Literature DB >> 26528564

The Systems Biology Markup Language (SBML): Language Specification for Level 3 Version 1 Core.

Michael Hucka, Frank T Bergmann, Stefan Hoops, Sarah M Keating, Sven Sahle, James C Schaff, Lucian P Smith, Darren J Wilkinson.   

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/.

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Year:  2015        PMID: 26528564      PMCID: PMC5451324          DOI: 10.2390/biecoll-jib-2015-266

Source DB:  PubMed          Journal:  J Integr Bioinform        ISSN: 1613-4516


  5 in total

1.  The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models.

Authors:  M Hucka; A Finney; H M Sauro; H Bolouri; J C Doyle; H Kitano; A P Arkin; B J Bornstein; D Bray; A Cornish-Bowden; A A Cuellar; S Dronov; E D Gilles; M Ginkel; V Gor; I I Goryanin; W J Hedley; T C Hodgman; J-H Hofmeyr; P J Hunter; N S Juty; J L Kasberger; A Kremling; U Kummer; N Le Novère; L M Loew; D Lucio; P Mendes; E Minch; E D Mjolsness; Y Nakayama; M R Nelson; P F Nielsen; T Sakurada; J C Schaff; B E Shapiro; T S Shimizu; H D Spence; J Stelling; K Takahashi; M Tomita; J Wagner; J Wang
Journal:  Bioinformatics       Date:  2003-03-01       Impact factor: 6.937

Review 2.  Conservation analysis in biochemical networks: computational issues for software writers.

Authors:  Herbert M Sauro; Brian Ingalls
Journal:  Biophys Chem       Date:  2004-04-01       Impact factor: 2.352

3.  Minimum information requested in the annotation of biochemical models (MIRIAM).

Authors:  Nicolas Le Novère; Andrew Finney; Michael Hucka; Upinder S Bhalla; Fabien Campagne; Julio Collado-Vides; Edmund J Crampin; Matt Halstead; Edda Klipp; Pedro Mendes; Poul Nielsen; Herbert Sauro; Bruce Shapiro; Jacky L Snoep; Hugh D Spence; Barry L Wanner
Journal:  Nat Biotechnol       Date:  2005-12       Impact factor: 54.908

4.  The SBML discrete stochastic models test suite.

Authors:  Thomas W Evans; Colin S Gillespie; Darren J Wilkinson
Journal:  Bioinformatics       Date:  2007-11-19       Impact factor: 6.937

5.  Metabolic control theory: a structural approach.

Authors:  C Reder
Journal:  J Theor Biol       Date:  1988-11-21       Impact factor: 2.691

  5 in total
  38 in total

1.  SBML Level 3 package: Hierarchical Model Composition, Version 1 Release 3.

Authors:  Lucian Paul Smith; Michael Hucka; Stefan Hoops; Andrew Finney; Martin Ginkel; Chris J Myers; Ion Moraru; Wolfram Liebermeister
Journal:  J Integr Bioinform       Date:  2015-09-04

2.  Parameter Estimation and Uncertainty Quantification for Systems Biology Models.

Authors:  Eshan D Mitra; William S Hlavacek
Journal:  Curr Opin Syst Biol       Date:  2019-11-06

3.  Ubiquity: a framework for physiological/mechanism-based pharmacokinetic/pharmacodynamic model development and deployment.

Authors:  John M Harrold; Anson K Abraham
Journal:  J Pharmacokinet Pharmacodyn       Date:  2014-03-12       Impact factor: 2.745

4.  Models and Simulations as a Service: Exploring the Use of Galaxy for Delivering Computational Models.

Authors:  Mark A Walker; Ravi Madduri; Alex Rodriguez; Joseph L Greenstein; Raimond L Winslow
Journal:  Biophys J       Date:  2016-03-08       Impact factor: 4.033

5.  Rule-based modeling with Virtual Cell.

Authors:  James C Schaff; Dan Vasilescu; Ion I Moraru; Leslie M Loew; Michael L Blinov
Journal:  Bioinformatics       Date:  2016-06-09       Impact factor: 6.937

Review 6.  Quantitative computational models of molecular self-assembly in systems biology.

Authors:  Marcus Thomas; Russell Schwartz
Journal:  Phys Biol       Date:  2017-05-23       Impact factor: 2.583

7.  Mechanistic modelling of cancer: some reflections from software engineering and philosophy of science.

Authors:  José M Cañete-Valdeón; Roel Wieringa; Kieran Smallbone
Journal:  Naturwissenschaften       Date:  2012-11-13

8.  BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

Authors:  Chen Li; Marco Donizelli; Nicolas Rodriguez; Harish Dharuri; Lukas Endler; Vijayalakshmi Chelliah; Lu Li; Enuo He; Arnaud Henry; Melanie I Stefan; Jacky L Snoep; Michael Hucka; Nicolas Le Novère; Camille Laibe
Journal:  BMC Syst Biol       Date:  2010-06-29

9.  MIMO: an efficient tool for molecular interaction maps overlap.

Authors:  Pietro Di Lena; Gang Wu; Pier Luigi Martelli; Rita Casadio; Christine Nardini
Journal:  BMC Bioinformatics       Date:  2013-05-15       Impact factor: 3.169

10.  Dealing with diversity in computational cancer modeling.

Authors:  David Johnson; Steve McKeever; Georgios Stamatakos; Dimitra Dionysiou; Norbert Graf; Vangelis Sakkalis; Konstantinos Marias; Zhihui Wang; Thomas S Deisboeck
Journal:  Cancer Inform       Date:  2013-05-07
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