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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. Personalized whole‐body models integrate metabolism, physiology, and the gut microbiome

Author

Ines Thiele, Swagatika Sahoo, Almut Heinken, Johannes Hertel, Laurent Heirendt, Maike K Aurich, and Ronan MT Fleming

Subjects
flux balance analysis, human metabolism, metabolic modeling, microbiome, Biology (General), QH301-705.5, Medicine (General), R5-920
Abstract

Abstract Comprehensive molecular‐level models of human metabolism have been generated on a cellular level. However, models of whole‐body metabolism have not been established as they require new methodological approaches to integrate molecular and physiological data. We developed a new metabolic network reconstruction approach that used organ‐specific information from literature and omics data to generate two sex‐specific whole‐body metabolic (WBM) reconstructions. These reconstructions capture the metabolism of 26 organs and six blood cell types. Each WBM reconstruction represents whole‐body organ‐resolved metabolism with over 80,000 biochemical reactions in an anatomically and physiologically consistent manner. We parameterized the WBM reconstructions with physiological, dietary, and metabolomic data. The resulting WBM models could recapitulate known inter‐organ metabolic cycles and energy use. We also illustrate that the WBM models can predict known biomarkers of inherited metabolic diseases in different biofluids. Predictions of basal metabolic rates, by WBM models personalized with physiological data, outperformed current phenomenological models. Finally, integrating microbiome data allowed the exploration of host–microbiome co‐metabolism. Overall, the WBM reconstructions, and their derived computational models, represent an important step toward virtual physiological humans.

Published
2020
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3. Do genome‐scale models need exact solvers or clearer standards?

Author

Ali Ebrahim, Eivind Almaas, Eugen Bauer, Aarash Bordbar, Anthony P Burgard, Roger L Chang, Andreas Dräger, Iman Famili, Adam M Feist, Ronan MT Fleming, Stephen S Fong, Vassily Hatzimanikatis, Markus J Herrgård, Allen Holder, Michael Hucka, Daniel Hyduke, Neema Jamshidi, Sang Yup Lee, Nicolas Le Novère, Joshua A Lerman, Nathan E Lewis, Ding Ma, Radhakrishnan Mahadevan, Costas Maranas, Harish Nagarajan, Ali Navid, Jens Nielsen, Lars K Nielsen, Juan Nogales, Alberto Noronha, Csaba Pal, Bernhard O Palsson, Jason A Papin, Kiran R Patil, Nathan D Price, Jennifer L Reed, Michael Saunders, Ryan S Senger, Nikolaus Sonnenschein, Yuekai Sun, and Ines Thiele

Subjects
Biology (General), QH301-705.5, Medicine (General), R5-920
Abstract

Graphical Abstract Recent results obtained by Chindelevitch et al (2014) are challenged by Ebrahim et al, illustrating several issues in the reproducibility of computational biology methods.

Published
2015
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4. Whole-body metabolic modelling predicts isoleucine dependency of SARS-CoV-2 replication

Author

Ronan MT Fleming and Ines Thiele

Subjects
viruses
Abstract

We aimed at investigating host-virus co-metabolism during SARS-CoV-2 infection. Therefore, we extended comprehensive sex-specific, whole-body organ resolved models of human metabolism with the necessary reactions to replicate SARS-CoV-2 in the lung as well as selected peripheral organs. Using this comprehensive host-virus model, we obtained the following key results: 1. The predicted maximal possible virus shedding rate was limited by isoleucine availability. 2. The supported initial viral load depended on the increase in CD4+ T-cells, consistent with the literature. 3. During viral infection, the whole-body metabolism changed including the blood metabolome, which agreed well with metabolomic studies from COVID-19 patients and healthy controls. 4. The virus shedding rate could be reduced by either inhibition of the guanylate kinase 1 or availability of amino acids, e.g., in the diet. 5. The virus variants achieved differed in their maximal possible virus shedding rates, which could be inversely linked to isoleucine occurrences in the sequences. Taken together, this study presents the metabolic crosstalk between host and virus and emphasis the role of amino acid metabolism during SARS-CoV-2 infection, in particular of isoleucine. As such, it provides an example of how computational modelling can complement more canonical approaches to gain insight into host-virus crosstalk and to identify potential therapeutic strategies.

Published
2022
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5. 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

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