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Multi-Algorithm Particle Simulations with Spatiocyte

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Protein Function Prediction

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1611))

Abstract

As quantitative biologists get more measurements of spatially regulated systems such as cell division and polarization, simulation of reaction and diffusion of proteins using the data is becoming increasingly relevant to uncover the mechanisms underlying the systems. Spatiocyte is a lattice-based stochastic particle simulator for biochemical reaction and diffusion processes. Simulations can be performed at single molecule and compartment spatial scales simultaneously. Molecules can diffuse and react in 1D (filament), 2D (membrane), and 3D (cytosol) compartments. The implications of crowded regions in the cell can be investigated because each diffusing molecule has spatial dimensions. Spatiocyte adopts multi-algorithm and multi-timescale frameworks to simulate models that simultaneously employ deterministic, stochastic, and particle reaction-diffusion algorithms. Comparison of light microscopy images to simulation snapshots is supported by Spatiocyte microscopy visualization and molecule tagging features. Spatiocyte is open-source software and is freely available at http://spatiocyte.org .

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References

  1. Fange D, Elf J (2006) Noise-induced min phenotypes in E. coli. PLoS Comput Biol 2(6):e80. doi:10.1371/journal.pcbi.0020080

    Article  PubMed  PubMed Central  Google Scholar 

  2. Hecht I, Kessler DA, Levine H (2010) Transient localized patterns in noise-driven reaction-diffusion systems. Phys Rev Lett 104(15):158301. doi:10.1103/PhysRevLett.104.158301

    Article  PubMed  PubMed Central  Google Scholar 

  3. Burrage K, Burrage PM, Marquez-lago T, Nicolau DV (2011) Stochastic simulation for spatial modelling of dynamic processes in a living cell. In: Koeppl H, Setti G, di Bernardo M, Densmore D (eds) Design and analysis of biomolecular circuits: engineering approaches to systems and synthetic biology. Springer, New York, NY, pp 43–62. doi:10.1007/978-1-4419-6766-4

    Chapter  Google Scholar 

  4. Klann M, Koeppl H (2012) Spatial simulations in systems biology: from molecules to cells. Int J Mol Sci 13(6):7798–7827. doi:10.3390/ijms13067798

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Schöneberg J, Ullrich A, Noé F (2014) Simulation tools for particle-based reaction-diffusion dynamics in continuous space. BMC Biophys 7(1):11. doi:10.1186/s13628-014-0011-5

    Article  PubMed  PubMed Central  Google Scholar 

  6. Karr JR, Takahashi K, Funahashi A (2015) The principles of whole-cell modeling. Curr Opin Microbiol 27:18–24. doi:10.1016/j.mib.2015.06.004

    Article  CAS  PubMed  Google Scholar 

  7. Kerr RA, Bartol TM, Kaminsky B, Dittrich M, Chang J-CJ, Baden SB, Sejnowski TJ, Stiles JR (2008) Fast Monte Carlo simulation methods for biological reaction-diffusion Systems in Solution and on surfaces. SIAM J Sci Comput 30(6):3126–3149. doi:10.1137/070692017

    Article  PubMed  PubMed Central  Google Scholar 

  8. Fange D, Mahmutovic A, Elf J (2012) MesoRD 1.0: Stochastic reaction-diffusion simulations in the microscopic limit. Bioinformatics 28:1–3. doi:10.1093/bioinformatics/bts584

    Article  Google Scholar 

  9. Angermann, B. R., Klauschen, F., Garcia, A. D., Prustel, T., Zhang, F., Germain, R. N., & Meier-Schellersheim, M. (2012). Computational modeling of cellular signaling processes embedded into dynamic spatial contexts. Nat Methods, (2011), 1–10. doi:10.1038/nmeth.1861

  10. Drawert B, Engblom S, Hellander A (2012) URDME : a modular framework for stochastic simulation of reaction-transport processes in complex geometries. BMC Syst Biol 6(76):1–17. doi:10.1186/1752-0509-6-76

    Google Scholar 

  11. Hepburn I, Chen W, Wils S, De Schutter E (2012) STEPS: efficient simulation of stochastic reaction-diffusion models in realistic morphologies. BMC Syst Biol 6(1):36. doi:10.1186/1752-0509-6-36

    Article  PubMed  PubMed Central  Google Scholar 

  12. Roberts E, Stone JE, Luthey-Schulten Z (2012) Lattice microbes: high-performance stochastic simulation method for the reaction-diffusion master equation. J Comput Chem. doi:10.1002/jcc.23130

    PubMed Central  Google Scholar 

  13. Andrews SS, Addy NJ, Brent R, Arkin AP (2010) Detailed simulations of cell biology with Smoldyn 2.1. PLoS Comput Biol 6(3):e1000705. doi:10.1371/journal.pcbi.1000705

    Article  PubMed  PubMed Central  Google Scholar 

  14. Byrne MJ, Waxham MN, Kubota Y (2010) Cellular dynamic simulator: an event driven molecular simulation environment for cellular physiology. Neuroinformatics 8(2):63–82. doi:10.1007/s12021-010-9066-x

    Article  PubMed  PubMed Central  Google Scholar 

  15. Takahashi K, Tanase-Nicola S, ten Wolde PR (2010) Spatio-temporal correlations can drastically change the response of a MAPK pathway. Proc Natl Acad Sci U S A 107(6):2473–2478. doi:10.1073/pnas.0906885107

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Tolle DP, Le Novere N (2010) Meredys, a multi-compartment reaction-diffusion simulator using multistate realistic molecular complexes. BMC Syst Biol 4(1):24. doi:10.1186/1752-0509-4-24

    Article  PubMed  PubMed Central  Google Scholar 

  17. Schöneberg J, Noé F (2013) ReaDDy—a software for particle-based reaction-diffusion dynamics in crowded cellular environments. PLoS One 8(9):e74261. doi:10.1371/journal.pone.0074261

    Article  PubMed  PubMed Central  Google Scholar 

  18. Karamitros, M., Luan, S., Bernal, M. A., Allison, J., Baldacchino, G., Davidkova, M., Z. Francis, W. Friedland, V. Ivantchenko, A. Ivantchenko, A. Mantero, P. Nieminem, G. Santin, H.N. Tran, V. Stepan, Incerti, S. (2014). Diffusion-controlled reactions modeling in Geant4-DNA. J Comput Phys, 274, 841–882. doi:10.1016/j.jcp.2014.06.011

  19. Michalski PJ, Loew LM (2016) SpringSaLaD: a spatial, particle-based biochemical simulation platform with excluded volume. Biophys J 110(3):523–529. http://doi.org/10.1016/j.bpj.2015.12.026

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Hellander A, Hellander S, Lötstedt P (2012) Coupled mesoscopic and microscopic simulation of stochastic reaction-diffusion processes in mixed dimensions. Multiscale Model Simul 10(2):585–611. doi:10.1137/110832148

    Article  Google Scholar 

  21. Klann M, Ganguly A, Koeppl H (2012) Hybrid spatial Gillespie and particle tracking simulation. Bioinformatics 28(18):i549–i555. doi:10.1093/bioinformatics/bts384

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Robinson M, Andrews SS, Erban R (2015) Multiscale reaction-diffusion simulations with Smoldyn. Bioinformatics 31(14):2406–2408. http://doi.org/10.1093/bioinformatics/btv149

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Arjunan SNV, Kaizu K, Takahashi K. Spatiocyte: a stochastic particle simulator for filament, membrane and cytosolic reaction-diffusion processes. In preparation.

    Google Scholar 

  24. Arjunan SNV, Tomita M (2010) A new multicompartmental reaction-diffusion modeling method links transient membrane attachment of E. coli MinE to E-ring formation. Syst Synth Biol 4(1):35–53. doi:10.1007/s11693-009-9047-2

    Article  PubMed  Google Scholar 

  25. Gibson MA, Bruck J (2000) Efficient exact stochastic simulation of chemical systems with many species and many channels. J Phys Chem A 104(9):1876–1889. doi:10.1021/jp993732q

    Article  CAS  Google Scholar 

  26. Arjunan SNV (2013) A guide to modeling reaction-diffusion of molecules with the E-cell system. In: Arjunan SNV, Tomita M, Dhar PK (eds) E-cell system: basic concepts and applications. Springer Science & Business Media, New York, NY

    Chapter  Google Scholar 

  27. King GF, Rowland SL, Pan B, Mackay JP, Mullen GP, Rothfield LI (1999) The dimerization and topological specificity functions of MinE reside in a structurally autonomous C-terminal domain. Mol Microbiol 31(4):1161–1169. doi:10.1046/j.1365-2958.1999.01256.x

    Article  CAS  PubMed  Google Scholar 

  28. Ma L-Y, King G, Rothfield L (2003) Mapping the MinE site involved in interaction with the MinD division site selection protein of Escherichia coli. J Bacteriol 185(16):4948–4955. doi:10.1128/JB.185.16.4948-4955.2003

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Loose M, Fischer-Friedrich E, Herold C, Kruse K, Schwille P (2011) Min protein patterns emerge from rapid rebinding and membrane interaction of MinE. Nat Struct Mol Biol 18(5):577–583. doi:10.1038/nsmb.2037

    Article  CAS  PubMed  Google Scholar 

  30. Park K-T, Wu W, Battaile KP, Lovell S, Holyoak T, Lutkenhaus J (2011) The Min oscillator uses MinD-dependent conformational changes in MinE to spatially regulate cytokinesis. Cell 146(3):396–407. doi:10.1016/j.cell.2011.06.042

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Shimo H, Arjunan SNV, Machiyama H, Nishino T, Suematsu M, Fujita H, Tomita M, Takahashi K (2015) Particle simulation of oxidation induced band 3 clustering in human erythrocytes. PLoS Comput Biol 11(6):e1004210. doi:10.1371/journal.pcbi.1004210

    Article  PubMed  PubMed Central  Google Scholar 

  32. Watabe M, Arjunan SNV, Fukushima S, Iwamoto K, Kozuka J, Matsuoka S, Shindo Y, Ueda M, Takahashi K (2015) A computational framework for bioimaging simulation. PLoS One 10(7):e0130089. doi:10.1371/journal.pone.0130089

    Article  PubMed  PubMed Central  Google Scholar 

  33. Varma A, Huang KC, Young KD (2008) The Min system as a general cell geometry detection mechanism: branch lengths in Y-shaped Escherichia coli cells affect Min oscillation patterns and division dynamics. J Bacteriol 190(6):2106–2117. doi:10.1128/JB.00720-07

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Schweizer J, Loose M, Bonny M, Kruse K, Monch I, Schwille P (2012) Geometry sensing by self-organized protein patterns. Proc Natl Acad Sci 109(38):15283–15288. doi:10.1073/pnas.1206953109

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Halatek J, Frey E (2014) Effective 2D model does not account for geometry sensing by self-organized proteins patterns. Proc Natl Acad Sci 111(18):E1817–E1817. doi:10.1073/pnas.1220971111

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Wu F, van Schie BGC, Keymer JE, Dekker C (2015) Symmetry and scale orient Min protein patterns in shaped bacterial sculptures. Nat Nanotechnol 10(8):719–726. doi:10.1038/nnano.2015.126

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Zieske K, Schwille P (2015) Reconstituting geometry-modulated protein patterns in membrane compartments. Methods Cell Biol 128:149–163. doi:10.1016/bs.mcb.2015.02.006

    Article  PubMed  Google Scholar 

  38. Zieske K, Schweizer J, Schwille P (2014) Surface topology assisted alignment of Min protein waves. FEBS Lett 588(15):2545–2549. doi:10.1016/j.febslet.2014.06.026

    Article  CAS  PubMed  Google Scholar 

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Acknowledgment

We thank Masaki Watabe, Hanae Shimo, and Kaizu Kazunari for discussions that led to the improvement of Spatiocyte usage. We also appreciate Kozo Nishida for Spatiocyte software packaging, installation, and documentation assistance.

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Correspondence to Satya N. V. Arjunan or Koichi Takahashi .

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Arjunan, S.N.V., Takahashi, K. (2017). Multi-Algorithm Particle Simulations with Spatiocyte. In: Kihara, D. (eds) Protein Function Prediction. Methods in Molecular Biology, vol 1611. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7015-5_16

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  • DOI: https://doi.org/10.1007/978-1-4939-7015-5_16

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-7013-1

  • Online ISBN: 978-1-4939-7015-5

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