Software:Libroadrunner

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libroadrunner
Initial releaseMarch 23, 2015; 8 years ago (2015-03-23)
Stable release
2.2.5 / August 12, 2022; 18 months ago (2022-08-12)
Written inPython,C++,C,FORTRAN
Operating systemLinux, macOS and Microsoft Windows
LicenseApache License
Websitegithub.com/sys-bio/roadrunner

libRoadRunner is a C/C++ software library that supports simulation of SBML based models.[1] It uses LLVM to generate extremely high-performance code and is the fastest SBML-based simulator currently available.[2] Its main purpose is for use as a reusable library that can be hosted by other applications, particularly on large compute clusters for doing parameter optimization where performance is critical. It also has a set of Python bindings that allow it to be easily used from Python.

libroadrunner is often paired with Tellurium,[3] which adds additional functionality such as Antimony[4] scripting.

Capabilities

  • Time-course simulation using the CVODE, RK45, and Euler solvers of ordinary differential equations, which can report on the system's variable concentrations and reaction rates over time.
  • Steady-state calculations using non-linear solvers such as kinsolve[5] and NLEQ2[6]
  • Supports both steady-state and time-dependent Metabolic control analysis, including calculating the elasticities towards the variable metabolites by algebraic or numerical differentiation of the rate equations, as well as the flux and concentration control coefficients by means of matrix inversion[7] and perturbation methods.
  • libroadrunner will also compute the structural matrices (e.g. K- and L-matrices) of a stoichiometric model[8].
  • The stability of a system can be investigated by way of the system eigenvalues.
  • Data and results can be plotted via matplotlib, or saved in text files.
  • libroadrunner supports the import and export of standard SBML.

Applications

libroadrunner has been widely used in the systems biology community for doing research in systems biology modeling, as well as being a host for other simulation platforms.

Software applications that use libroadrunner

Research applications

libroadrunner has been used in a large variety of research projects. The following lists a small number of those studies:

  • Tickman et al[18], describe developing multi-layer CRIPRa/i circuits for genetic programs using Tellurium/libroadrunner as the computational application.
  • Salazar-Cavazos et al[19] used pyBioNetFit/libroadrunner to investigate Multisite EGFR phosphorylation.
  • Douilhet et al.[20] used Tellurium/libroadrunner to investigate the use of genetic algorithms with rank selection optimization.
  • Schmiester et al.[21] used pyBioNetFit/libroadrunner to investigate gradient-based parameter estimation using qualitative data.
  • Yang et al[22] used CompuCell3D/libroadrunner to model transcript factor cooperation in mouse liver.

Notability

  • libroadrunner was the first SBML simulation to use just-in-time compilation using LLVM.
  • It is the only SBML simulator that exploits [AUTO2000 for bifurcation analysis[23].

A number of reviews and commentaries have been written that discuss libroadrunner:

  • Maggioli et al.[24] conduct a speed comparison of various SBML simulators and conclude libroadrunner is the fastest SBML simulator currently available to researchers.
  • Koster et al,[25]discuss the speed advantages of libroadrunner for solving differential equations compared to solving stochastic systems.

Development

Development of libroadrunner is primarily funded through research grants from the National Institutes of Health[26].

See also

References

Category:Systems biology Category:Ordinary differential equations Category:Software using the Apache license


  1. ↑ Somogyi, Endre T.; Bouteiller, Jean-Marie; Glazier, James A.; König, Matthias; Medley, J. Kyle; Swat, Maciej H.; Sauro, Herbert M. (15 October 2015). "libRoadRunner: a high performance SBML simulation and analysis library: Table 1.". Bioinformatics 31 (20): 3315–3321. doi:10.1093/bioinformatics/btv363. 
  2. ↑ Maggioli, F; Mancini, T; Tronci, E (1 April 2020). "SBML2Modelica: integrating biochemical models within open-standard simulation ecosystems". Bioinformatics 36 (7): 2165–2172. doi:10.1093/bioinformatics/btz860. 
  3. ↑ Choi, Kiri; Medley, J. Kyle; König, Matthias; Stocking, Kaylene; Smith, Lucian; Gu, Stanley; Sauro, Herbert M. (September 2018). "Tellurium: An extensible python-based modeling environment for systems and synthetic biology". Biosystems 171: 74–79. doi:10.1016/j.biosystems.2018.07.006. 
  4. ↑ Smith, L. P.; Bergmann, F. T.; Chandran, D.; Sauro, H. M. (15 September 2009). "Antimony: a modular model definition language". Bioinformatics 25 (18): 2452–2454. doi:10.1093/bioinformatics/btp401. 
  5. ↑ Hindmarsh, Alan C.; Brown, Peter N.; Grant, Keith E.; Lee, Steven L.; Serban, Radu; Shumaker, Dan E.; Woodward, Carol S. (September 2005). "SUNDIALS: Suite of nonlinear and differential/algebraic equation solvers". ACM Transactions on Mathematical Software 31 (3): 363–396. doi:10.1145/1089014.1089020. 
  6. ↑ Deuflhard, P (2004). Newton Methods for Nonlinear Problems. Springer-Verlag, NY. 
  7. ↑ Hofmeyr, Jannie (2001). "Metabolic control analysis in a nutshell" (in en). Proceedings of the 2nd International Conference on Systems Biology. https://www.semanticscholar.org/paper/Metabolic-control-analysis-in-a-nutshell-Hofmeyr-Hucka/ab5bbdf2d261d098d5584fa8074d80664b578ef2. 
  8. ↑ Kerkhoven, Eduard J.; Achcar, Fiona; Alibu, Vincent P.; Burchmore, Richard J.; Gilbert, Ian H.; Trybiło, Maciej; Driessen, Nicole N.; Gilbert, David et al. (5 December 2013). "Handling Uncertainty in Dynamic Models: The Pentose Phosphate Pathway in Trypanosoma brucei". PLoS Computational Biology 9 (12): e1003371. doi:10.1371/journal.pcbi.1003371. 
  9. ↑ Reyes, Brandon C; Otero-Muras, Irene; Shuen, Michael T; Tartakovsky, Alexandre M; Petyuk, Vladislav A (1 June 2020). "CRNT4SBML: a Python package for the detection of bistability in biochemical reaction networks". Bioinformatics 36 (12): 3922–3924. doi:10.1093/bioinformatics/btaa241. 
  10. ↑ Nguyen, Lan K.; Degasperi, Andrea; Cotter, Philip; Kholodenko, Boris N. (December 2015). "DYVIPAC: an integrated analysis and visualisation framework to probe multi-dimensional biological networks". Scientific Reports 5 (1): 12569. doi:10.1038/srep12569. 
  11. ↑ Haiman, Zachary B.; Zielinski, Daniel C.; Koike, Yuko; Yurkovich, James T.; Palsson, Bernhard O. (28 January 2021). "MASSpy: Building, simulating, and visualizing dynamic biological models in Python using mass action kinetics". PLOS Computational Biology 17 (1): e1008208. doi:10.1371/journal.pcbi.1008208. 
  12. ↑ Neumann, Jacob; Lin, Yen Ting; Mallela, Abhishek; Miller, Ely F; Colvin, Joshua; Duprat, Abell T; Chen, Ye; Hlavacek, William S et al. (4 March 2022). "Implementation of a practical Markov chain Monte Carlo sampling algorithm in PyBioNetFit". Bioinformatics 38 (6): 1770–1772. doi:10.1093/bioinformatics/btac004. 
  13. ↑ Ghaffarizadeh, Ahmadreza; Heiland, Randy; Friedman, Samuel H.; Mumenthaler, Shannon M.; Macklin, Paul (23 February 2018). "PhysiCell: An open source physics-based cell simulator for 3-D multicellular systems". PLOS Computational Biology 14 (2): e1005991. doi:10.1371/journal.pcbi.1005991. 
  14. ↑ Ortega, Oscar O.; Lopez, Carlos F. (January 2020). "Interactive Multiresolution Visualization of Cellular Network Processes". iScience 23 (1): 100748. doi:10.1016/j.isci.2019.100748. 
  15. ↑ Shaikh, Bilal; Marupilla, Gnaneswara; Wilson, Mike; Blinov, Michael L; Moraru, Ion; Karr, Jonathan R (2 July 2021). "RunBioSimulations: an extensible web application that simulates a wide range of computational modeling frameworks, algorithms, and formats". Nucleic Acids Research 49 (W1): W597–W602. doi:10.1093/nar/gkab411. 
  16. ↑ Konig, Matthias. "SBMLSim". https://github.com/matthiaskoenig/sbmlsim. 
  17. ↑ Choi, Kiri; Medley, J. Kyle; König, Matthias; Stocking, Kaylene; Smith, Lucian; Gu, Stanley; Sauro, Herbert M. (September 2018). "Tellurium: An extensible python-based modeling environment for systems and synthetic biology". Biosystems 171: 74–79. doi:10.1016/j.biosystems.2018.07.006. 
  18. ↑ Tickman, Benjamin I.; Burbano, Diego Alba; Chavali, Venkata P.; Kiattisewee, Cholpisit; Fontana, Jason; Khakimzhan, Aset; Noireaux, Vincent; Zalatan, Jesse G. et al. (March 2022). "Multi-layer CRISPRa/i circuits for dynamic genetic programs in cell-free and bacterial systems". Cell Systems 13 (3): 215–229.e8. doi:10.1016/j.cels.2021.10.008. 
  19. ↑ Salazar-Cavazos, Emanuel; Nitta, Carolina Franco; Mitra, Eshan D.; Wilson, Bridget S.; Lidke, Keith A.; Hlavacek, William S.; Lidke, Diane S. (19 March 2020). "Multisite EGFR phosphorylation is regulated by adaptor protein abundances and dimer lifetimes". Molecular Biology of the Cell 31 (7): 695–708. doi:10.1091/mbc.E19-09-0548. 
  20. ↑ Douilhet, Gemma; Niranjan, Mahesan; Vallejo, Andres; Clayton, Kalum; Davies, James; Sirvent, Sofia; Pople, Jenny; Ardern-Jones, Michael R et al. (23 February 2022). "Genetic Algorithm with Rank Selection optimises robust parameter estimation for systems biology models". bioxriv. doi:10.1101/2022.02.22.481394. 
  21. ↑ Schmiester, Leonard; Weindl, Daniel; Hasenauer, Jan (7 December 2021). "Efficient gradient-based parameter estimation for dynamic models using qualitative data". Bioinformatics 37 (23): 4493–4500. doi:10.1093/bioinformatics/btab512. 
  22. ↑ Yang, Yongliang; Filipovic, David; Bhattacharya, Sudin (April 2022). "A Negative Feedback Loop and Transcription Factor Cooperation Regulate Zonal Gene Induction by 2, 3, 7, 8‐Tetrachlorodibenzo‐p‐Dioxin in the Mouse Liver". Hepatology Communications 6 (4): 750–764. doi:10.1002/hep4.1848. 
  23. ↑ "Bifurcation Analysis". https://libroadrunner.readthedocs.io/en/latest/bifurcation.html. 
  24. ↑ Maggioli, F; Mancini, T; Tronci, E (1 April 2020). "SBML2Modelica: integrating biochemical models within open-standard simulation ecosystems". Bioinformatics 36 (7): 2165–2172. doi:10.1093/bioinformatics/btz860. 
  25. ↑ Köster, Till; Warnke, Tom; Uhrmacher, Adelinde M. (30 April 2022). "Generating Fast Specialized Simulators for Stochastic Reaction Networks via Partial Evaluation". ACM Transactions on Modeling and Computer Simulation 32 (2): 1–25. doi:10.1145/3485465. 
  26. ↑ "Development Support". https://grantome.com/grant/NIH/R01-GM123032-04. 
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