The Systems Biology Simulation Core Library (SBSCL)[1][2] is an open-source, cross-platform pure Java™ programming library that supports the simulation and analysis of computational models encoded in SBML (Systems Biology Markup Language) and SED-ML (Simulation Experiment Description Markup Language) formats. SBSCL offers a robust and extensible Java-based implementation of numerical solvers and simulation algorithms, making it a valuable tool for researchers and developers in systems biology.
| SBSCL | |
|---|---|
 | |
| Original author(s) | Andreas Dräger, Roland Keller, Alexander Dörr |
| Developer(s) | Community contributors |
| Initial release | August 12, 2011 |
| Stable release | 2.1
/ April 21, 2021 |
| Written in | Java™ |
| Operating system | Linux, macOS and Microsoft Windows |
| License | LGPL |
| Website | github |
SBSCL, built on top of another open-source library, JSBML[3][4] , enables platform-independent model simulation across any environment supporting the Java Virtual Machine. It includes a comprehensive suite of numerical methods for solving ordinary differential equations and other dynamic behaviors described in biological models.
SBSCL is designed as a reusable software library and does not come with a GUI or a CLI by default. Instead, it is intended to be embedded into larger software projects where custom user interfaces can be developed as needed. Researchers can also integrate SBSCL directly into their Java projects via its Apache Maven or Gradle dependency to programmatically simulate SBML models.
Capabilities
editSBSCL offers a wide range of features for simulating and analyzing computational models in systems biology.
1. Numerical simulation in an ordinary differential equation framework
edit- LSODA-Integrator (Livermore Solver for Ordinary Differential Equation Automatic) for the simulation of models containing general ODEs
- Rosenbrock solver for the integration of stiff differential equation systems
- Several solvers from the Apache Commons Math[5] Library are included.
- Clear separation of SBML interpretation and integration routines
- Fast SBML interpretation by using a transformed syntax graph
- Full support of SBML events, algebraic rules, and fast reactions
- Support of all models from the SBML Test Suite for all levels and versions
- Compatible with Flux Balance Constraints (fbc) and Hierarchical Model Composition (comp) extensions.
2. Stochastic simulation framework
edit- Supports different solvers from the FERN library
- The Gillespie Algorithm is used currently to solve the SBML models.
- Support of models from the Stochastic Test Suite for all levels and versions
3. Linear optimization framework
edit- Incorporation of external implementations of the simplex algorithm in libraries, such as Gurobi, CPLEX, and many more, supports simulating models via linear optimization.
- At the moment, the implementation comprises flux balance analysis.
4. Support for SED-ML
edit- Fully interprets and simulates models described in the Simulation Experiment Description Markup Language (SED-ML).
5. Data Analysis Functions
edit- Several quality functions can compute the distance from simulated data to given data and provide a basis for model calibration.
6. Application Programming Interface Support
edit- Offers a well-defined API, enabling integration into other software projects or direct use in Java applications via Apache Maven or Gradle.
Supported Packages
edit- Systems Biology Markup Language (L1V1 - L3V2)
- SBML models with fbc V1 and V2
- SBML models with comp extensions
- Simulation Experiment Description Markup Language (L1V3)
- COMBINE archive and OMEX format
- All models from the SBML Test Suite and the BiGG models database
- All models from the Stochastic Test Suite
Application
edit- SBSCL has been part of the systems biology community for several years and its adoption has also increased widely with time, primarily in research environments for the simulation of SBML models.
- It also hosts different SBML-related software like SBMLsimulator[6] and CellDesigner[7].
- It has also been used to model zinc transportation in Bacillus spp[8]. and to conduct research on engineering the non-oxidative glycolytic pathway in Streptomyces toxytricini[9]
See also
editReferences
edit- ^ Panchiwala, Hemil; Shah, Shalin; Planatscher, Hannes; Zakharchuk, Mykola; König, Matthias; Dräger, Andreas (12 January 2022). "The systems biology simulation core library" (PDF). Bioinformatics. 38 (3): 864–865. doi:10.1093/bioinformatics/btab669. PMC 8756180. PMID 34554191.
- ^ Keller, Roland; Dörr, Alexander; Tabira, Akito; Funahashi, Akira; Ziller, Michael J; Adams, Richard; Rodriguez, Nicolas; Novère, Nicolas Le; Hiroi, Noriko; Planatscher, Hannes; Zell, Andreas; Dräger, Andreas (December 2013). "The systems biology simulation core algorithm" (PDF). BMC Systems Biology. 7 (1): 55. doi:10.1186/1752-0509-7-55. PMID 23826941.
- ^ Dräger, A.; Rodriguez, N.; Dumousseau, M.; Dorr, A.; Wrzodek, C.; Le Novère, N.; Zell, A.; Hucka, M. (2011). "JSBML: A flexible Java library for working with SBML" (PDF). Bioinformatics. 27 (15): 2167–2168. doi:10.1093/bioinformatics/btr361. PMC 3137227. PMID 21697129.
- ^ Rodriguez, N.; Thomas, A.; Watanabe, L.; Vazirabad, I. Y.; Kofia, V.; Gómez, H. F.; Mittag, F.; Matthes, J.; Rudolph, J. D.; Wrzodek, F.; Netz, E.; Diamantikos, A.; Eichner, J.; Keller, R.; Wrzodek, C.; Fröhlich, S.; Lewis, N. E.; Myers, C. J.; Le Novère, N.; Palsson, B. Ø.; Hucka, M.; Dräger, A. (2022). "JSBML 1.0: providing a smorgasbord of options to encode systems biology models" (PDF). Bioinformatics. 31 (20): 3383–3386. doi:10.1093/bioinformatics/btv341. PMC 4595895. PMID 26079347.
- ^ "The Apache Commons Mathematics Library".
- ^ Dörr, Alexander; Keller, Roland; Zell, Andreas; Dräger, Andreas (18 December 2014). "SBMLSimulator: A Java Tool for Model Simulation and Parameter Estimation in Systems Biology". Computation. 2 (4): 246–257. doi:10.3390/computation2040246. PMC 7093077. PMID 32211200.
- ^ Funahashi, Akira; Morohashi, Mineo; Kitano, Hiroaki; Tanimura, Naoki (November 2003). "CellDesigner: a process diagram editor for gene-regulatory and biochemical networks". Biosilico. 1 (5): 159–162. doi:10.1016/S1478-5382(03)02370-9.
- ^ Bhatt, Kalpana; Maheshwari, Dinesh Kumar (22 January 2022). "Insights into zinc-sensing metalloregulator 'Zur' deciphering mechanism of zinc transportation in Bacillus spp. by modeling, simulation and molecular docking". Journal of Biomolecular Structure and Dynamics. 40 (2): 764–779. doi:10.1080/07391102.2020.1818625. PMID 32924811.
- ^ Jatain, Indu; Yadav, Karuna; Nitharwal, Ram Gopal; Arora, Devender; Dubey, Kashyap Kumar (September 2022). "A system biology approach for engineering non-oxidative glycolysis pathway in Streptomyces toxytricini for high lipstatin biosynthesis". Bioresource Technology Reports. 19 101188. Bibcode:2022BiTeR..1901188J. doi:10.1016/j.biteb.2022.101188.