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3. Longitudinal Multi-omics Analyses Identify Responses of Megakaryocytes, Erythroid Cells, and Plasmablasts as Hallmarks of Severe COVID-19

Author

Banovich, Nicholas E., Desai, Tushar, Eickelberg, Oliver, Haniffa, Muzlifa, Horvath, Peter, Kropski, Jonathan A., Lafyatis, Robert, Lundeberg, Joakim, Meyer, Kerstin, Nawijn, Martijn C., Nikolic, Marko, Ordovas Montanes, Jose, Pe’er, Dana, Tata, Purushothama Rao, Rawlins, Emma, Regev, Aviv, Reyfman, Paul, Samakovlis, Christos, Schultze, Joachim, Shalek, Alex, Shepherd, Douglas, Spence, Jason, Teichmann, Sarah, Theis, Fabian, Tsankov, Alexander, van den Berge, Maarten, von Papen, Michael, Whitsett, Jeffrey, Zaragosi, Laure Emmanuelle, Angelov, Angel, Bals, Robert, Bartholomäus, Alexander, Becker, Anke, Bezdan, Daniela, Bonifacio, Ezio, Bork, Peer, Clavel, Thomas, Colme-Tatche, Maria, Diefenbach, Andreas, Dilthey, Alexander, Fischer, Nicole, Förstner, Konrad, Frick, Julia-Stefanie, Gagneur, Julien, Goesmann, Alexander, Hain, Torsten, Hummel, Michael, Janssen, Stefan, Kalinowski, Jörn, Kallies, René, Kehr, Birte, Keller, Andreas, Kim-Hellmuth, Sarah, Klein, Christoph, Kohlbacher, Oliver, Korbel, Jan O., Kurth, Ingo, Landthaler, Markus, Li, Yang, Ludwig, Kerstin, Makarewicz, Oliwia, Marz, Manja, McHardy, Alice, Mertes, Christian, Nöthen, Markus, Nürnberg, Peter, Ohler, Uwe, Ossowski, Stephan, Overmann, Jörg, Peter, Silke, Pfeffer, Klaus, Poetsch, Anna R., Pühler, Alfred, Rajewsky, Niklaus, Ralser, Markus, Rieß, Olaf, Ripke, Stephan, Nunes da Rocha, Ulisses, Rosenstiel, Philip, Saliba, Antoine-Emmanuel, Sander, Leif Erik, Sawitzki, Birgit, Schiffer, Philipp, Schulte, Eva-Christina, Schultze, Joachim L., Sczyrba, Alexander, Stegle, Oliver, Stoye, Jens, Vehreschild, Janne, Vogel, Jörg, von Kleist, Max, Walker, Andreas, Walter, Jörn, Wieczorek, Dagmar, Ziebuhr, John, Bernardes, Joana P., Mishra, Neha, Tran, Florian, Bahmer, Thomas, Best, Lena, Blase, Johanna I., Bordoni, Dora, Franzenburg, Jeanette, Geisen, Ulf, Josephs-Spaulding, Jonathan, Köhler, Philipp, Künstner, Axel, Rosati, Elisa, Aschenbrenner, Anna C., Bacher, Petra, Baran, Nathan, Boysen, Teide, Brandt, Burkhard, Bruse, Niklas, Dörr, Jonathan, Dräger, Andreas, Elke, Gunnar, Ellinghaus, David, Fischer, Julia, Forster, Michael, Franke, Andre, Franzenburg, Sören, Frey, Norbert, Friedrichs, Anette, Fuß, Janina, Glück, Andreas, Hamm, Jacob, Hinrichsen, Finn, Hoeppner, Marc P., Imm, Simon, Junker, Ralf, Kaiser, Sina, Kan, Ying H., Knoll, Rainer, Lange, Christoph, Laue, Georg, Lier, Clemens, Lindner, Matthias, Marinos, Georgios, Markewitz, Robert, Nattermann, Jacob, Noth, Rainer, Pickkers, Peter, Rabe, Klaus F., Renz, Alina, Röcken, Christoph, Rupp, Jan, Schaffarzyk, Annika, Scheffold, Alexander, Schulte-Schrepping, Jonas, Schunk, Domagoj, Skowasch, Dirk, Ulas, Thomas, Wandinger, Klaus-Peter, Wittig, Michael, Zimmermann, Johannes, Busch, Hauke, Hoyer, Bimba F., Kaleta, Christoph, Heyckendorf, Jan, Kox, Matthijs, Rybniker, Jan, and Schreiber, Stefan

Published
2020
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4. SBML Qualitative Models: a model representation format and infrastructure to foster interactions between qualitative modelling formalisms and tools

Author

Chaouiya, Claudine, Berenguier, Duncan, Keating, Sarah M, Naldi, Aurelien, van Iersel, Martijn P., Rodriguez, Nicolas, Dräger, Andreas, Büchel, Finja, Cokelaer, Thomas, Kowal, Bryan, Wicks, Benjamin, Gonçalves, Emanuel, Dorier, Julien, Page, Michel, Monteiro, Pedro T., von Kamp, Axel, Xenarios, Ioannis, de Jong, Hidde, Hucka, Michael, Klamt, Steffen, Thieffry, Denis, Novère, Nicolas Le, Saez-Rodriguez, Julio, and Helikar, Tomáš

Subjects
Quantitative Biology - Molecular Networks
Abstract

Background: Qualitative frameworks, especially those based on the logical discrete formalism, are increasingly used to model regulatory and signalling networks. A major advantage of these frameworks is that they do not require precise quantitative data, and that they are well-suited for studies of large networks. While numerous groups have developed specific computational tools that provide original methods to analyse qualitative models, a standard format to exchange qualitative models has been missing. Results: We present the System Biology Markup Language (SBML) Qualitative Models Package ("qual"), an extension of the SBML Level 3 standard designed for computer representation of qualitative models of biological networks. We demonstrate the interoperability of models via SBML qual through the analysis of a specific signalling network by three independent software tools. Furthermore, the cooperative development of the SBML qual format paved the way for the development of LogicalModel, an open-source model library, which will facilitate the adoption of the format as well as the collaborative development of algorithms to analyze qualitative models. Conclusion: SBML qual allows the exchange of qualitative models among a number of complementary software tools. SBML qual has the potential to promote collaborative work on the development of novel computational approaches, as well as on the specification and the analysis of comprehensive qualitative models of regulatory and signalling networks., Comment: 29 pages, 7 figures

Published
2013

5. Large-scale generation of computational models from biochemical pathway maps

Author

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

Subjects
Quantitative Biology - Molecular Networks
Abstract

Background: Systems biology projects and omics technologies have led to a growing number of biochemical pathway reconstructions. However, mathematical models are still most often created de novo, based on reading the literature and processing pathway data manually. Results: To increase the efficiency with which such models can be created, we automatically generated mathematical models from pathway representations using a suite of freely available software. We produced models that combine data from KEGG PATHWAY, BioCarta, MetaCyc and SABIO-RK; According to the source data, three types of models are provided: kinetic, logical and constraint-based. All models are encoded using SBML Core and Qual packages, and available through BioModels Database. Each model contains the list of participants, the interactions, and the relevant mathematical constructs, but, in most cases, no meaningful parameter values. Most models are also available as easy to understand graphical SBGN maps. Conclusions: to date, the project has resulted in more than 140000 models freely available. We believe this resource can tremendously accelerate the development of mathematical models by providing initial starting points ready for parametrization., Comment: 29 pages, 8 figures

Published
2013
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7. COVID19 Disease Map, a computational knowledge repository of virus-host interaction mechanisms

Author

Ostaszewski, Marek, Niarakis, Anna, Mazein, Alexander, Kuperstein, Inna, Phair, Robert, Orta-Resendiz, Aurelio, Singh, Vidisha, Aghamiri, Sara Sadat, Acencio, Marcio Luis, Glaab, Enrico, Ruepp, Andreas, Fobo, Gisela, Montrone, Corinna, Brauner, Barbara, Frishman, Goar, Gomez, Luis Cristobal Monraz, Somers, Julia, Hoch, Matti, Gupta, Shailendra Kumar, Scheel, Julia, Borlinghaus, Hanna, Czauderna, Tobias, Schreiber, Falk, Montagud, Arnau, de Leon, Miguel Ponce, Funahashi, Akira, Hiki, Yusuke, Hiroi, Noriko, Yamada, Takahiro G., Draeger, Andreas, Renz, Alina, Naveez, Muhammad, Bocskei, Zsolt, Messina, Francesco, Boernigen, Daniela, Fergusson, Liam, Conti, Marta, Rameil, Marius, Nakonecnij, Vanessa, Vanhoefer, Jakob, Schmiester, Leonard, Wang, Muying, Ackerman, Emily E., Shoemaker, Jason E., Zucker, Jeremy, Oxford, Kristie, Teuton, Jeremy, Kocakaya, Ebru, Summak, Gokce Yagmur, Hanspers, Kristina, Kutmon, Martina, Coort, Susan, Eijssen, Lars, Ehrhart, Friederike, Rex, Devasahayam Arokia Balaya, Slenter, Denise, Martens, Marvin, Pham, Nhung, Haw, Robin, Jassal, Bijay, Matthews, Lisa, Orlic-Milacic, Marija, Ribeiro, Andrea Senff, Rothfels, Karen, Shamovsky, Veronica, Stephan, Ralf, Sevilla, Cristoffer, Varusai, Thawfeek, Ravel, Jean-Marie, Fraser, Rupsha, Ortseifen, Vera, Marchesi, Silvia, Gawron, Piotr, Smula, Ewa, Heirendt, Laurent, Satagopam, Venkata, Wu, Guanming, Riutta, Anders, Golebiewski, Martin, Owen, Stuart, Goble, Carole, Hu, Xiaoming, Overall, Rupert W., Maier, Dieter, Bauch, Angela, Gyori, Benjamin M., Bachman, John A., Vega, Carlos, Groues, Valentin, Vazquez, Miguel, Porras, Pablo, Licata, Luana, Iannuccelli, Marta, Sacco, Francesca, Nesterova, Anastasia, Yuryev, Anton, de Waard, Anita, Turei, Denes, Luna, Augustin, Babur, Ozgun, Soliman, Sylvain, Valdeolivas, Alberto, Esteban-Medina, Marina, Pena-Chilet, Maria, Rian, Kinza, Helikar, Tomas, Puniya, Bhanwar Lal, Modos, Dezso, Treveil, Agatha, Olbei, Marton, De Meulder, Bertrand, Ballereau, Stephane, Dugourd, Aurelien, Naldi, Aurelien, Noel, Vincent, Calzone, Laurence, Sander, Chris, Demir, Emek, Korcsmaros, Tamas, Freeman, Tom C., Auge, Franck, Beckmann, Jacques S., Hasenauer, Jan, Wolkenhauer, Olaf, Wilighagen, Egon L., Pico, Alexander R., Evelo, Chris T., Gillespie, Marc E., Stein, Lincoln D., Hermjakob, Henning, D'Eustachio, Peter, Saez-Rodriguez, Julio, Dopazo, Joaquin, Valencia, Alfonso, Kitano, Hiroaki, Barillot, Emmanuel, Auffray, Charles, Balling, Rudi, Schneider, Reinhard, Ostaszewski, Marek, Niarakis, Anna, Mazein, Alexander, Kuperstein, Inna, Phair, Robert, Orta-Resendiz, Aurelio, Singh, Vidisha, Aghamiri, Sara Sadat, Acencio, Marcio Luis, Glaab, Enrico, Ruepp, Andreas, Fobo, Gisela, Montrone, Corinna, Brauner, Barbara, Frishman, Goar, Gomez, Luis Cristobal Monraz, Somers, Julia, Hoch, Matti, Gupta, Shailendra Kumar, Scheel, Julia, Borlinghaus, Hanna, Czauderna, Tobias, Schreiber, Falk, Montagud, Arnau, de Leon, Miguel Ponce, Funahashi, Akira, Hiki, Yusuke, Hiroi, Noriko, Yamada, Takahiro G., Draeger, Andreas, Renz, Alina, Naveez, Muhammad, Bocskei, Zsolt, Messina, Francesco, Boernigen, Daniela, Fergusson, Liam, Conti, Marta, Rameil, Marius, Nakonecnij, Vanessa, Vanhoefer, Jakob, Schmiester, Leonard, Wang, Muying, Ackerman, Emily E., Shoemaker, Jason E., Zucker, Jeremy, Oxford, Kristie, Teuton, Jeremy, Kocakaya, Ebru, Summak, Gokce Yagmur, Hanspers, Kristina, Kutmon, Martina, Coort, Susan, Eijssen, Lars, Ehrhart, Friederike, Rex, Devasahayam Arokia Balaya, Slenter, Denise, Martens, Marvin, Pham, Nhung, Haw, Robin, Jassal, Bijay, Matthews, Lisa, Orlic-Milacic, Marija, Ribeiro, Andrea Senff, Rothfels, Karen, Shamovsky, Veronica, Stephan, Ralf, Sevilla, Cristoffer, Varusai, Thawfeek, Ravel, Jean-Marie, Fraser, Rupsha, Ortseifen, Vera, Marchesi, Silvia, Gawron, Piotr, Smula, Ewa, Heirendt, Laurent, Satagopam, Venkata, Wu, Guanming, Riutta, Anders, Golebiewski, Martin, Owen, Stuart, Goble, Carole, Hu, Xiaoming, Overall, Rupert W., Maier, Dieter, Bauch, Angela, Gyori, Benjamin M., Bachman, John A., Vega, Carlos, Groues, Valentin, Vazquez, Miguel, Porras, Pablo, Licata, Luana, Iannuccelli, Marta, Sacco, Francesca, Nesterova, Anastasia, Yuryev, Anton, de Waard, Anita, Turei, Denes, Luna, Augustin, Babur, Ozgun, Soliman, Sylvain, Valdeolivas, Alberto, Esteban-Medina, Marina, Pena-Chilet, Maria, Rian, Kinza, Helikar, Tomas, Puniya, Bhanwar Lal, Modos, Dezso, Treveil, Agatha, Olbei, Marton, De Meulder, Bertrand, Ballereau, Stephane, Dugourd, Aurelien, Naldi, Aurelien, Noel, Vincent, Calzone, Laurence, Sander, Chris, Demir, Emek, Korcsmaros, Tamas, Freeman, Tom C., Auge, Franck, Beckmann, Jacques S., Hasenauer, Jan, Wolkenhauer, Olaf, Wilighagen, Egon L., Pico, Alexander R., Evelo, Chris T., Gillespie, Marc E., Stein, Lincoln D., Hermjakob, Henning, D'Eustachio, Peter, Saez-Rodriguez, Julio, Dopazo, Joaquin, Valencia, Alfonso, Kitano, Hiroaki, Barillot, Emmanuel, Auffray, Charles, Balling, Rudi, and Schneider, Reinhard

Abstract

We need to effectively combine the knowledge from surging literature with complex datasets to propose mechanistic models of SARS-CoV-2 infection, improving data interpretation and predicting key targets of intervention. Here, we describe a large-scale community effort to build an open access, interoperable and computable repository of COVID-19 molecular mechanisms. The COVID-19 Disease Map (C19DMap) is a graphical, interactive representation of disease-relevant molecular mechanisms linking many knowledge sources. Notably, it is a computational resource for graph-based analyses and disease modelling. To this end, we established a framework of tools, platforms and guidelines necessary for a multifaceted community of biocurators, domain experts, bioinformaticians and computational biologists. The diagrams of the C19DMap, curated from the literature, are integrated with relevant interaction and text mining databases. We demonstrate the application of network analysis and modelling approaches by concrete examples to highlight new testable hypotheses. This framework helps to find signatures of SARS-CoV-2 predisposition, treatment response or prioritisation of drug candidates. Such an approach may help deal with new waves of COVID-19 or similar pandemics in the long-term perspective.

Published
2021
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8. Longitudinal Multi-omics Analyses Identify Responses of Megakaryocytes, Erythroid Cells, and Plasmablasts as Hallmarks of Severe COVID-19

Author

Bernardes, Joana P., Mishra, Neha, Tran, Florian, Bahmer, Thomas, Best, Lena, Blase, Johanna, I, Bordoni, Dora, Franzenburg, Jeanette, Geisen, Ulf, Josephs-Spaulding, Jonathan, Koehler, Philipp, Kuenstner, Axel, Rosati, Elisa, Aschenbrenner, Anna C., Bacher, Petra, Baran, Nathan, Boysen, Teide, Brandt, Burkhard, Bruse, Niklas, Doerr, Jonathan, Draeger, Andreas, Elke, Gunnar, Ellinghaus, David, Fischer, Julia, Forster, Michael, Franke, Andre, Franzenburg, Soeren, Frey, Norbert, Friedrichs, Anette, Fuss, Janina, Glueck, Andreas, Hamm, Jacob, Hinrichsen, Finn, Hoeppner, Marc P., Imm, Simon, Junker, Ralf, Kaiser, Sina, Kan, Ying H., Knoll, Rainer, Lange, Christoph, Laue, Georg, Lier, Clemens, Lindner, Matthias, Marinos, Georgios, Markewitz, Robert, Nattermann, Jacob, Noth, Rainer, Pickkers, Peter, Rabe, Klaus F., Renz, Alina, Roecken, Christoph, Rupp, Jan, Schaffarzyk, Annika, Scheffold, Alexander, Schulte-Schrepping, Jonas, Schunk, Domagoj, Skowasch, Dirk, Ulas, Thomas, Wandinger, Klaus-Peter, Wittig, Michael, Zimmermann, Johannes, Busch, Hauke, Hoyer, Bimba F., Kaleta, Christoph, Heyckendorf, Jan, Kox, Matthijs, Rybniker, Jan, Schreiber, Stefan, Schultze, Joachim L., Rosenstiel, Philip, Bernardes, Joana P., Mishra, Neha, Tran, Florian, Bahmer, Thomas, Best, Lena, Blase, Johanna, I, Bordoni, Dora, Franzenburg, Jeanette, Geisen, Ulf, Josephs-Spaulding, Jonathan, Koehler, Philipp, Kuenstner, Axel, Rosati, Elisa, Aschenbrenner, Anna C., Bacher, Petra, Baran, Nathan, Boysen, Teide, Brandt, Burkhard, Bruse, Niklas, Doerr, Jonathan, Draeger, Andreas, Elke, Gunnar, Ellinghaus, David, Fischer, Julia, Forster, Michael, Franke, Andre, Franzenburg, Soeren, Frey, Norbert, Friedrichs, Anette, Fuss, Janina, Glueck, Andreas, Hamm, Jacob, Hinrichsen, Finn, Hoeppner, Marc P., Imm, Simon, Junker, Ralf, Kaiser, Sina, Kan, Ying H., Knoll, Rainer, Lange, Christoph, Laue, Georg, Lier, Clemens, Lindner, Matthias, Marinos, Georgios, Markewitz, Robert, Nattermann, Jacob, Noth, Rainer, Pickkers, Peter, Rabe, Klaus F., Renz, Alina, Roecken, Christoph, Rupp, Jan, Schaffarzyk, Annika, Scheffold, Alexander, Schulte-Schrepping, Jonas, Schunk, Domagoj, Skowasch, Dirk, Ulas, Thomas, Wandinger, Klaus-Peter, Wittig, Michael, Zimmermann, Johannes, Busch, Hauke, Hoyer, Bimba F., Kaleta, Christoph, Heyckendorf, Jan, Kox, Matthijs, Rybniker, Jan, Schreiber, Stefan, Schultze, Joachim L., and Rosenstiel, Philip

Abstract

Temporal resolution of cellular features associated with a severe COVID-19 disease trajectory is needed for understanding skewed immune responses and defining predictors of outcome. Here, we performed a longitudinal multi-omics study using a two-center cohort of 14 patients. We analyzed the bulk transcriptome, bulk DNA methylome, and single-cell transcriptome (>358,000 cells, including BCR profiles) of peripheral blood samples harvested from up to 5 time points. Validation was performed in two independent cohorts of COVID-19 patients. Severe COVID-19 was characterized by an increase of proliferating, metabolically hyperactive plasmablasts. Coinciding with critical illness, we also identified an expansion of interferon-activated circulating megakaryocytes and increased erythropoiesis with features of hypoxic signaling. Megakaryocyte- and erythroid-cell-derived co-expression modules were predictive of fatal disease outcome. The study demonstrates broad cellular effects of SARS-CoV-2 infection beyond adaptive immune cells and provides an entry point toward developing biomarkers and targeted treatments of patients with COVID-19.

Published
2020

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

Author

Computer Science, Keating, Sarah M., Waltemath, Dagmar, Koenig, Matthias, Zhang, Fengkai, Draeger, Andreas, Chaouiya, Claudine, Bergmann, Frank T., Finney, Andrew, Gillespie, Colin S., Helikar, Tomas, Hoops, Stefan, Malik-Sheriff, Rahuman S., Moodie, Stuart L., Moraru, Ion I., Myers, Chris J., Naldi, Aurelien, Olivier, Brett G., Sahle, Sven, Schaff, James C., Smith, Lucian P., Swat, Maciej J., Thieffry, Denis, Watanabe, Leandro, Wilkinson, Darren J., Blinov, Michael L., Begley, Kimberly, Faeder, James R., Gomez, Harold F., Hamm, Thomas M., Inagaki, Yuichiro, Liebermeister, Wolfram, Lister, Allyson L., Lucio, Daniel, Mjolsness, Eric, Proctor, Carole J., Raman, Karthik, Rodriguez, Nicolas, Shaffer, Clifford A., Shapiro, Bruce E., Stelling, Joerg, Swainston, Neil, Tanimura, Naoki, Wagner, John, Meier-Schellersheim, Martin, Sauro, Herbert M., Palsson, Bernhard, Bolouri, Hamid, Kitano, Hiroaki, Funahashi, Akira, Hermjakob, Henning, Doyle, John C., Hucka, Michael, Computer Science, Keating, Sarah M., Waltemath, Dagmar, Koenig, Matthias, Zhang, Fengkai, Draeger, Andreas, Chaouiya, Claudine, Bergmann, Frank T., Finney, Andrew, Gillespie, Colin S., Helikar, Tomas, Hoops, Stefan, Malik-Sheriff, Rahuman S., Moodie, Stuart L., Moraru, Ion I., Myers, Chris J., Naldi, Aurelien, Olivier, Brett G., Sahle, Sven, Schaff, James C., Smith, Lucian P., Swat, Maciej J., Thieffry, Denis, Watanabe, Leandro, Wilkinson, Darren J., Blinov, Michael L., Begley, Kimberly, Faeder, James R., Gomez, Harold F., Hamm, Thomas M., Inagaki, Yuichiro, Liebermeister, Wolfram, Lister, Allyson L., Lucio, Daniel, Mjolsness, Eric, Proctor, Carole J., Raman, Karthik, Rodriguez, Nicolas, Shaffer, Clifford A., Shapiro, Bruce E., Stelling, Joerg, Swainston, Neil, Tanimura, Naoki, Wagner, John, Meier-Schellersheim, Martin, Sauro, Herbert M., Palsson, Bernhard, Bolouri, Hamid, Kitano, Hiroaki, Funahashi, Akira, Hermjakob, Henning, Doyle, John C., and Hucka, Michael

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 developedSBMLLevel 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 ofSBMLand 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 howSBMLLevel 3 provides the foundation needed to support this evolution.

Published
2020

11. SBMLsqueezer 2: context-sensitive creation of kinetic equations in biochemical networks

Author

Draeger, Andreas, Zielinski, Daniel C., Keller, Roland, Rall, Matthias, Eichner, Johannes, Palsson, Bernhard O., and Zell, Andreas

Subjects
Software engineering, Other Medical and Health Sciences, Information extraction, Bioinformatics, Applied Mathematics, Systems Biology, Biological networks, Bioengineering, Biological, Regulatory networks, Computer Software, Kinetics, Structural Biology, Models, Modelling and Simulation, Computer Simulation, Mathematical modeling, Biochemistry and Cell Biology, Molecular Biology, Metabolic engineering, Algorithms, Software, Metabolic Networks and Pathways
Abstract

Background: The size and complexity of published biochemical network reconstructions are steadily increasing, expanding the potential scale of derived computational models. However, the construction of large biochemical network models is a laborious and error-prone task. Automated methods have simplified the network reconstruction process, but building kinetic models for these systems is still a manually intensive task. Appropriate kinetic equations, based upon reaction rate laws, must be constructed and parameterized for each reaction. The complex test-and-evaluation cycles that can be involved during kinetic model construction would thus benefit from automated methods for rate law assignment. Results: We present a high-throughput algorithm to automatically suggest and create suitable rate laws based upon reaction type according to several criteria. The criteria for choices made by the algorithm can be influenced in order to assign the desired type of rate law to each reaction. This algorithm is implemented in the software package SBMLsqueezer 2. In addition, this program contains an integrated connection to the kinetics database SABIO-RK to obtain experimentally-derived rate laws when desired. Conclusions: The described approach fills a heretofore absent niche in workflows for large-scale biochemical kinetic model construction. In several applications the algorithm has already been demonstrated to be useful and scalable. SBMLsqueezer is platform independent and can be used as a stand-alone package, as an integrated plugin, or through a web interface, enabling flexible solutions and use-case scenarios.

Published
2015
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12. Evaluation of rate law approximations in bottom-up kinetic models of metabolism

Author

Du, Bin, Zielinski, Daniel C., Kavvas, Erol S., Draeger, Andreas, Tan, Justin, Zhang, Zhen, Ruggiero, Kayla E., Arzumanyan, Garri A., Palsson, Bernhard, Du, Bin, Zielinski, Daniel C., Kavvas, Erol S., Draeger, Andreas, Tan, Justin, Zhang, Zhen, Ruggiero, Kayla E., Arzumanyan, Garri A., and Palsson, Bernhard

Abstract

Background: The mechanistic description of enzyme kinetics in a dynamic model of metabolism requires specifying the numerical values of a large number of kinetic parameters. The parameterization challenge is often addressed through the use of simplifying approximations to form reaction rate laws with reduced numbers of parameters. Whether such simplified models can reproduce dynamic characteristics of the full system is an important question. Results: In this work, we compared the local transient response properties of dynamic models constructed using rate laws with varying levels of approximation. These approximate rate laws were: 1) a Michaelis-Menten rate law with measured enzyme parameters, 2) a Michaelis-Menten rate law with approximated parameters, using the convenience kinetics convention, 3) a thermodynamic rate law resulting from a metabolite saturation assumption, and 4) a pure chemical reaction mass action rate law that removes the role of the enzyme from the reaction kinetics. We utilized in vivo data for the human red blood cell to compare the effect of rate law choices against the backdrop of physiological flux and concentration differences. We found that the Michaelis-Menten rate law with measured enzyme parameters yields an excellent approximation of the full system dynamics, while other assumptions cause greater discrepancies in system dynamic behavior. However, iteratively replacing mechanistic rate laws with approximations resulted in a model that retains a high correlation with the true model behavior. Investigating this consistency, we determined that the order of magnitude differences among fluxes and concentrations in the network were greatly influential on the network dynamics. We further identified reaction features such as thermodynamic reversibility, high substrate concentration, and lack of allosteric regulation, which make certain reactions more suitable for rate law approximations. Conclusions: Overall, our work generally supports the u

Published
2016

13. Escher: A Web Application for Building, Sharing, and Embedding Data-Rich Visualizations of Biological Pathways

Author

King, Zachary A., Draeger, Andreas, Ebrahim, Ali, Sonnenschein, Nikolaus, Lewis, Nathan, Palsson, Bernhard O., King, Zachary A., Draeger, Andreas, Ebrahim, Ali, Sonnenschein, Nikolaus, Lewis, Nathan, and Palsson, Bernhard O.

Abstract

Escher is a web application for visualizing data on biological pathways. Three key features make Escher a uniquely effective tool for pathway visualization. First, users can rapidly design new pathway maps. Escher provides pathway suggestions based on user data and genome-scale models, so users can draw pathways in a semi-automated way. Second, users can visualize data related to genes or proteins on the associated reactions and pathways, using rules that define which enzymes catalyze each reaction. Thus, users can identify trends in common genomic data types (e.g. RNA-Seq, proteomics, ChIP)-in conjunction with metabolite-and reaction-oriented data types (e.g. metabolomics, fluxomics). Third, Escher harnesses the strengths of web technologies (SVG, D3, developer tools) so that visualizations can be rapidly adapted, extended, shared, and embedded. This paper provides examples of each of these features and explains how the development approach used for Escher can be used to guide the development of future visualization tools.

Published
2015

14. Successful Termination of a Furunculosis Outbreak Due to lukS-lukF--Positive, Methicillin-Susceptible Staphylococcus aureus in a German Village by Stringent Decolonization, 2002-2005.

Author

Wiese-Posselt, Miriam, Heuck, Dagmar, Draeger, Andreas, Mielke, Martin, Wolfgang Witte, Ammon, Andrea, and Hamouda, Osamah

Subjects
STAPHYLOCOCCUS aureus infections, DRUG resistance in microorganisms, ANTIBIOTICS, FURUNCULOSIS, DISEASE outbreaks
Abstract

Background. Skin infections due to Staphylococcus aureus have recently become a public concern, mainly because of emerging resistance against widely used antibiotics and specific virulence determinants. Strains harboring the lukS-lukF gene (which codes for Panton-Valentine leukocidin) are frequently associated with severe furunculosis. Generally applicable strategies for the control of community outbreaks of furunculosis have not been defined. Methods. We report the investigation and successful termination of an outbreak of furunculosis due to lukSlukF-positive S. aureus in a German village (n = 144). Nasal swab specimens were obtained from village residents. A retrospective cohort study was conducted. Nasally colonized persons, persons who had current furuncles or who had experienced relapsing furuncles since 2002, and their family members underwent stringent decolonization measures using mupirocin nasal ointment and disinfecting wash solution. Multiple nasal swab specimens were obtained to monitor the long-term outcome of decolonization measures. Results. From January 1998 through December 2004, 42 cases and 59 relapses of furunculosis were identified by active case finding. Of 140 participants tested, 51 (36%) were found to be nasally colonized with S. aureus. In 9 participants, the strain was positive for lukS-lukF. No methicillin resistance was detected. Risk of furunculosis was associated with contact with case patients (relative risk, 6.8; 95% confidence interval, 3.2-14.3) and nasal colonization with a lukS-lukF-positive strain of S. aureus (relative risk, 3.6; 95% confidence interval, 2.3-5.9). Passive surveillance implemented in January 2005 did not detect any case of lukS-lukF-positive, S. aureus-associated furuncles in this village. Conclusion. This report describes a successful strategy for terminating the transmission of epidemic strains of S. aureus among a nonhospitalized population. [ABSTRACT FROM AUTHOR]

Published
2007
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15. Do genome-scale models need exact solvers or clearer standards?

Author

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

Published
2015
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16. The systems biology simulation core algorithm

Author

Keller, Roland, Dörr, Alexander, Tabira, Akito, Funahashi, Akira, Ziller, Michael J, Adams, Richard, Rodriguez, Nicolas, Novère, Nicolas Le, Hiroi, Noriko, Planatscher, Hannes, Zell, Andreas, and Dräger, Andreas

Subjects
Systems biology, Biological networks, Mathematical modeling, Simulation, Algorithms, Ordinary differential equation systems, Numerical integration, Software engineering
Abstract

Background: With the increasing availability of high dimensional time course data for metabolites, genes, and fluxes, the mathematical description of dynamical systems has become an essential aspect of research in systems biology. Models are often encoded in formats such as SBML, whose structure is very complex and difficult to evaluate due to many special cases. Results: This article describes an efficient algorithm to solve SBML models that are interpreted in terms of ordinary differential equations. We begin our consideration with a formal representation of the mathematical form of the models and explain all parts of the algorithm in detail, including several preprocessing steps. We provide a flexible reference implementation as part of the Systems Biology Simulation Core Library, a community-driven project providing a large collection of numerical solvers and a sophisticated interface hierarchy for the definition of custom differential equation systems. To demonstrate the capabilities of the new algorithm, it has been tested with the entire SBML Test Suite and all models of BioModels Database. Conclusions: The formal description of the mathematics behind the SBML format facilitates the implementation of the algorithm within specifically tailored programs. The reference implementation can be used as a simulation backend for Java™-based programs. Source code, binaries, and documentation can be freely obtained under the terms of the LGPL version 3 from http://simulation-core.sourceforge.net. Feature requests, bug reports, contributions, or any further discussion can be directed to the mailing list simulation-core-development@lists.sourceforge.net.

Published
2013
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