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Spatial Rule-Based Simulations: The SRSim Software

  • Richard Henze
  • Gerd Grünert
  • Bashar Ibrahim
  • Peter DittrichEmail author
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1945)

Abstract

SRSim combines rule-based reaction network models with spatial particle simulations allowing to simulate the dynamics of large molecular complexes changing according to a set of chemical reaction rules. As the rule can contain patterns of molecular complexes and specific states of certain binding sites, a combinatorially complex or even infinitely sized reaction network can be defined. Particles move in a three-dimensional space according to molecular dynamics implemented by LAMMPS, while the BioNetGen language is used to formulate reaction rules. Geometric information is added in a specific XML format. The simulation protocol is exemplified by two different variants of polymerization as well as a toy model of DNA helix formation. SRSim is open source and available for download.

Key words

Modeling Simulation Chemoinformatics Molecular dynamics Polymerization LAMMPS BioNetGen 

References

  1. 1.
    Gruenert G, Ibrahim B, Lenser T, Lohel M, Hinze T, Dittrich P (2010) Rule-based spatial modeling with diffusing, geometrically constrained molecules. BMC Bioinformatics 11(1):307CrossRefGoogle Scholar
  2. 2.
    Schwartz R, Shor PW, Prevelige PE, Berger B (1998) Local rules simulation of the kinetics of virus capsid self-assembly. Biophys J 75(6):2626–2636CrossRefGoogle Scholar
  3. 3.
    Danos V, Honorato-Zimmer R, Jaramillo-Riveri S, Stucki S (2015) Rigid geometric constraints for kappa models. Electro Notes Theor Comput Sci 313:23–46CrossRefGoogle Scholar
  4. 4.
    Hoard B (2016) Modeling steric effects in antibody aggregation using rule-based methods. PhD thesisGoogle Scholar
  5. 5.
    Hoard B, Jacobson B, Manavi K, Tapia L (2016) Extending rule-based methods to model molecular geometry and 3D model resolution. BMC Syst Biol 10(Suppl 2):48CrossRefGoogle Scholar
  6. 6.
    Santos-García G, Talcott C, Riesco A, Santos-Buitrago B, De Las Rivas J (2016) Role of nerve growth factor signaling in cancer cell proliferation and survival using a reachability analysis approach. In: 10th International conference on practical applications of computational biology & bioinformatics. Springer, New York, pp 173–181Google Scholar
  7. 7.
    Andrews SS, Bray D (2004) Stochastic simulation of chemical reactions with spatial resolution and single molecule detail. Phys Biol 1(3–4):137–151CrossRefGoogle Scholar
  8. 8.
    Michalski PJ, Loew LM (2016) Springsalad: A spatial, particle-based biochemical simulation platform with excluded volume. Biophys J 110(3):523–529CrossRefGoogle Scholar
  9. 9.
    Blinov ML, Faeder JR, Goldstein B, Hlavacek WS (2004) BioNetGen: software for rule-based modeling of signal transduction based on the interactions of molecular domains. Bioinformatics 20(17):3289–3291CrossRefGoogle Scholar
  10. 10.
    Hlavacek WS, Faeder JR, Blinov ML, Posner RG, Hucka M, Fontana W (2006) Rules for modeling signal-transduction systems. Sci STKE 2006(344):re6Google Scholar
  11. 11.
    Danos V, Feret J, Fontana W, Harmer R, Krivine J (2007) Rule-based modelling of cellular signalling. In: Caires L, Vasconcelos VT (eds) Proceedings of CONCUR 2007 – concurrency theory: 18th international conference, CONCUR 2007, Lisbon, Portugal, 3–8 September 2007. Springer, Berlin, pp 17–41CrossRefGoogle Scholar
  12. 12.
    Chylek LA, Harris LA, Faeder JR, Hlavacek WS (2015) Modeling for (physical) biologists: an introduction to the rule-based approach. Phys Biol 12(4):045,007CrossRefGoogle Scholar
  13. 13.
    Plimpton S (1995) Fast parallel algorithms for short-range molecular dynamics. J Comput Phys 117(1):1–19CrossRefGoogle Scholar
  14. 14.
    Leach AR (2001) Molecular modelling: principles and applications. Pearson Education, HarlowGoogle Scholar
  15. 15.
    Martys NS, Mountain RD (1999) Velocity verlet algorithm for dissipative-particle-dynamics-based models of suspensions. Phys Rev E 59:3733–3736.  https://doi.org/10.1103/PhysRevE.59.3733, http://link.aps.org/doi/10.1103/PhysRevE.59.3733
  16. 16.
    Callen HB, Welton TA (1951) Irreversibility and generalized noise. Phys Rev 83:34–40.  https://doi.org/10.1103/PhysRev.83.34, http://link.aps.org/doi/10.1103/PhysRev.83.34
  17. 17.
    Gillespie DT (1976) A general method for numerically simulating the stochastic time evolution of coupled chemical reactions. J Comput Phys 22(4):403–434. http://dx.doi.org/10.1016/0021-9991(76)90041-3, http://www.sciencedirect.com/science/article/pii/0021999176900413
  18. 18.
    Gillespie DT (2009) A diffusional bimolecular propensity function. J Chem Phys 131(16):164109CrossRefGoogle Scholar
  19. 19.
    Mullis KB, Faloona FA (1987) [21] specific synthesis of DNA in vitro via a polymerase-catalyzed chain reaction. Methods Enzymol 155:335–350CrossRefGoogle Scholar
  20. 20.
    Grünert G, Dittrich P (2011) Using the srsim software for spatial and rule-based modeling of combinatorially complex biochemical reaction systems. In: Gheorghe M, Hinze T, Paun G, Rozenberg G, Salomaa A (eds) Membrane computing. Lecture notes in computer science, vol 6501. Springer, Berlin, pp 240–256CrossRefGoogle Scholar
  21. 21.
    Humphrey W, Dalke A, Schulten K (1996) VMD – visual molecular dynamics. J Mol Graphics 14:33–38CrossRefGoogle Scholar
  22. 22.
    Tschernyschkow S, Herda S, Gruenert G, Doring V, Gorlich D, Hofmeister A, Hoischen C, Dittrich P, Diekmann S, Ibrahim B (2013) Rule-based modeling and simulations of the inner kinetochore structure. Prog Biophys Mol Biol 113(1):33–45CrossRefGoogle Scholar
  23. 23.
    Ibrahim B, Henze R, Gruenert G, Egbert M, Huwald J, Dittrich P (2013) Rule-based modeling in space for linking heterogeneous interaction data to large-scale dynamical molecular complexes. Cells 2:506–544CrossRefGoogle Scholar
  24. 24.
    Henze R, Huwald J, Mostajo N, Dittrich P, Ibrahim B (2015) Structural analysis of in silico mutant experiments of human inner-kinetochore structure. BioSystems 127:47–59CrossRefGoogle Scholar
  25. 25.
    Görlich D, Escuela G, Gruenert G, Dittrich P, Ibrahim B (2014) Molecular codes through complex formation in a model of the human inner kinetochore. Biosemiotics 7(2):223–247. https://doi.org/10.1007/s12304-013-9193-5, http://dx.doi.org/10.1007/s12304-013-9193-5
  26. 26.
    Frisco P, Gheorghe M, Pérez-Jiménez MJ (2014) Applications of membrane computing in systems and synthetic biology. Springer, New YorkCrossRefGoogle Scholar
  27. 27.
    Klann M, Paulevé L, Petrov T, Koeppl H (2013) Coarse-grained Brownian dynamics simulation of rule-based models. In: International conference on computational methods in systems biology. Springer, New York, pp 64–77CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Richard Henze
    • 1
  • Gerd Grünert
    • 1
  • Bashar Ibrahim
    • 2
  • Peter Dittrich
    • 1
    Email author
  1. 1.Department of Mathematics and Computer ScienceFriedrich Schiller University JenaJenaGermany
  2. 2.Chair of Bioinformatics, Matthias-Schleiden-InstituteFriedrich Schiller University JenaJenaGermany

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