Systems biology markup language (SBML) level 3 package: multistate, multicomponent and multicompartment species, version 1, release 2
Abstract
Rule-based modeling is an approach that permits constructing reaction networks based on the specification of rules for molecular interactions and transformations. These rules can encompass details such as the interacting sub-molecular domains and the states and binding status of the involved components. Conceptually, fine-grained spatial information such as locations can also be provided. Through "wildcards" representing component states, entire families of molecule complexes sharing certain properties can be specified as patterns. This can significantly simplify the definition of models involving species with multiple components, multiple states, and multiple compartments. The systems biology markup language (SBML) Level 3 Multi Package Version 1 extends the SBML Level 3 Version 1 core with the "type" concept in the Species and Compartment classes. Therefore, reaction rules may contain species that can be patterns and exist in multiple locations. Multiple software tools such as Simmune and BioNetGen support this standard that thus also becomes amedium for exchanging rule-based models. This document provides the specification for Release 2 of Version 1 of the SBML Level 3 Multi package. No design changes have been made to the description of models between Release 1 and Release 2; changes are restricted to the correction of errata and the addition of clarifications.
Additional Information
© 2020 Fengkai Zhang et al., published by De Gruyter. Berlin/Boston. This work is licensed under the Creative Commons Attribution 4.0 Public License. Received April 1, 2020; accepted April 20, 2020; Published online: 06 Jul 2020. This work was supported by the Intramural Research Program of the US National Institute of Allergy and Infectious Diseases of the National Institutes of Health. We sincerely thank all the contributors and their funding agencies. L.P.S., S.M.K, N.R., A.D., F.B. and M.H. were supported by the National Institute of General Medical Sciences (NIGMS, US) grant R01 GM070923. In addition, A.D. was supported by the DZIF (German Center for Infection Research) and by infrastructural funding from the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), Cluster of Excellence EXC 2124 Controlling Microbes to Fight Infections; and F.B. was supported by the Bundesministeriumfür Bildung und Forschung (BMBF, DE) grant de.NBIModSim1, 031L0104A. M.L.B. was supported by NIH (US) grant P41 GM103313 and R24 GM134211. J.F. and J.J.T. were supported by National Institutes of Health (NIH, US) grant P41 GM103712 to the National Center for Multiscale Modeling of Biological Systems (MMBioS).W.S.H was supported by the National Institute of General Medical Sciences (NIGMS, US) grant R01 GM111510.Attached Files
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Additional details
- Eprint ID
- 104357
- DOI
- 10.1515/jib-2020-0015
- Resolver ID
- CaltechAUTHORS:20200713-124645293
- National Institute of Allergy and Infectious Diseases
- NIH
- R01 GM070923
- Deutsches Zentrum für Infektionsforschung (DZIF)
- Deutsche Forschungsgemeinschaft (DFG)
- EXC 2124
- Bundesministerium für Bildung und Forschung (BMBF)
- de.NBIModSim1
- Bundesministerium für Bildung und Forschung (BMBF)
- 031L0104A
- NIH
- P41 GM103313
- NIH
- R24 GM134211
- NIH
- P41 GM103712
- NIH
- R01 GM111510
- Created
-
2020-07-13Created from EPrint's datestamp field
- Updated
-
2021-11-16Created from EPrint's last_modified field
- Caltech groups
- Caltech Library