143 results on '"Hoops S"'
Search Results
2. Sphingolipid-Inherited Diseases of the Central Nervous System
- Author
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Hoops, S. L., Kolter, T., Sandhoff, K., Lajtha, Abel, editor, Tettamanti, Guido, editor, and Goracci, Gianfrancesco, editor
- Published
- 2009
- Full Text
- View/download PDF
3. E-selectin ligands recognised by HECA452 induce drug resistance in myeloma, which is overcome by the E-selectin antagonist, GMI-1271
- Author
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Natoni, A, Smith, T AG, Keane, N, McEllistrim, C, Connolly, C, Jha, A, Andrulis, M, Ellert, E, Raab, M S, Glavey, S V, Kirkham-McCarthy, L, Kumar, S K, Locatelli-Hoops, S C, Oliva, I, Fogler, W E, Magnani, J L, and OʼDwyer, M E
- Published
- 2017
- Full Text
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4. Phase III Placebo-Controlled, Randomized Clinical Trial With Synthetic Crohn's Disease Patients to Evaluate Treatment Response
- Author
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Abedi, V., primary, Lu, P., additional, Hontecillas, R., additional, Verma, M., additional, Vess, G.A., additional, Philipson, C.W., additional, Carbo, A., additional, Leber, A., additional, Juni, N.T., additional, Hoops, S., additional, and Bassaganya-Riera, J., additional
- Published
- 2016
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5. List of Contributors
- Author
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Abedi, V., primary, Albasri, J., additional, Andrews, D.J., additional, Bahrami, A.A., additional, Baptista, M.S., additional, Bassaganya-Riera, J., additional, Baturalp, T.B., additional, Ben Youssef, B., additional, Beyerer, J., additional, Bhavani, S.D., additional, Black, E., additional, Brooks, J.W., additional, Carbo, A., additional, Chan, A., additional, Chang, Y., additional, Cole, C.A., additional, Cordeiro, R.M., additional, Costa, E.B., additional, Costa, P., additional, de Luna Ortega, C.A., additional, Deeter, A., additional, Deller, J.R., additional, Di Ruberto, C., additional, Duan, Z.-H., additional, Early, C., additional, Ee, C.S., additional, Ertas, A., additional, Fahim, A., additional, Ferraz, A.C., additional, Fischer, Y., additional, Fleet, B.D., additional, Fronville, A., additional, Garza, J., additional, Gong, P., additional, Gonya, J., additional, Gonzalez, R.M., additional, Goodman, E.D., additional, Gupta, V., additional, Hashemi, R.R., additional, Hazzazi, N., additional, Hempel, D., additional, Hennig, M., additional, Hodges, V., additional, Hontecillas, R., additional, Hoops, S., additional, Irausquin, S., additional, Ishimaru, D., additional, Ji, W., additional, Juni, N.T., additional, Kho, T.K., additional, Koh, W., additional, Kumar, M., additional, Leber, A., additional, Li, Y., additional, Lin, H., additional, Liou, W.W., additional, Lu, P., additional, Lynch, A.G., additional, Manca, V., additional, Manzourolajdad, A., additional, Maruo, T., additional, Maxwell, A., additional, Miotto, R., additional, Mohamed, E.A., additional, Monteagudo, Á., additional, Montoni, L.M., additional, Mustard, J.L., additional, Neto, A.J.P., additional, Nia, M.E., additional, Nishimura, H., additional, Nobukawa, S., additional, Philipp, P., additional, Philipson, C.W., additional, Putzu, L., additional, Rani, T.S., additional, Rath, S.K., additional, Ray, W.C., additional, Rehbock, V., additional, Rivas, V.L., additional, Rodin, V., additional, Romo, J.C.M., additional, Rosas, F.J.L., additional, Rumpf, R.W., additional, Sahoo, R., additional, Samoylo, I., additional, Santos, J., additional, Sarr, A., additional, Schreiber, G., additional, Schrey, A., additional, Seidler, N.W., additional, Setola, R., additional, Shen, M., additional, Sim, K.S., additional, Ştirb, I., additional, Subedi, S., additional, Swain, D., additional, Ta, C.S., additional, Tao, Z., additional, Tavaré, S., additional, Tran, Q.N., additional, Trellese, G.G., additional, Tso, C.P., additional, Tyler, N.R., additional, Valafar, H., additional, Veloz, G.M., additional, Verma, M., additional, Vess, G.A., additional, Wijesekera, D., additional, Worley, J.B., additional, Wu, X., additional, Yang, B., additional, Yao, M., additional, Yao, Y., additional, Yu, B., additional, Zaki, N., additional, Zhang, C., additional, Zhang, Q., additional, Zhang, Y., additional, Zhao, H., additional, Zheng, B., additional, Zhukov, D., additional, and Zobel, B.B., additional
- Published
- 2016
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6. Influence of urban greenspace on the early-life gut microbiome
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De Roos, A. J., primary, Hoops, S. L., additional, Schinasi, L. H., additional, Frager, N., additional, Melly, S., additional, Puopolo, K. M., additional, Mukhopadhyay, S., additional, Knights, D., additional, and Gerber, J. S., additional
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- 2020
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7. Assessing the importance of individual players in biochemical networks in a global way: I67
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Kummer, U., Sahle, S., Hoops, S., and Mendes, P.
- Published
- 2010
8. A trial of nicotinamide in newly diagnosed patients with Type 1 (insulin-dependent) diabetes mellitus
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Chase, H. P., Butler-Simon, N., Garg, S., McDuffie, M., Hoops, S. L., and O'Brien, D.
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- 1990
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9. Chapter 28 - Phase III Placebo-Controlled, Randomized Clinical Trial With Synthetic Crohn's Disease Patients to Evaluate Treatment Response
- Author
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Abedi, V., Lu, P., Hontecillas, R., Verma, M., Vess, G.A., Philipson, C.W., Carbo, A., Leber, A., Juni, N.T., Hoops, S., and Bassaganya-Riera, J.
- Published
- 2016
- Full Text
- View/download PDF
10. Validity of the MoCA and MMSE in the detection of MCI and dementia in Parkinson disease.
- Author
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Hoops S, Nazem S, Siderowf AD, Duda JE, Xie SX, Stern MB, Weintraub D, Hoops, S, Nazem, S, Siderowf, A D, Duda, J E, Xie, S X, Stern, M B, and Weintraub, D
- Published
- 2009
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11. ENteric Immunity SImulator: A Tool for In Silico Study of Gastroenteric Infections
- Author
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Wendelsdorf, K. V., primary, Alam, M., additional, Bassaganya-Riera, J., additional, Bisset, K., additional, Eubank, S., additional, Hontecillas, R., additional, Hoops, S., additional, and Marathe, M., additional
- Published
- 2012
- Full Text
- View/download PDF
12. Identification of Pigment Epithelium-Derived Factor Protein Forms with Distinct Activities on Tumor Cell Lines
- Author
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Subramanian, P., primary, Deshpande, M., additional, Locatelli-Hoops, S., additional, Moghaddam-Taaheri, S., additional, Gutierrez, D., additional, Fitzgerald, D. P., additional, Guerrier, S., additional, Rapp, M., additional, Notario, V., additional, and Becerra, S. P., additional
- Published
- 2012
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13. 6. CAN PROFESSIONALISM BE ASSESSED THROUGH THE USE OF AN OBJECTIVE STRUCTURED CLINICAL EXAMINATION? A THREE PROGRAM EXPERIMENT.
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Kenney-Moore, P., primary, Landel, G., additional, and Hoops, S., additional
- Published
- 2008
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14. Sphingolipid-Inherited Diseases of the Central Nervous System.
- Author
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Hoops, S. L., Kolter, T., and Sandhoff, K.
- Abstract
The sphingolipidoses are a group of inherited lysosomal storage diseases, which are caused by a defect in one or more sphingolipid degradation steps. The subsequent accumulation of nondegradable material in one or more organs leads to the expression of the disease. Sphingolipids are integral parts of the plasma membrane of eukaryotic cells, where they form characteristic patterns. They contribute to the glycocalix of the cell and are believed to play a role in cell adhesion phenomena, in the barrier function of the skin, in the immune system, in signal transduction processes, and during embryogenesis. After their biosynthesis at intracellular membranes, they reach the plasma membrane, where they contribute to membrane function. The constitutive degradation of glycosphingolipids takes place on the surface of intraendosomal and intralysosomal membrane structures by the action of specific acid exohydrolases, sphingolipid activator proteins (SAPs), and anionic phospholipids. The deficiency of one of the proteins involved in sphingolipid degradation can cause sphingolipidosis. The storage of nondegradable compounds, the nature of the storage material, the cell-type specific expression of glycosphingolipids, and the residual activity of the degrading system are significant factors that contribute to the clinical manifestations of sphingolipidoses. However, the pathogenesis of these diseases is poorly understood until now. In this chapter, we summarize the characteristics of GM1-Gangliosidosis, GM2-Gangliosidoses (including B-variant or Tay-Sachs disease; 0-variant, or Sandhoff's disease, and AB-variant), Fabry's disease, Niemann-Pick disease (NPD) (types A and B), Metachromatic Leukodystrophy (MLD), Gaucher's disease, Krabbe's disease, and Farber's disease, which are all sphinglipidoses affecting the central nervous system. Most sphingolipidoses are as yet incurable diseases. Both, the ratio of substrate influx into the lysosomes and the degradation capacity can be addressed by therapeutic approaches. The current strategies for restoration of the defective degradation capacity within the lysosome are enzyme replacement therapy (ERT), cell-mediated therapy (CMT) including bone marrow transplantation (BMT) and cell-mediated ˵cross correction,″ gene therapy, and enzyme-enhancement therapy with chemical chaperones. The reduction of substrate influx into the lysosomes can be achieved by substrate deprivation therapy. Patients suffering from adult forms of Gaucher's disease and Fabry's disease have been successfully treated by ERT. [ABSTRACT FROM AUTHOR]
- Published
- 2010
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15. Simulation of Biochemical Networks using Copasi - A Complex Pathway Simulator.
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Sahle, S., Gauges, R., Pahle, J., Simus, N., Kummer, U., Hoops, S., Lee, C., Singhal, M., Liang Xu, and Mendes, P.
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- 2006
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16. Pulmonary edema associated with the treatment of preterm labor: what critical care nurses need to know
- Author
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Dabbs, AD, primary, Kraemer, KL, additional, and Hoops, S, additional
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- 1996
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17. Limited joint mobility in subjects with insulin dependent diabetes mellitus: relationship with eye and kidney complications.
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Garg, S K, primary, Chase, H P, additional, Marshall, G, additional, Jackson, W E, additional, Holmes, D, additional, Hoops, S, additional, and Harris, S, additional
- Published
- 1992
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18. The design and implementation of a longitudinal clinical competency assessment of physician assistant students.
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Hoops S, Barley G, and Chung A
- Published
- 2004
19. The Systems Biology Markup Language (SBML): Language Specification for Level 3 Version 2 Core Release 2
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Hucka Michael, Bergmann Frank T., Chaouiya Claudine, Dräger Andreas, Hoops Stefan, Keating Sarah M., König Matthias, Novère Nicolas Le, Myers Chris J., Olivier Brett G., Sahle Sven, Schaff James C., Sheriff Rahuman, Smith Lucian P., Waltemath Dagmar, Wilkinson Darren J., and Zhang Fengkai
- Subjects
systems biology markup language ,standards ,visualization ,representation ,Biotechnology ,TP248.13-248.65 - Abstract
Computational models can help researchers to interpret data, understand biological functions, and make quantitative predictions. The Systems Biology Markup Language (SBML) is a file format for representing computational models in a declarative form that different software systems can exchange. SBML is oriented towards describing biological processes of the sort common in research on a number of topics, including metabolic pathways, cell signaling pathways, and many others. By supporting SBML as an input/output format, different tools can all operate on an identical representation of a model, removing opportunities for translation errors and assuring a common starting point for analyses and simulations. This document provides the specification for Release 2 of Version 2 of SBML Level 3 Core. The specification defines the data structures prescribed by SBML as well as their encoding in XML, the eXtensible Markup Language. Release 2 corrects some errors and clarifies some ambiguities discovered in Release 1. This specification also defines validation rules that determine the validity of an SBML document, and provides many examples of models in SBML form. Other materials and software are available from the SBML project website at http://sbml.org/.
- Published
- 2019
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20. The effect of sugar cereal with and without a mixed meal on glycemic response in children with diabetes.
- Author
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Wang, Susan R., Chase, H. Peter, Garg, Satish K., Hoops, Sandy L., Harris, Mary A., Wang, S R, Chase, H P, Garg, S K, Hoops, S L, and Harris, M A
- Published
- 1991
21. Oral contraceptives and renal and retinal complications in young women with insulin-dependent diabetes mellitus.
- Author
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Garg SK, Chase HP, Marshall G, Hoops SL, Holmes DL, Jackson WE, Garg, S K, Chase, H P, Marshall, G, Hoops, S L, Holmes, D L, and Jackson, W E
- Abstract
Objective: To evaluate the effects of oral contraceptives (OCs) as a possible risk factor for early diabetic renal and/or retinal complications.Design: A retrospective case-control study.Setting: A university hospital diabetes clinic.Participants: Forty-three diabetic women who used OCs for 1 year or longer (mean, 3.4 years; range, 1.0 to 7.0 years) were compared with a computer-matched control group of 43 diabetic women who never used OCs.Main Outcome Measures: Hemoglobin A1c levels, albumin excretion rates, and mean retinopathy scores.Results: The mean +/- SEM age and duration of diabetes were 22.7 +/- 0.5 years (range, 17.1 to 30.5 years) and 13.8 +/- 0.8 years, respectively, for the study group. The mean longitudinal hemoglobin A1c values were similar for study subjects and control subjects. The final mean albumin excretion rates, reflecting diabetic renal damage, and the mean eye grades were not significantly different between the groups.Conclusions: The use of OCs among young women with insulin-dependent diabetes mellitus does not pose an additional risk for the development of early diabetic retinopathy and/or nephropathy. [ABSTRACT FROM AUTHOR]- Published
- 1994
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22. Glucose control and the renal and retinal complications of insulin-dependent diabetes.
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Chase, H P, Jackson, W E, Hoops, S L, Cockerham, R S, Archer, P G, and O'Brien, D
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AGE distribution ,ALBUMINURIA ,BLOOD sugar ,COMPARATIVE studies ,DIABETIC nephropathies ,DIABETIC retinopathy ,GLYCOSYLATED hemoglobin ,TYPE 1 diabetes ,LONGITUDINAL method ,RESEARCH methodology ,MEDICAL cooperation ,RESEARCH ,TIME ,EVALUATION research ,DISEASE complications - Abstract
Two hundred thirty subjects with insulin-dependent diabetes were followed up longitudinally by measuring glycohemoglobin values to relate glucose control with renal and retinal complications. Subjects with long-term poor control (glycohemoglobin values greater than 1.5 times the upper limit of normal) had 3.6 times the prevalence of microalbuminuria and 2.5 times the prevalence of level 3 to 6 retinopathy than that found in subjects with long-term good control (glycohemoglobin values within 1.33 times the upper limit of normal). Variables related to kidney damage were glucose control and, to a lesser degree, duration of diabetes. Variables related to eye disease were, in descending order of significance, duration of diabetes, glucose control, and age. No subject whose mean glycohemoglobin value was consistently within 1.1 times the upper limit of normal had retinopathy or microalbuminuria. In contrast, when the mean glycohemoglobin value was more than 1.5 times the upper limit of normal, 24 (29%) of 82 subjects had microalbuminuria and 30 (37%) of 82 had level 3 to 6 retinopathy. [ABSTRACT FROM AUTHOR]
- Published
- 1989
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23. Renal and retinal complications in insulin-dependent diabetes mellitus: the art of changing the outcome.
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Hoops S
- Published
- 1990
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24. The Systems Biology Markup Language (SBML): Language Specification for Level 3 Version 1 Core
- Author
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Hucka Michael, Bergmann Frank T., Dräger Andreas, Hoops Stefan, Keating Sarah M., Le Novère Nicolas, Myers Chris J., Olivier Brett G., Sahle Sven, Schaff James C., Smith Lucian P., Waltemath Dagmar, and Wilkinson Darren J.
- Subjects
sbml ,modeling ,standards ,Biotechnology ,TP248.13-248.65 - Abstract
Computational models can help researchers to interpret data, understand biological functions, and make quantitative predictions. The Systems Biology Markup Language (SBML) is a file format for representing computational models in a declarative form that different software systems can exchange. SBML is oriented towards describing biological processes of the sort common in research on a number of topics, including metabolic pathways, cell signaling pathways, and many others. By supporting SBML as an input/output format, different tools can all operate on an identical representation of a model, removing opportunities for translation errors and assuring a common starting point for analyses and simulations. This document provides the specification for Release 2 of Version 1 of SBML Level 3 Core. The specification defines the data structures prescribed by SBML, their encoding in XML (the eXtensible Markup Language), validation rules that determine the validity of an SBML document, and examples of models in SBML form. No design changes have been made to the description of models between Release 1 and Release 2; changes are restricted to the format of annotations, the correction of errata and the addition of clarifications. Other materials and software are available from the SBML project website at http://sbml.org/.
- Published
- 2018
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- View/download PDF
25. The Systems Biology Markup Language (SBML): Language Specification for Level 3 Version 2 Core
- Author
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Hucka Michael, Bergmann Frank T., Dräger Andreas, Hoops Stefan, Keating Sarah M., Le Novère Nicolas, Myers Chris J., Olivier Brett G., Sahle Sven, Schaff James C., Smith Lucian P., Waltemath Dagmar, and Wilkinson Darren J.
- Subjects
sbml ,modeling ,computational biology ,systems biology ,standards ,Biotechnology ,TP248.13-248.65 - Abstract
Computational models can help researchers to interpret data, understand biological functions, and make quantitative predictions. The Systems Biology Markup Language (SBML) is a file format for representing computational models in a declarative form that different software systems can exchange. SBML is oriented towards describing biological processes of the sort common in research on a number of topics, including metabolic pathways, cell signaling pathways, and many others. By supporting SBML as an input/output format, different tools can all operate on an identical representation of a model, removing opportunities for translation errors and assuring a common starting point for analyses and simulations. This document provides the specification for Version 2 of SBML Level 3 Core. The specification defines the data structures prescribed by SBML, their encoding in XML (the eXtensible Markup Language), validation rules that determine the validity of an SBML document, and examples of models in SBML form. The design of Version 2 differs from Version 1 principally in allowing new MathML constructs, making more child elements optional, and adding identifiers to all SBML elements instead of only selected elements. Other materials and software are available from the SBML project website at http://sbml.org/.
- Published
- 2018
- Full Text
- View/download PDF
26. Reactions of dicarbonyltitanocenes with 2-diazo-1,3-diketones: O-,N- versus O-,O- chelation and self-assembly of a novel heteroleptic Ti5O6-cage compound
- Author
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Nieuwenhuyzen, M., Schobert, R., Hampel, F., and Hoops, S.
- Published
- 2000
- Full Text
- View/download PDF
27. Systems Biology Markup Language (SBML) Level 2 Version 5: Structures and Facilities for Model Definitions
- Author
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Hucka Michael, Bergmann Frank T., Dräger Andreas, Hoops Stefan, Keating Sarah M., Le Novère Nicolas, Myers Chris J., Olivier Brett G., Sahle Sven, Schaff James C., Smith Lucian P., Waltemath Dagmar, and Wilkinson Darren J.
- Subjects
Biotechnology ,TP248.13-248.65 - Abstract
Computational models can help researchers to interpret data, understand biological function, and make quantitative predictions. The Systems Biology Markup Language (SBML) is a file format for representing computational models in a declarative form that can be exchanged between different software systems. SBML is oriented towards describing biological processes of the sort common in research on a number of topics, including metabolic pathways, cell signaling pathways, and many others. By supporting SBML as an input/output format, different tools can all operate on an identical representation of a model, removing opportunities for translation errors and assuring a common starting point for analyses and simulations. This document provides the specification for Version 5 of SBML Level 2. The specification defines the data structures prescribed by SBML as well as their encoding in XML, the eXtensible Markup Language. This specification also defines validation rules that determine the validity of an SBML document, and provides many examples of models in SBML form. Other materials and software are available from the SBML project web site, http://sbml.org/.
- Published
- 2015
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- View/download PDF
28. SBML Level 3 package: Hierarchical Model Composition, Version 1 Release 3
- Author
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Smith Lucian P., Hucka Michael, Hoops Stefan, Finney Andrew, Ginkel Martin, Myers Chris J., Moraru Ion, and Liebermeister Wolfram
- Subjects
Biotechnology ,TP248.13-248.65 - Abstract
Constructing a model in a hierarchical fashion is a natural approach to managing model complexity, and offers additional opportunities such as the potential to re-use model components. The SBML Level 3 Version 1 Core specification does not directly provide a mechanism for defining hierarchical models, but it does provide a mechanism for SBML packages to extend the Core specification and add additional syntactical constructs. The SBML Hierarchical Model Composition package for SBML Level 3 adds the necessary features to SBML to support hierarchical modeling. The package enables a modeler to include submodels within an enclosing SBML model, delete unneeded or redundant elements of that submodel, replace elements of that submodel with element of the containing model, and replace elements of the containing model with elements of the submodel. In addition, the package defines an optional “port” construct, allowing a model to be defined with suggested interfaces between hierarchical components; modelers can chose to use these interfaces, but they are not required to do so and can still interact directly with model elements if they so chose. Finally, the SBML Hierarchical Model Composition package is defined in such a way that a hierarchical model can be “flattened” to an equivalent, non-hierarchical version that uses only plain SBML constructs, thus enabling software tools that do not yet support hierarchy to nevertheless work with SBML hierarchical models.
- Published
- 2015
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29. The Systems Biology Markup Language (SBML): Language Specification for Level 3 Version 1 Core
- Author
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Hucka Michael, Bergmann Frank T., Hoops Stefan, Keating Sarah M., Sahle Sven, Schaff James C., Smith Lucian P., and Wilkinson Darren J.
- Subjects
Biotechnology ,TP248.13-248.65 - Abstract
Computational models can help researchers to interpret data, understand biological function, and make quantitative predictions. The Systems Biology Markup Language (SBML) is a file format for representing computational models in a declarative form that can be exchanged between different software systems. SBML is oriented towards describing biological processes of the sort common in research on a number of topics, including metabolic pathways, cell signaling pathways, and many others. By supporting SBML as an input/output format, different tools can all operate on an identical representation of a model, removing opportunities for translation errors and assuring a common starting point for analyses and simulations. This document provides the specification for Version 1 of SBML Level 3 Core. The specification defines the data structures prescribed by SBML as well as their encoding in XML, the eXtensible Markup Language. This specification also defines validation rules that determine the validity of an SBML document, and provides many examples of models in SBML form. Other materials and software are available from the SBML project web site, http://sbml.org/.
- Published
- 2015
- Full Text
- View/download PDF
30. ModelMage: a tool for automatic model generation, selection and management
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Flöttmann M, Hoops S, Klipp E, Pedro Mendes, and Schaber J
- Subjects
Automation ,Kinetics ,Models, Genetic ,Species Specificity ,Discriminant Analysis ,Computer Simulation ,Documentation ,Selection, Genetic ,Software - Abstract
Mathematical modeling of biological systems usually involves implementing, simulating, and discriminating several candidate models that represent alternative hypotheses. Generating and managing these candidate models is a tedious and difficult task and can easily lead to errors. ModelMage is a tool that facilitates management of candidate models. It is designed for the easy and rapid development, generation, simulation, and discrimination of candidate models. The main idea of the program is to automatically create a defined set of model alternatives from a single master model. The user provides only one SBML-model and a set of directives from which the candidate models are created by leaving out species, modifiers or reactions. After generating models the software can automatically fit all these models to the data and provides a ranking for model selection, in case data is available. In contrast to other model generation programs, ModelMage aims at generating only a limited set of models that the user can precisely define. ModelMage uses COPASI as a simulation and optimization engine. Thus, all simulation and optimization features of COPASI are readily incorporated. ModelMage can be downloaded from http://sysbio.molgen.mpg.de/modelmage and is distributed as free software.
31. ChemInform Abstract: ENERGETICS OF CARBONYL ADDITION AND ELIMINATION. METHOXIDE ION WITH ESTERS
- Author
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HOOPS, S. C., primary, VAN OOSTERDIEP, J., additional, and SCHOWEN, R. L., additional
- Published
- 1984
- Full Text
- View/download PDF
32. Can professionalism be assessed through the use of an objective structured clinical examination? A three program experiment.
- Author
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Kenney-Moore P, Landel G, and Hoops S
- Published
- 2008
- Full Text
- View/download PDF
33. Identification of Pigment Epithelium-Derived Factor Protein Forms with Distinct Activities on Tumor Cell Lines
- Author
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Subramanian, P., Deshpande, M., Locatelli-Hoops, S., Moghaddam-Taaheri, S., Gutierrez, D., P. Fitzgerald, D., Guerrier, S., Rapp, M., Notario, V., and P. Becerra, S.
- Abstract
Purpose. Pigment epithelium-derived factor (PEDF) is a multifunctional serpin. The purpose of this study is to identify PEDF protein forms and investigate their biological activities on tumor cell lines. Methods. Recombinant human PEDF proteins were purified by cation- and anion-exchange column chromatography. They were subjected to SDS-PAGE, IEF, deglycosylation, heparin affinity chromatography, and limited proteolysis. Cell viability, real-time electrical impedance of cells, and wound healing assays were performed using bladder and breast cancer cell lines, rat retinal R28, and human ARPE-19 cells. Results. Two PEDF protein peaks were identified after anion-exchange column chromatography: PEDF-1 eluting with lower ionic strength than PEDF-2. PEDF-1 had higher pI value and lower apparent molecular weight than PEDF-2. Both PEDF forms were glycosylated, bound to heparin, and had identical patterns by limited proteolysis. However, PEDF-2 emerged as being highly potent in lowering cell viability in all tumor cell lines tested, and in inhibiting tumor and ARPE-19 cell migration. In contrast, PEDF-1 minimally affected tumor cell viability and cell migration but protected R28 cells against death caused by serum starvation. Conclusion. Two distinct biochemical forms of PEDF varying in overall charge have distinct biological effects on tumor cell viability and migration. The existence of PEDF forms may explain the multifunctional modality of PEDF.
- Published
- 2012
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34. SBML Level 3: an extensible format for the exchange and reuse of biological models
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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)
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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.
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- 2020
35. Novel multi-cluster workflow system to support real-time HPC-enabled epidemic science: Investigating the impact of vaccine acceptance on COVID-19 spread.
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Bhattacharya P, Machi D, Chen J, Hoops S, Lewis B, Mortveit H, Venkatramanan S, Wilson ML, Marathe A, Porebski P, Klahn B, Outten J, Vullikanti A, Xie D, Adiga A, Brown S, Barrett C, and Marathe M
- Abstract
We present MacKenzie, a HPC-driven multi-cluster workflow system that was used repeatedly to configure and execute fine-grained US national-scale epidemic simulation models during the COVID-19 pandemic. Mackenzie supported federal and Virginia policymakers, in real-time, for a large number of "what-if" scenarios during the COVID-19 pandemic, and continues to be used to answer related questions as COVID-19 transitions to the endemic stage of the disease. MacKenzie is a novel HPC meta-scheduler that can execute US-scale simulation models and associated workflows that typically present significant big data challenges. The meta-scheduler optimizes the total execution time of simulations in the workflow, and helps improve overall human productivity. As an exemplar of the kind of studies that can be conducted using Mackenzie, we present a modeling study to understand the impact of vaccine-acceptance in controlling the spread of COVID-19 in the US. We use a 288 million node synthetic social contact network (digital twin) spanning all 50 US states plus Washington DC, comprised of 3300 counties, with 12 billion daily interactions. The highly-resolved agent-based model used for the epidemic simulations uses realistic information about disease progression, vaccine uptake, production schedules, acceptance trends, prevalence, and social distancing guidelines. Computational experiments show that, for the simulation workload discussed above, MacKenzie is able to scale up well to 10K CPU cores. Our modeling results show that, when compared to faster and accelerating vaccinations, slower vaccination rates due to vaccine hesitancy cause averted infections to drop from 6.7M to 4.5M, and averted total deaths to drop from 39.4K to 28.2K across the US. This occurs despite the fact that the final vaccine coverage is the same in both scenarios. We also find that if vaccine acceptance could be increased by 10% in all states, averted infections could be increased from 4.5M to 4.7M (a 4.4% improvement) and total averted deaths could be increased from 28.2K to 29.9K (a 6% improvement) nationwide., Competing Interests: Parantapa Bhattacharya reports financial support was provided by Centers for Disease Control and Prevention. Parantapa Bhattacharya reports financial support was provided by Virginia Department of Health. Parantapa Bhattacharya reports financial support was provided by National Science Foundation. Parantapa Bhattacharya reports financial support was provided by National Institutes of Health.
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- 2024
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36. Role of heterogeneity: National scale data-driven agent-based modeling for the US COVID-19 Scenario Modeling Hub.
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Chen J, Bhattacharya P, Hoops S, Machi D, Adiga A, Mortveit H, Venkatramanan S, Lewis B, and Marathe M
- Abstract
UVA-EpiHiper is a national scale agent-based model to support the US COVID-19 Scenario Modeling Hub (SMH). UVA-EpiHiper uses a detailed representation of the underlying social contact network along with data measured during the course of the pandemic to initialize and calibrate the model. In this paper, we study the role of heterogeneity on model complexity and resulting epidemic dynamics using UVA-EpiHiper. We discuss various sources of heterogeneity that we encounter in the use of UVA-EpiHiper to support modeling and analysis of epidemic dynamics under various scenarios. We also discuss how this affects model complexity and computational complexity of the corresponding simulations. Using round 13 of the SMH as an example, we discuss how UVA-EpiHiper was initialized and calibrated. We then discuss how the detailed output produced by UVA-EpiHiper can be analyzed to obtain interesting insights. We find that despite the complexity in the model, the software, and the computation incurred to an agent-based model in scenario modeling, it is capable of capturing various heterogeneities of real-world systems, especially those in networks and behaviors, and enables analyzing heterogeneities in epidemiological outcomes between different demographic, geographic, and social cohorts. In applying UVA-EpiHiper to round 13 scenario modeling, we find that disease outcomes are different between and within states, and between demographic groups, which can be attributed to heterogeneities in population demographics, network structures, and initial immunity., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)
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- 2024
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37. Potential impact of annual vaccination with reformulated COVID-19 vaccines: Lessons from the US COVID-19 scenario modeling hub.
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Jung SM, Loo SL, Howerton E, Contamin L, Smith CP, Carcelén EC, Yan K, Bents SJ, Levander J, Espino J, Lemaitre JC, Sato K, McKee CD, Hill AL, Chinazzi M, Davis JT, Mu K, Vespignani A, Rosenstrom ET, Rodriguez-Cartes SA, Ivy JS, Mayorga ME, Swann JL, España G, Cavany S, Moore SM, Perkins TA, Chen S, Paul R, Janies D, Thill JC, Srivastava A, Aawar MA, Bi K, Bandekar SR, Bouchnita A, Fox SJ, Meyers LA, Porebski P, Venkatramanan S, Adiga A, Hurt B, Klahn B, Outten J, Chen J, Mortveit H, Wilson A, Hoops S, Bhattacharya P, Machi D, Vullikanti A, Lewis B, Marathe M, Hochheiser H, Runge MC, Shea K, Truelove S, Viboud C, and Lessler J
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- Humans, United States epidemiology, Aged, Middle Aged, Adult, Adolescent, Young Adult, Child, Aged, 80 and over, Male, COVID-19 Vaccines immunology, COVID-19 prevention & control, COVID-19 epidemiology, COVID-19 immunology, Hospitalization statistics & numerical data, SARS-CoV-2 immunology, Vaccination
- Abstract
Background: Coronavirus Disease 2019 (COVID-19) continues to cause significant hospitalizations and deaths in the United States. Its continued burden and the impact of annually reformulated vaccines remain unclear. Here, we present projections of COVID-19 hospitalizations and deaths in the United States for the next 2 years under 2 plausible assumptions about immune escape (20% per year and 50% per year) and 3 possible CDC recommendations for the use of annually reformulated vaccines (no recommendation, vaccination for those aged 65 years and over, vaccination for all eligible age groups based on FDA approval)., Methods and Findings: The COVID-19 Scenario Modeling Hub solicited projections of COVID-19 hospitalization and deaths between April 15, 2023 and April 15, 2025 under 6 scenarios representing the intersection of considered levels of immune escape and vaccination. Annually reformulated vaccines are assumed to be 65% effective against symptomatic infection with strains circulating on June 15 of each year and to become available on September 1. Age- and state-specific coverage in recommended groups was assumed to match that seen for the first (fall 2021) COVID-19 booster. State and national projections from 8 modeling teams were ensembled to produce projections for each scenario and expected reductions in disease outcomes due to vaccination over the projection period. From April 15, 2023 to April 15, 2025, COVID-19 is projected to cause annual epidemics peaking November to January. In the most pessimistic scenario (high immune escape, no vaccination recommendation), we project 2.1 million (90% projection interval (PI) [1,438,000, 4,270,000]) hospitalizations and 209,000 (90% PI [139,000, 461,000]) deaths, exceeding pre-pandemic mortality of influenza and pneumonia. In high immune escape scenarios, vaccination of those aged 65+ results in 230,000 (95% confidence interval (CI) [104,000, 355,000]) fewer hospitalizations and 33,000 (95% CI [12,000, 54,000]) fewer deaths, while vaccination of all eligible individuals results in 431,000 (95% CI: 264,000-598,000) fewer hospitalizations and 49,000 (95% CI [29,000, 69,000]) fewer deaths., Conclusions: COVID-19 is projected to be a significant public health threat over the coming 2 years. Broad vaccination has the potential to substantially reduce the burden of this disease, saving tens of thousands of lives each year., Competing Interests: JE is president of General Biodefense LLC, a private consulting group for public health informatics, and has interest in READE.ai, a medical artificial intelligence solutions company. MR reports stock ownership in Becton Dickinson & Co., which manufactures medical equipment used in COVID-19 testing, vaccination, and treatment. JL has served as an expert witness on cases where the likely length of the pandemic was of issue. The remaining authors declare no competing interests., (Copyright: © 2024 Jung et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
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- 2024
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38. Evaluation of the US COVID-19 Scenario Modeling Hub for informing pandemic response under uncertainty.
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Howerton E, Contamin L, Mullany LC, Qin M, Reich NG, Bents S, Borchering RK, Jung SM, Loo SL, Smith CP, Levander J, Kerr J, Espino J, van Panhuis WG, Hochheiser H, Galanti M, Yamana T, Pei S, Shaman J, Rainwater-Lovett K, Kinsey M, Tallaksen K, Wilson S, Shin L, Lemaitre JC, Kaminsky J, Hulse JD, Lee EC, McKee CD, Hill A, Karlen D, Chinazzi M, Davis JT, Mu K, Xiong X, Pastore Y Piontti A, Vespignani A, Rosenstrom ET, Ivy JS, Mayorga ME, Swann JL, España G, Cavany S, Moore S, Perkins A, Hladish T, Pillai A, Ben Toh K, Longini I Jr, Chen S, Paul R, Janies D, Thill JC, Bouchnita A, Bi K, Lachmann M, Fox SJ, Meyers LA, Srivastava A, Porebski P, Venkatramanan S, Adiga A, Lewis B, Klahn B, Outten J, Hurt B, Chen J, Mortveit H, Wilson A, Marathe M, Hoops S, Bhattacharya P, Machi D, Cadwell BL, Healy JM, Slayton RB, Johansson MA, Biggerstaff M, Truelove S, Runge MC, Shea K, Viboud C, and Lessler J
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- Humans, Pandemics prevention & control, SARS-CoV-2, Uncertainty, COVID-19 epidemiology
- Abstract
Our ability to forecast epidemics far into the future is constrained by the many complexities of disease systems. Realistic longer-term projections may, however, be possible under well-defined scenarios that specify the future state of critical epidemic drivers. Since December 2020, the U.S. COVID-19 Scenario Modeling Hub (SMH) has convened multiple modeling teams to make months ahead projections of SARS-CoV-2 burden, totaling nearly 1.8 million national and state-level projections. Here, we find SMH performance varied widely as a function of both scenario validity and model calibration. We show scenarios remained close to reality for 22 weeks on average before the arrival of unanticipated SARS-CoV-2 variants invalidated key assumptions. An ensemble of participating models that preserved variation between models (using the linear opinion pool method) was consistently more reliable than any single model in periods of valid scenario assumptions, while projection interval coverage was near target levels. SMH projections were used to guide pandemic response, illustrating the value of collaborative hubs for longer-term scenario projections., (© 2023. The Author(s).)
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- 2023
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39. Potential impact of annual vaccination with reformulated COVID-19 vaccines: lessons from the U.S. COVID-19 Scenario Modeling Hub.
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Jung SM, Loo SL, Howerton E, Contamin L, Smith CP, Carcelén EC, Yan K, Bents SJ, Levander J, Espino J, Lemaitre JC, Sato K, McKee CD, Hill AL, Chinazzi M, Davis JT, Mu K, Vespignani A, Rosenstrom ET, Rodriguez-Cartes SA, Ivy JS, Mayorga ME, Swann JL, España G, Cavany S, Moore SM, Perkins A, Chen S, Paul R, Janies D, Thill JC, Srivastava A, Al Aawar M, Bi K, Bandekar SR, Bouchnita A, Fox SJ, Meyers LA, Porebski P, Venkatramanan S, Adiga A, Hurt B, Klahn B, Outten J, Chen J, Mortveit H, Wilson A, Hoops S, Bhattacharya P, Machi D, Vullikanti A, Lewis B, Marathe M, Hochheiser H, Runge MC, Shea K, Truelove S, Viboud C, and Lessler J
- Abstract
Importance: COVID-19 continues to cause significant hospitalizations and deaths in the United States. Its continued burden and the impact of annually reformulated vaccines remain unclear., Objective: To project COVID-19 hospitalizations and deaths from April 2023-April 2025 under two plausible assumptions about immune escape (20% per year and 50% per year) and three possible CDC recommendations for the use of annually reformulated vaccines (no vaccine recommendation, vaccination for those aged 65+, vaccination for all eligible groups)., Design: The COVID-19 Scenario Modeling Hub solicited projections of COVID-19 hospitalization and deaths between April 15, 2023-April 15, 2025 under six scenarios representing the intersection of considered levels of immune escape and vaccination. State and national projections from eight modeling teams were ensembled to produce projections for each scenario., Setting: The entire United States., Participants: None., Exposure: Annually reformulated vaccines assumed to be 65% effective against strains circulating on June 15 of each year and to become available on September 1. Age and state specific coverage in recommended groups was assumed to match that seen for the first (fall 2021) COVID-19 booster., Main Outcomes and Measures: Ensemble estimates of weekly and cumulative COVID-19 hospitalizations and deaths. Expected relative and absolute reductions in hospitalizations and deaths due to vaccination over the projection period., Results: From April 15, 2023-April 15, 2025, COVID-19 is projected to cause annual epidemics peaking November-January. In the most pessimistic scenario (high immune escape, no vaccination recommendation), we project 2.1 million (90% PI: 1,438,000-4,270,000) hospitalizations and 209,000 (90% PI: 139,000-461,000) deaths, exceeding pre-pandemic mortality of influenza and pneumonia. In high immune escape scenarios, vaccination of those aged 65+ results in 230,000 (95% CI: 104,000-355,000) fewer hospitalizations and 33,000 (95% CI: 12,000-54,000) fewer deaths, while vaccination of all eligible individuals results in 431,000 (95% CI: 264,000-598,000) fewer hospitalizations and 49,000 (95% CI: 29,000-69,000) fewer deaths., Conclusion and Relevance: COVID-19 is projected to be a significant public health threat over the coming two years. Broad vaccination has the potential to substantially reduce the burden of this disease., Competing Interests: Conflict of Interest Disclosures J. Espino is president of General Biodefense LLC, a private consulting group for public health informatics, and has interest in READE.ai, a medical artificial intelligence solutions company. M. Runge reports stock ownership in Becton Dickinson & Co., which manufactures medical equipment used in COVID-19 testing, vaccination, and treatment. J. Lessler has served as an expert witness on cases where the likely length of the pandemic was of issue.
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- 2023
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40. Informing pandemic response in the face of uncertainty. An evaluation of the U.S. COVID-19 Scenario Modeling Hub .
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Howerton E, Contamin L, Mullany LC, Qin M, Reich NG, Bents S, Borchering RK, Jung SM, Loo SL, Smith CP, Levander J, Kerr J, Espino J, van Panhuis WG, Hochheiser H, Galanti M, Yamana T, Pei S, Shaman J, Rainwater-Lovett K, Kinsey M, Tallaksen K, Wilson S, Shin L, Lemaitre JC, Kaminsky J, Hulse JD, Lee EC, McKee C, Hill A, Karlen D, Chinazzi M, Davis JT, Mu K, Xiong X, Piontti APY, Vespignani A, Rosenstrom ET, Ivy JS, Mayorga ME, Swann JL, España G, Cavany S, Moore S, Perkins A, Hladish T, Pillai A, Toh KB, Longini I Jr, Chen S, Paul R, Janies D, Thill JC, Bouchnita A, Bi K, Lachmann M, Fox S, Meyers LA, Srivastava A, Porebski P, Venkatramanan S, Adiga A, Lewis B, Klahn B, Outten J, Hurt B, Chen J, Mortveit H, Wilson A, Marathe M, Hoops S, Bhattacharya P, Machi D, Cadwell BL, Healy JM, Slayton RB, Johansson MA, Biggerstaff M, Truelove S, Runge MC, Shea K, Viboud C, and Lessler J
- Abstract
Our ability to forecast epidemics more than a few weeks into the future is constrained by the complexity of disease systems, our limited ability to measure the current state of an epidemic, and uncertainties in how human action will affect transmission. Realistic longer-term projections (spanning more than a few weeks) may, however, be possible under defined scenarios that specify the future state of critical epidemic drivers, with the additional benefit that such scenarios can be used to anticipate the comparative effect of control measures. Since December 2020, the U.S. COVID-19 Scenario Modeling Hub (SMH) has convened multiple modeling teams to make 6-month ahead projections of the number of SARS-CoV-2 cases, hospitalizations and deaths. The SMH released nearly 1.8 million national and state-level projections between February 2021 and November 2022. SMH performance varied widely as a function of both scenario validity and model calibration. Scenario assumptions were periodically invalidated by the arrival of unanticipated SARS-CoV-2 variants, but SMH still provided projections on average 22 weeks before changes in assumptions (such as virus transmissibility) invalidated scenarios and their corresponding projections. During these periods, before emergence of a novel variant, a linear opinion pool ensemble of contributed models was consistently more reliable than any single model, and projection interval coverage was near target levels for the most plausible scenarios (e.g., 79% coverage for 95% projection interval). SMH projections were used operationally to guide planning and policy at different stages of the pandemic, illustrating the value of the hub approach for long-term scenario projections.
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- 2023
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41. Data-driven scalable pipeline using national agent-based models for real-time pandemic response and decision support.
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Bhattacharya P, Chen J, Hoops S, Machi D, Lewis B, Venkatramanan S, Wilson ML, Klahn B, Adiga A, Hurt B, Outten J, Adiga A, Warren A, Baek YY, Porebski P, Marathe A, Xie D, Swarup S, Vullikanti A, Mortveit H, Eubank S, Barrett CL, and Marathe M
- Abstract
This paper describes an integrated, data-driven operational pipeline based on national agent-based models to support federal and state-level pandemic planning and response. The pipeline consists of ( i ) an automatic semantic-aware scheduling method that coordinates jobs across two separate high performance computing systems; ( ii ) a data pipeline to collect, integrate and organize national and county-level disaggregated data for initialization and post-simulation analysis; ( iii ) a digital twin of national social contact networks made up of 288 Million individuals and 12.6 Billion time-varying interactions covering the US states and DC; ( iv ) an extension of a parallel agent-based simulation model to study epidemic dynamics and associated interventions. This pipeline can run 400 replicates of national runs in less than 33 h, and reduces the need for human intervention, resulting in faster turnaround times and higher reliability and accuracy of the results. Scientifically, the work has led to significant advances in real-time epidemic sciences., Competing Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (© The Author(s) 2022.)
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- 2023
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42. Impact of SARS-CoV-2 vaccination of children ages 5-11 years on COVID-19 disease burden and resilience to new variants in the United States, November 2021-March 2022: A multi-model study.
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Borchering RK, Mullany LC, Howerton E, Chinazzi M, Smith CP, Qin M, Reich NG, Contamin L, Levander J, Kerr J, Espino J, Hochheiser H, Lovett K, Kinsey M, Tallaksen K, Wilson S, Shin L, Lemaitre JC, Hulse JD, Kaminsky J, Lee EC, Hill AL, Davis JT, Mu K, Xiong X, Pastore Y Piontti A, Vespignani A, Srivastava A, Porebski P, Venkatramanan S, Adiga A, Lewis B, Klahn B, Outten J, Hurt B, Chen J, Mortveit H, Wilson A, Marathe M, Hoops S, Bhattacharya P, Machi D, Chen S, Paul R, Janies D, Thill JC, Galanti M, Yamana T, Pei S, Shaman J, España G, Cavany S, Moore S, Perkins A, Healy JM, Slayton RB, Johansson MA, Biggerstaff M, Shea K, Truelove SA, Runge MC, Viboud C, and Lessler J
- Abstract
Background: The COVID-19 Scenario Modeling Hub convened nine modeling teams to project the impact of expanding SARS-CoV-2 vaccination to children aged 5-11 years on COVID-19 burden and resilience against variant strains., Methods: Teams contributed state- and national-level weekly projections of cases, hospitalizations, and deaths in the United States from September 12, 2021 to March 12, 2022. Four scenarios covered all combinations of 1) vaccination (or not) of children aged 5-11 years (starting November 1, 2021), and 2) emergence (or not) of a variant more transmissible than the Delta variant (emerging November 15, 2021). Individual team projections were linearly pooled. The effect of childhood vaccination on overall and age-specific outcomes was estimated using meta-analyses., Findings: Assuming that a new variant would not emerge, all-age COVID-19 outcomes were projected to decrease nationally through mid-March 2022. In this setting, vaccination of children 5-11 years old was associated with reductions in projections for all-age cumulative cases (7.2%, mean incidence ratio [IR] 0.928, 95% confidence interval [CI] 0.880-0.977), hospitalizations (8.7%, mean IR 0.913, 95% CI 0.834-0.992), and deaths (9.2%, mean IR 0.908, 95% CI 0.797-1.020) compared with scenarios without childhood vaccination. Vaccine benefits increased for scenarios including a hypothesized more transmissible variant, assuming similar vaccine effectiveness. Projected relative reductions in cumulative outcomes were larger for children than for the entire population. State-level variation was observed., Interpretation: Given the scenario assumptions (defined before the emergence of Omicron), expanding vaccination to children 5-11 years old would provide measurable direct benefits, as well as indirect benefits to the all-age U.S. population, including resilience to more transmissible variants., Funding: Various (see acknowledgments)., Competing Interests: JL has served as an expert witness on cases where the likely length of the pandemic was of issue. MCR reports stock ownership in Becton Dickinson & Co., which manufactures medical equipment used in COVID-19 testing, vaccination, and treatment. JS and Columbia University disclose partial ownership of SK Analytics. JS discloses consulting for BNI. There are no other competing interests to declare.
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- 2023
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43. COVID's collateral damage: likelihood of measles resurgence in the United States.
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Thakur M, Zhou R, Mohan M, Marathe A, Chen J, Hoops S, Machi D, Lewis B, and Vullikanti A
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- Child, Communicable Disease Control, Humans, Measles-Mumps-Rubella Vaccine, Pandemics, United States epidemiology, COVID-19 epidemiology, Measles epidemiology, Measles prevention & control
- Abstract
Background: Lockdowns imposed throughout the US to control the COVID-19 pandemic led to a decline in all routine immunizations rates, including the MMR (measles, mumps, rubella) vaccine. It is feared that post-lockdown, these reduced MMR rates will lead to a resurgence of measles., Methods: To measure the potential impact of reduced MMR vaccination rates on measles outbreak, this research examines several counterfactual scenarios in pre-COVID-19 and post-COVID-19 era. An agent-based modeling framework is used to simulate the spread of measles on a synthetic yet realistic social network of Virginia. The change in vulnerability of various communities to measles due to reduced MMR rate is analyzed., Results: Results show that a decrease in vaccination rate [Formula: see text] has a highly non-linear effect on the number of measles cases and this effect grows exponentially beyond a threshold [Formula: see text]. At low vaccination rates, faster isolation of cases and higher compliance to home-isolation are not enough to control the outbreak. The overall impact on urban and rural counties is proportional to their population size but the younger children, African Americans and American Indians are disproportionately infected and hence are more vulnerable to the reduction in the vaccination rate., Conclusions: At low vaccination rates, broader interventions are needed to control the outbreak. Identifying the cause of the decline in vaccination rates (e.g., low income) can help design targeted interventions which can dampen the disproportional impact on more vulnerable populations and reduce disparities in health. Per capita burden of the potential measles resurgence is equivalent in the rural and the urban communities and hence proportionally equitable public health resources should be allocated to rural regions., (© 2022. The Author(s).)
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- 2022
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44. BioSimulators: a central registry of simulation engines and services for recommending specific tools.
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Shaikh B, Smith LP, Vasilescu D, Marupilla G, Wilson M, Agmon E, Agnew H, Andrews SS, Anwar A, Beber ME, Bergmann FT, Brooks D, Brusch L, Calzone L, Choi K, Cooper J, Detloff J, Drawert B, Dumontier M, Ermentrout GB, Faeder JR, Freiburger AP, Fröhlich F, Funahashi A, Garny A, Gennari JH, Gleeson P, Goelzer A, Haiman Z, Hasenauer J, Hellerstein JL, Hermjakob H, Hoops S, Ison JC, Jahn D, Jakubowski HV, Jordan R, Kalaš M, König M, Liebermeister W, Sheriff RSM, Mandal S, McDougal R, Medley JK, Mendes P, Müller R, Myers CJ, Naldi A, Nguyen TVN, Nickerson DP, Olivier BG, Patoliya D, Paulevé L, Petzold LR, Priya A, Rampadarath AK, Rohwer JM, Saglam AS, Singh D, Sinha A, Snoep J, Sorby H, Spangler R, Starruß J, Thomas PJ, van Niekerk D, Weindl D, Zhang F, Zhukova A, Goldberg AP, Schaff JC, Blinov ML, Sauro HM, Moraru II, and Karr JR
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- Humans, Bioengineering, Models, Biological, Registries, Research Personnel, Computer Simulation, Software
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Computational models have great potential to accelerate bioscience, bioengineering, and medicine. However, it remains challenging to reproduce and reuse simulations, in part, because the numerous formats and methods for simulating various subsystems and scales remain siloed by different software tools. For example, each tool must be executed through a distinct interface. To help investigators find and use simulation tools, we developed BioSimulators (https://biosimulators.org), a central registry of the capabilities of simulation tools and consistent Python, command-line and containerized interfaces to each version of each tool. The foundation of BioSimulators is standards, such as CellML, SBML, SED-ML and the COMBINE archive format, and validation tools for simulation projects and simulation tools that ensure these standards are used consistently. To help modelers find tools for particular projects, we have also used the registry to develop recommendation services. We anticipate that BioSimulators will help modelers exchange, reproduce, and combine simulations., (© The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research.)
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- 2022
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45. Projected resurgence of COVID-19 in the United States in July-December 2021 resulting from the increased transmissibility of the Delta variant and faltering vaccination.
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Truelove S, Smith CP, Qin M, Mullany LC, Borchering RK, Lessler J, Shea K, Howerton E, Contamin L, Levander J, Kerr J, Hochheiser H, Kinsey M, Tallaksen K, Wilson S, Shin L, Rainwater-Lovett K, Lemairtre JC, Dent J, Kaminsky J, Lee EC, Perez-Saez J, Hill A, Karlen D, Chinazzi M, Davis JT, Mu K, Xiong X, Pastore Y Piontti A, Vespignani A, Srivastava A, Porebski P, Venkatramanan S, Adiga A, Lewis B, Klahn B, Outten J, Orr M, Harrison G, Hurt B, Chen J, Vullikanti A, Marathe M, Hoops S, Bhattacharya P, Machi D, Chen S, Paul R, Janies D, Thill JC, Galanti M, Yamana TK, Pei S, Shaman JL, Healy JM, Slayton RB, Biggerstaff M, Johansson MA, Runge MC, and Viboud C
- Subjects
- Humans, Pandemics prevention & control, United States epidemiology, Vaccination, COVID-19 epidemiology, COVID-19 prevention & control, SARS-CoV-2 genetics
- Abstract
In Spring 2021, the highly transmissible SARS-CoV-2 Delta variant began to cause increases in cases, hospitalizations, and deaths in parts of the United States. At the time, with slowed vaccination uptake, this novel variant was expected to increase the risk of pandemic resurgence in the US in summer and fall 2021. As part of the COVID-19 Scenario Modeling Hub, an ensemble of nine mechanistic models produced 6-month scenario projections for July-December 2021 for the United States. These projections estimated substantial resurgences of COVID-19 across the US resulting from the more transmissible Delta variant, projected to occur across most of the US, coinciding with school and business reopening. The scenarios revealed that reaching higher vaccine coverage in July-December 2021 reduced the size and duration of the projected resurgence substantially, with the expected impacts was largely concentrated in a subset of states with lower vaccination coverage. Despite accurate projection of COVID-19 surges occurring and timing, the magnitude was substantially underestimated 2021 by the models compared with the of the reported cases, hospitalizations, and deaths occurring during July-December, highlighting the continued challenges to predict the evolving COVID-19 pandemic. Vaccination uptake remains critical to limiting transmission and disease, particularly in states with lower vaccination coverage. Higher vaccination goals at the onset of the surge of the new variant were estimated to avert over 1.5 million cases and 21,000 deaths, although may have had even greater impacts, considering the underestimated resurgence magnitude from the model., Competing Interests: ST, CS, MQ, LM, RB, KS, EH, LC, JL, JK, HH, MK, KT, SW, LS, KR, JL, JD, JK, EL, JP, AH, DK, MC, JD, KM, XX, AP, AV, AS, PP, SV, AA, BL, BK, JO, MO, GH, BH, JC, AV, MM, SH, PB, DM, SC, RP, DJ, JT, MG, TY, SP, JH, RS, MB, MJ, CV No competing interests declared, JL has served as an expert witness on cases where the likely length of the pandemic was of issue, JS and Columbia University disclose partial ownership of SK Analytics. Discloses consulting for BNI, MR reports stock ownership in Becton Dickinson & Co, which manufactures medical equipment used in COVID testing, vaccination, and treatment
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- 2022
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46. Impact of SARS-CoV-2 vaccination of children ages 5-11 years on COVID-19 disease burden and resilience to new variants in the United States, November 2021-March 2022: a multi-model study.
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Borchering RK, Mullany LC, Howerton E, Chinazzi M, Smith CP, Qin M, Reich NG, Contamin L, Levander J, Kerr J, Espino J, Hochheiser H, Lovett K, Kinsey M, Tallaksen K, Wilson S, Shin L, Lemaitre JC, Hulse JD, Kaminsky J, Lee EC, Davis JT, Mu K, Xiong X, Piontti APY, Vespignani A, Srivastava A, Porebski P, Venkatramanan S, Adiga A, Lewis B, Klahn B, Outten J, Hurt B, Chen J, Mortveit H, Wilson A, Marathe M, Hoops S, Bhattacharya P, Machi D, Chen S, Paul R, Janies D, Thill JC, Galanti M, Yamana T, Pei S, Shaman J, Espana G, Cavany S, Moore S, Perkins A, Healy JM, Slayton RB, Johansson MA, Biggerstaff M, Shea K, Truelove SA, Runge MC, Viboud C, and Lessler J
- Abstract
Background: SARS-CoV-2 vaccination of persons aged 12 years and older has reduced disease burden in the United States. The COVID-19 Scenario Modeling Hub convened multiple modeling teams in September 2021 to project the impact of expanding vaccine administration to children 5-11 years old on anticipated COVID-19 burden and resilience against variant strains., Methods: Nine modeling teams contributed state- and national-level projections for weekly counts of cases, hospitalizations, and deaths in the United States for the period September 12, 2021 to March 12, 2022. Four scenarios covered all combinations of: 1) presence vs. absence of vaccination of children ages 5-11 years starting on November 1, 2021; and 2) continued dominance of the Delta variant vs. emergence of a hypothetical more transmissible variant on November 15, 2021. Individual team projections were combined using linear pooling. The effect of childhood vaccination on overall and age-specific outcomes was estimated by meta-analysis approaches., Findings: Absent a new variant, COVID-19 cases, hospitalizations, and deaths among all ages were projected to decrease nationally through mid-March 2022. Under a set of specific assumptions, models projected that vaccination of children 5-11 years old was associated with reductions in all-age cumulative cases (7.2%, mean incidence ratio [IR] 0.928, 95% confidence interval [CI] 0.880-0.977), hospitalizations (8.7%, mean IR 0.913, 95% CI 0.834-0.992), and deaths (9.2%, mean IR 0.908, 95% CI 0.797-1.020) compared with scenarios where children were not vaccinated. This projected effect of vaccinating children 5-11 years old increased in the presence of a more transmissible variant, assuming no change in vaccine effectiveness by variant. Larger relative reductions in cumulative cases, hospitalizations, and deaths were observed for children than for the entire U.S. population. Substantial state-level variation was projected in epidemic trajectories, vaccine benefits, and variant impacts., Conclusions: Results from this multi-model aggregation study suggest that, under a specific set of scenario assumptions, expanding vaccination to children 5-11 years old would provide measurable direct benefits to this age group and indirect benefits to the all-age U.S. population, including resilience to more transmissible variants.
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- 2022
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47. Epidemiological and economic impact of COVID-19 in the US.
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Chen J, Vullikanti A, Santos J, Venkatramanan S, Hoops S, Mortveit H, Lewis B, You W, Eubank S, Marathe M, Barrett C, and Marathe A
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- Agriculture economics, COVID-19 economics, COVID-19 prevention & control, Communicable Disease Control, Construction Industry economics, Employment, Humans, Industry economics, Models, Economic, SARS-CoV-2 isolation & purification, Teleworking, United States epidemiology, COVID-19 epidemiology
- Abstract
This research measures the epidemiological and economic impact of COVID-19 spread in the US under different mitigation scenarios, comprising of non-pharmaceutical interventions. A detailed disease model of COVID-19 is combined with a model of the US economy to estimate the direct impact of labor supply shock to each sector arising from morbidity, mortality, and lockdown, as well as the indirect impact caused by the interdependencies between sectors. During a lockdown, estimates of jobs that are workable from home in each sector are used to modify the shock to labor supply. Results show trade-offs between economic losses, and lives saved and infections averted are non-linear in compliance to social distancing and the duration of the lockdown. Sectors that are worst hit are not the labor-intensive sectors such as the Agriculture sector and the Construction sector, but the ones with high valued jobs such as the Professional Services, even after the teleworkability of jobs is accounted for. Additionally, the findings show that a low compliance to interventions can be overcome by a longer shutdown period and vice versa to arrive at similar epidemiological impact but their net effect on economic loss depends on the interplay between the marginal gains from averting infections and deaths, versus the marginal loss from having healthy workers stay at home during the shutdown., (© 2021. The Author(s).)
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- 2021
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48. Projected resurgence of COVID-19 in the United States in July-December 2021 resulting from the increased transmissibility of the Delta variant and faltering vaccination.
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Truelove S, Smith CP, Qin M, Mullany LC, Borchering RK, Lessler J, Shea K, Howerton E, Contamin L, Levander J, Salerno J, Hochheiser H, Kinsey M, Tallaksen K, Wilson S, Shin L, Rainwater-Lovett K, Lemaitre JC, Dent J, Kaminsky J, Lee EC, Perez-Saez J, Hill A, Karlen D, Chinazzi M, Davis JT, Mu K, Xiong X, Piontti APY, Vespignani A, Srivastava A, Porebski P, Venkatramanan S, Adiga A, Lewis B, Klahn B, Outten J, Schlitt J, Corbett P, Telionis PA, Wang L, Peddireddy AS, Hurt B, Chen J, Vullikanti A, Marathe M, Hoops S, Bhattacharya P, Machi D, Chen S, Paul R, Janies D, Thill JC, Galanti M, Yamana T, Pei S, Shaman J, Reich NG, Healy JM, Slayton RB, Biggerstaff M, Johansson MA, Runge MC, and Viboud C
- Abstract
What Is Already Known About This Topic?: The highly transmissible SARS-CoV-2 Delta variant has begun to cause increases in cases, hospitalizations, and deaths in parts of the United States. With slowed vaccination uptake, this novel variant is expected to increase the risk of pandemic resurgence in the US in July-December 2021., What Is Added by This Report?: Data from nine mechanistic models project substantial resurgences of COVID-19 across the US resulting from the more transmissible Delta variant. These resurgences, which have now been observed in most states, were projected to occur across most of the US, coinciding with school and business reopening. Reaching higher vaccine coverage in July-December 2021 reduces the size and duration of the projected resurgence substantially. The expected impact of the outbreak is largely concentrated in a subset of states with lower vaccination coverage., What Are the Implications for Public Health Practice?: Renewed efforts to increase vaccination uptake are critical to limiting transmission and disease, particularly in states with lower current vaccination coverage. Reaching higher vaccination goals in the coming months can potentially avert 1.5 million cases and 21,000 deaths and improve the ability to safely resume social contacts, and educational and business activities. Continued or renewed non-pharmaceutical interventions, including masking, can also help limit transmission, particularly as schools and businesses reopen.
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- 2021
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49. Placentas delivered by pre-pregnant obese women have reduced abundance and diversity in the microbiome.
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Benny PA, Al-Akwaa FM, Dirkx C, Schlueter RJ, Wolfgruber TK, Chern IY, Hoops S, Knights D, and Garmire LX
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- Adult, Cohort Studies, Female, Fetal Development physiology, Humans, Pregnancy, Pregnancy Complications etiology, Microbiota physiology, Obesity complications, Obesity, Maternal metabolism, Placenta metabolism
- Abstract
Maternal pre-pregnancy obesity may have an impact on both maternal and fetal health. We examined the microbiome recovered from placentas in a multi-ethnic maternal pre-pregnant obesity cohort, through an optimized microbiome protocol to enrich low bacterial biomass samples. We found that the microbiomes recovered from the placentas of obese pre-pregnant mothers are less abundant and less diverse when compared to those from mothers of normal pre-pregnancy weight. Microbiome richness also decreases from the maternal side to the fetal side, demonstrating heterogeneity by geolocation within the placenta. In summary, our study shows that the microbiomes recovered from the placentas are associated with pre-pregnancy obesity. IMPORTANCE: Maternal pre-pregnancy obesity may have an impact on both maternal and fetal health. The placenta is an important organ at the interface of the mother and fetus, and supplies nutrients to the fetus. We report that the microbiomes enriched from the placentas of obese pre-pregnant mothers are less abundant and less diverse when compared to those from mothers of normal pre-pregnancy weight. More over, the microbiomes also vary by geolocation within the placenta., (© 2021 The Authors. The FASEB Journal published by Wiley Periodicals LLC on behalf of Federation of American Societies for Experimental Biology.)
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- 2021
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50. Computational strategies to combat COVID-19: useful tools to accelerate SARS-CoV-2 and coronavirus research.
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Hufsky F, Lamkiewicz K, Almeida A, Aouacheria A, Arighi C, Bateman A, Baumbach J, Beerenwinkel N, Brandt C, Cacciabue M, Chuguransky S, Drechsel O, Finn RD, Fritz A, Fuchs S, Hattab G, Hauschild AC, Heider D, Hoffmann M, Hölzer M, Hoops S, Kaderali L, Kalvari I, von Kleist M, Kmiecinski R, Kühnert D, Lasso G, Libin P, List M, Löchel HF, Martin MJ, Martin R, Matschinske J, McHardy AC, Mendes P, Mistry J, Navratil V, Nawrocki EP, O'Toole ÁN, Ontiveros-Palacios N, Petrov AI, Rangel-Pineros G, Redaschi N, Reimering S, Reinert K, Reyes A, Richardson L, Robertson DL, Sadegh S, Singer JB, Theys K, Upton C, Welzel M, Williams L, and Marz M
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- Biomedical Research, COVID-19 epidemiology, COVID-19 virology, Genome, Viral, Humans, Pandemics, SARS-CoV-2 genetics, COVID-19 prevention & control, Computational Biology, SARS-CoV-2 isolation & purification
- Abstract
SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) is a novel virus of the family Coronaviridae. The virus causes the infectious disease COVID-19. The biology of coronaviruses has been studied for many years. However, bioinformatics tools designed explicitly for SARS-CoV-2 have only recently been developed as a rapid reaction to the need for fast detection, understanding and treatment of COVID-19. To control the ongoing COVID-19 pandemic, it is of utmost importance to get insight into the evolution and pathogenesis of the virus. In this review, we cover bioinformatics workflows and tools for the routine detection of SARS-CoV-2 infection, the reliable analysis of sequencing data, the tracking of the COVID-19 pandemic and evaluation of containment measures, the study of coronavirus evolution, the discovery of potential drug targets and development of therapeutic strategies. For each tool, we briefly describe its use case and how it advances research specifically for SARS-CoV-2. All tools are free to use and available online, either through web applications or public code repositories. Contact:evbc@unj-jena.de., (© The Author(s) 2020. Published by Oxford University Press.)
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- 2021
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