen Bond Activation with Powerful Heavy Alkaline Earth Metal Hydrides

Author

Christian Knüpfer, Jonathan Mai, Jens Langer, Bastian Rösch, Michael Wiesinger, and Sjoerd Harder

Subjects
Inorganic Chemistry, Alkaline earth metal, Halogen bond, chemistry, Hydride, ddc:540, Inorganic chemistry, chemistry.chemical_element, Carbon
Abstract

Reaction of [(DIPePBDI)SrH]2 with C6H5X (X=Cl, Br, I) led to hydride‐halogenide exchange (DIPePBDI=HC[(Me)CN‐2,6‐(3‐pentyl)phenyl]2). Conversion rates increase with increasing halogen size (F

Published
2021
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9. Metallic Barium: A Versatile and Efficient Hydrogenation Catalyst

Author

Sjoerd Harder, Jonathan Eyselein, Christian Knüpfer, Ulrich Zenneck, Christian Färber, Michael Wiesinger, and Philipp Stegner

Subjects
metal activation, Aldimine, Materials science, alkaline earth metals, Ba‐Metal Catalysis | Very Important Paper, barium, hydride, Conjugated system, 010402 general chemistry, 01 natural sciences, Catalysis, Metal, chemistry.chemical_compound, Polymer chemistry, Benzene, Research Articles, chemistry.chemical_classification, Heptane, 010405 organic chemistry, Hydride, General Chemistry, Tetraphenylethylene, General Medicine, hydrogenation catalysis, 0104 chemical sciences, chemistry, visual_art, visual_art.visual_art_medium, ddc:546, Research Article
Abstract

Ba metal was activated by evaporation and cocondensation with heptane. This black powder is a highly active hydrogenation catalyst for the reduction of a variety of unactivated (non‐conjugated) mono‐, di‐ and tri‐substituted alkenes, tetraphenylethylene, benzene, a number of polycyclic aromatic hydrocarbons, aldimines, ketimines and various pyridines. The performance of metallic Ba in hydrogenation catalysis tops that of the hitherto most active molecular group 2 metal catalysts. Depending on the substrate, two different catalytic cycles are proposed. A: a classical metal hydride cycle and B: the Ba metal cycle. The latter is proposed for substrates that are easily reduced by Ba0, that is, conjugated alkenes, alkynes, annulated rings, imines and pyridines. In addition, a mechanism in which Ba0 and BaH2 are both essential is discussed. DFT calculations on benzene hydrogenation with a simple model system (Ba/BaH2) confirm that the presence of metallic Ba has an accelerating effect., Just a pinch of Ba metal: Efficient hydrogenation of a wide range of substrates is achieved with metallic barium previously activated by metal vapor synthesis. A mechanism in which Ba0 and BaH2 are both essential is discussed.

Published
2020
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10. Highly Active Superbulky Alkaline Earth Metal Amide Catalysts for Hydrogenation of Challenging Alkenes and Aromatic Rings

Author

Sjoerd Harder, Katharina Thum, Christian Knüpfer, Michael Wiesinger, Johannes Martin, Jonathan Eyselein, Samuel Grams, Jens Langer, and Christian Färber

Subjects
Cyclohexane, Aromatic rings, Cyclohexene, Alkenes, DFT calculations, 010402 general chemistry, 01 natural sciences, Medicinal chemistry, Catalysis, chemistry.chemical_compound, Amide, Benzene, Research Articles, chemistry.chemical_classification, 010405 organic chemistry, Alkene, Ligand, General Chemistry, Tetraphenylethylene, General Medicine, 0104 chemical sciences, chemistry, ddc:540, Alkaline Earth Metals, Hydrogenation, Research Article
Abstract

Two series of bulky alkaline earth (Ae) metal amide complexes have been prepared: Ae[N(TRIP)2]2 (1‐Ae) and Ae[N(TRIP)(DIPP)]2 (2‐Ae) (Ae=Mg, Ca, Sr, Ba; TRIP=SiiPr3, DIPP=2,6‐diisopropylphenyl). While monomeric 1‐Ca was already known, the new complexes have been structurally characterized. Monomers 1‐Ae are highly linear while the monomers 2‐Ae are slightly bent. The bulkier amide complexes 1‐Ae are by far the most active catalysts in alkene hydrogenation with activities increasing from Mg to Ba. Catalyst 1‐Ba can reduce internal alkenes like cyclohexene or 3‐hexene and highly challenging substrates like 1‐Me‐cyclohexene or tetraphenylethylene. It is also active in arene hydrogenation reducing anthracene and naphthalene (even when substituted with an alkyl) as well as biphenyl. Benzene could be reduced to cyclohexane but full conversion was not reached. The first step in catalytic hydrogenation is formation of an (amide)AeH species, which can form larger aggregates. Increasing the bulk of the amide ligand decreases aggregate size but it is unclear what the true catalyst(s) is (are). DFT calculations suggest that amide bulk also has a noticeable influence on the thermodynamics for formation of the (amide)AeH species. Complex 1‐Ba is currently the most powerful Ae metal hydrogenation catalyst. Due to tremendously increased activities in comparison to those of previously reported catalysts, the substrate scope in hydrogenation catalysis could be extended to challenging multi‐substituted unactivated alkenes and even to arenes among which benzene., Bulk=Hulk: Superbulky alkaline earth metal amide complexes were found to be extremely active catalysts for alkene hydrogenation, clearly extending the substrate scope. Even various arenes, including benzene, can be reduced under relatively mild conditions.

Published
2020
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13. 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
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14. Early Main Group Metal Catalysts for Imine Hydrosilylation

Author

Sjoerd Harder, Christian Knüpfer, Holger Elsen, Ana Escalona, and Christian Fischer

Subjects
Aldimine, Hydrosilylation, alkaline earth metals, Imine, 010402 general chemistry, 01 natural sciences, Medicinal chemistry, Catalysis, chemistry.chemical_compound, chemistry.chemical_classification, Full Paper, 010405 organic chemistry, Hydride, Aryl, potassium, Organic Chemistry, hydrosilylation, General Chemistry, Full Papers, Naturwissenschaftliche Fakultät, 0104 chemical sciences, chemistry, Main group element, Phenylsilane, imines, density functional calculations, ddc:540, Catalysis | Hot Paper
Abstract

The efficient catalytic reduction of imines with phenylsilane is achieved by using the potassium, calcium and strontium based catalysts [(DMAT)K (THF)]∞, (DMAT)2Ca⋅(THF)2 and (DMAT)2Sr⋅(THF)2 (DMAT=2‐dimethylamino‐α‐trimethylsilylbenzyl). Eight different aldimines and the ketimine Ph2C=NPh could be successfully reduced by PhSiH3 at temperatures between 25–60 °C with catalyst loadings down to 2.5 mol %. Also, simple amides like KN(SiMe3)2 or Ae[N(SiMe3)2]2 (Ae=Ca, Sr, Ba) catalyze this reaction. Activities increase with metal size. For most substrates the activity increases along the row K, First s ‐block metal catalysts for imine hydrosilylation are reported. The mechanism is confirmed by stoichiometric reactions, intermediate isolation and DFT calculations.

Published
2019

15. 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

19. Notions of similarity for systems biology models

Author

Christian Knüpfer, Dagmar Waltemath, Robert Hoehndorf, Tim Kacprowski, Ron Henkel, and Wolfram Liebermeister

Subjects
0301 basic medicine, Databases, Factual, Computer science, General problem, Systems biology, 0206 medical engineering, Network structure, 02 engineering and technology, Computational biology, Machine learning, computer.software_genre, Models, Biological, SBML, 03 medical and health sciences, User-Computer Interface, Software, Mathematical equations, model version control, Animals, Humans, Computer Simulation, information retrieval, Molecular Biology, Network model, Computational model, business.industry, Computational Biology, systems biology, network model, 030104 developmental biology, Papers, Artificial intelligence, business, Corrigendum, computer, 020602 bioinformatics, Information Systems, Signal Transduction
Abstract

Systems biology models are rapidly increasing in complexity, size and numbers. When building large models, researchers rely on software tools for the retrieval, comparison, combination and merging of models, as well as for version control. These tools need to be able to quantify the differences and similarities between computational models. However, depending on the specific application, the notion of ‘similarity’ may greatly vary. A general notion of model similarity, applicable to various types of models, is still missing. Here we survey existing methods for the comparison of models, introduce quantitative measures for model similarity, and discuss potential applications of combined similarity measures. To frame model comparison as a general problem, we describe a theoretical approach to defining and computing similarities based on a combination of different model aspects. The six aspects that we define as potentially relevant for similarity are underlying encoding, references to biological entities, quantitative behaviour, qualitative behaviour, mathematical equations and parameters and network structure. We argue that future similarity measures will benefit from combining these model aspects in flexible, problem-specific ways to mimic users’ intuition about model similarity, and to support complex model searches in databases.

Published
2017

20. Toward community standards and software for whole-cell modeling

Author

Vasundra Touré, Matteo Cantarelli, Argyris Zardilis, Bertrand Moreau, Arthur P. Goldberg, Milenko Tokic, Pınar Pir, Dagmar Waltemath, Kieran Smallbone, Jens Hahn, Kambiz Baghalian, Daewon Lee, Naveen K. Aranganathan, Vijayalakshmi Chelliah, Arne T. Bittig, Namrata Tomar, Anna Zhukova, Florian Wendland, Marcus Krantz, Yan Zhu, Mahesh C. Sharma, Wolfram Liebermeister, Frank Bergmann, Joseph Cursons, Rafael S. Costa, Matthias König, Leandro Watanabe, Pedro Mendes, Natalie J. Stanford, James T. Yurkovich, Falk Schreiber, Harold F. Gómez, J. Kyle Medley, Martin Scharm, Vincent Knight-Schrijver, Denis Kazakiewicz, Ilya Kiselev, Jannis Uhlendorf, Daniel Alejandro Priego-Espinosa, Chris J. Myers, Sucheendra K. Palaniappan, Hojjat Naderi-Meshkin, Michael Hucka, Thawfeek M. Varusai, Nikita Mandrik, Markus Wolfien, Begum Alaybeyoglu, Paulo E. Pinto Burke, Daniel Federico Hernandez Gardiol, Audald Lloret-Villas, Tom Theile, Tuure Hameri, Christian Knüpfer, Jonathan R. Karr, Je-Hoon Song, Yin Hoon Chew, and Tobias Czauderna

Subjects
Male, 0301 basic medicine, Standards, Markup language, Computer science, Systems biology, Cytological Techniques, 0206 medical engineering, Biomedical Engineering, 02 engineering and technology, Models, Biological, Article, Education, Computational science, 03 medical and health sciences, Whole-cell modeling, Software, Humans, Computer Simulation, Community standards, business.industry, Systems Biology, Computational Biology, 3. Good health, 030104 developmental biology, computational biology, education, simulation, standards, systems biology, whole-cell (WC) modeling, Female, ddc:004, Whole cell, Software engineering, business, Simulation, 020602 bioinformatics
Abstract

Objective: Whole-cell (WC) modeling is a promising tool for biological research, bioengineering, and medicine. However, substantial work remains to create accurate comprehensive models of complex cells. Methods: We organized the 2015 Whole-Cell Modeling Summer School to teach WC modeling and evaluate the need for new WC modeling standards and software by recoding a recently published WC model in the Systems Biology Markup Language. Results: Our analysis revealed several challenges to representing WC models using the current standards. Conclusion: We, therefore, propose several new WC modeling standards, software, and databases. Significance: We anticipate that these new standards and software will enable more comprehensive models. The Rostock and Utah meetings were supported by the Volkswagen Foundation (Grant 88495 to D. Waltemath and F. Schreiber). The work of J. R. Karr was supported by the James S. McDonnell Foundation Postdoctoral Fellowship Award in Studying Complex Systems and the National Science Foundation under Grant 1548123. The work of J. Cursons was supported by the Australian Research Council Centre of Excellence in Convergent Bio-Nano Science and Technology through Project CE140100036. Asterisk indicates corresponding authors.

Published
2016

21. Controlled vocabularies and semantics in systems biology

Author

Mélanie Courtot, Nick Juty, Christian Knüpfer, Dagmar Waltemath, Anna Zhukova, Andreas Dräger, Michel Dumontier, Andrew Finney, Martin Golebiewski, Janna Hastings, Stefan Hoops, Sarah Keating, Douglas B Kell, Samuel Kerrien, James Lawson, Allyson Lister, James Lu, Rainer Machne, Pedro Mendes, Matthew Pocock, Nicolas Rodriguez, Alice Villeger, Darren J Wilkinson, Sarala Wimalaratne, Camille Laibe, Michael Hucka, Nicolas Le Novère

Published
2011

22. Beyond Structure: KiSAO and TEDDY -- Two Ontologies Addressing Pragmatical and Dynamical Aspects of Computational Models in Systems Biology

Author

Nicolas Le Novère, Christian Knüpfer, and Dagmar Köhn

Subjects
Computational model, Theoretical computer science, Computer science, General Materials Science, Context (language use), SBML, Mathematical structure, Solver, Ontology (information science), Dynamical system, Living systems
Abstract

Computational models are becoming more and more the central scientific paradigm for understanding the complexity of living systems. With the increasing number and size of these models there is a growing need for model reuse and exchange. Furthermore, detailed models are not manageable without computer support. There are efforts to formalise the mathematical structure of models (e.g. SBML) and to standardise the kinetic and biological meaning of model components (e.g. SBO, GO, UniProt). However, formalising only the structure of computational models is not sufficient to easily exchange and reuse models and to achieve full computer support for modelling. We also need to formalise the pragmatical and dynamical aspects of models.For this purpose we propose two ontologies: The Kinetic Simulation Algorithm Ontology (KiSAO) and the TErminology for the Description of DYnamics (TEDDY). KiSAO covers algorithms used for simulation of computational models. The ontology classifies and puts into context existing simulation algorithms. For the classification, it uses several criteria such as deterministic/stochastic or spatial/nonspatial. The aim of TEDDY is to provide terms for describing and characterising dynamical behaviours, observable dynamical phenomena, and control elements of biological models and biological systems in Systems Biology and Synthetic Biology.We demonstrate how these new ontologies can extend the formalisation of models beyond structure, using the well-known repressilator model as an example. The simulation results depend pragmatically on the used algorithm: We compare the simulation results of the deterministic Livermore solver for ordinary differential equations (KiSAO:0000071) to the simulation results of the stochastic Gibson and Bruck’s next reaction method (KiSAO:0000027). The simulation results depend dynamically on the parameter setting: While parameter * (maximum number of produced proteins per promotor) is increased the modelled dynamical system undergoes a Supercritical Hopf Bifurcation (TEDDY_0000074). Below the critical value of * the system exhibits _Damped Oscillation_ (TEDDY_0000063) converging to a Stable Spiral Point (TEDDY_0000126). Above the bifurcation the system possesses a Stable _Limit Cycle_ (TEDDY_0000114), i.e. it shows Sustained Oscillation. The Negative Feedback (TEDDY_0000034) of the system is a necessary precondition for the ability of the system to oscillate.For details on KiSAO see the "MIASE project page":http://sourceforge.net/projects/miase, for details on TEDDY see the "project page":http://sourceforge.net/projects/teddyontology.

Published
2009
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24. How to formalise the meaning of a bio-model: a case study

Author

Peter Dittrich, Clemens Beckstein, and Christian Knüpfer

Subjects
Cognitive science, Computer science, Structural Biology, Modeling and Simulation, Systems biology, Modelling and Simulation, Applied Mathematics, Meaning (existential), Computational biology, Biological computation, Molecular Biology, Computer Science Applications
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