Skip to main content
SpringerLink
Log in
Book cover

Modeling Biomolecular Site Dynamics pp 203–229Cite as

MCell-R: A Particle-Resolution Network-Free Spatial Modeling Framework

  • Protocol
  • First Online:

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1945))

Abstract

Spatial heterogeneity can have dramatic effects on the biochemical networks that drive cell regulation and decision-making. For this reason, a number of methods have been developed to model spatial heterogeneity and incorporated into widely used modeling platforms. Unfortunately, the standard approaches for specifying and simulating chemical reaction networks become untenable when dealing with multistate, multicomponent systems that are characterized by combinatorial complexity. To address this issue, we developed MCell-R, a framework that extends the particle-based spatial Monte Carlo simulator, MCell, with the rule-based model specification and simulation capabilities provided by BioNetGen and NFsim. The BioNetGen syntax enables the specification of biomolecules as structured objects whose components can have different internal states that represent such features as covalent modification and conformation and which can bind components of other molecules to form molecular complexes. The network-free simulation algorithm used by NFsim enables efficient simulation of rule-based models even when the size of the network implied by the biochemical rules is too large to enumerate explicitly, which frequently occurs in detailed models of biochemical signaling. The result is a framework that can efficiently simulate systems characterized by combinatorial complexity at the level of spatially resolved individual molecules over biologically relevant time and length scales.

This is a preview of subscription content, log in via an institution.

Protocol
USD   49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD   169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Springer Nature is developing a new tool to find and evaluate Protocols. Learn more

References

  1. Chylek LA, Harris LA, Tung C-S, Faeder JR, Lopez CF, Hlavacek WS (2013) Rule-based modeling: a computational approach for studying biomolecular site dynamics in cell signaling systems. Wiley Interdiscip Rev Syst Biol Med 6(1):13–36

    Article  Google Scholar 

  2. Gillespie DT (1977) Exact stochastic simulation of coupled chemical reactions. J Phys Chem 81(25):2340–2361

    Article  CAS  Google Scholar 

  3. Funahashi A, Matsuoka Y, Jouraku A, Morohashi M, Kikuchi N, Kitano H (2008) CellDesigner 3.5: a versatile modeling tool for biochemical networks. Proc IEEE 96(8):1254–1265

    Article  Google Scholar 

  4. Hoops S et al (2006) COPASI--a COmplex PAthway SImulator. Bioinformatics 22(24):3067–3074

    Article  CAS  Google Scholar 

  5. Bartol TM et al (2015) Computational reconstitution of spine calcium transients from individual proteins. Front Synaptic Neurosci 7:17

    Article  Google Scholar 

  6. Kerr RA, Levine H, Sejnowski TJ, Rappel W-J (2006) Division accuracy in a stochastic model of Min oscillations in Escherichia coli. Proc Natl Acad Sci U S A 103(2):347–352

    Article  CAS  Google Scholar 

  7. Takahashi K, Arjunan SNV, Tomita M (2005) Space in systems biology of signaling pathways – towards intracellular molecular crowding in silico. FEBS Lett 579(8):1783–1788

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  9. Moraru II et al (2008) Virtual Cell modelling and simulation software environment. IET Syst Biol 2(5):352–362

    Article  CAS  Google Scholar 

  10. Hattne J, Fange D, Elf J (2005) Stochastic reaction-diffusion simulation with MesoRD. Bioinformatics 21(12):2923–2924

    Article  CAS  Google Scholar 

  11. Gillespie DT, Hellander A, Petzold LR (2013) Perspective: stochastic algorithms for chemical kinetics. J Chem Phys 138(17):170901–144908

    Article  Google Scholar 

  12. Drawert B et al (2016) Stochastic simulation service: bridging the gap between the computational expert and the biologist. PLoS Comput Biol 12(12):e1005220

    Article  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

    Article  Google Scholar 

  14. Kerr RA et al (2008) Fast Monte Carlo simulation methods for biological reaction-diffusion systems in solution and on surfaces. SIAM J Sci Comput 30(6):3126–3149

    Article  Google Scholar 

  15. Hlavacek WS, Faeder JR, Blinov ML, Perelson AS, Goldstein B (2003) The complexity of complexes in signal transduction. Biotechnol Bioeng 84(7):783–794

    Article  CAS  Google Scholar 

  16. Sneddon MW, Faeder JR, Emonet T (2011) Efficient modeling, simulation and coarse-graining of biological complexity with NFsim. Nat Methods 8(2):177–183

    Article  CAS  Google Scholar 

  17. 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):3289–3291

    Article  CAS  Google Scholar 

  18. Danos V, Feret J, Fontana W, Krivine J (2007) Scalable simulation of cellular signaling networks. Lect Notes Comput Sci 4807:139–157

    Article  Google Scholar 

  19. Meier-Schellersheim M, Xu X, Angermann B, Kunkel EJ, Jin T, Germain RN (2006) Key role of local regulation in chemosensing revealed by a new molecular interaction-based modeling method. PLoS Comput Biol 2:0710–0724

    Article  CAS  Google Scholar 

  20. Chylek LA, Harris LA, Faeder JR, Hlavacek WS (2015) Modeling for (physical) biologists: an introduction to the rule-based approach. Phys Biol 12(4):045007

    Article  Google Scholar 

  21. Faeder JR, Blinov ML, Hlavacek WS (2009) Rule-based modeling of biochemical systems with BioNetGen. Methods Mol Biol 500:113–167

    Article  CAS  Google Scholar 

  22. Boutillier P et al (2018) The Kappa platform for rule-based modeling. Bioinformatics 34(13):i583–i592

    Article  Google Scholar 

  23. Angermann BR et al (2012) Computational modeling of cellular signaling processes embedded into dynamic spatial contexts. Nat Methods 9:283–289

    Article  Google Scholar 

  24. Harris LA, Hogg JS, Faeder JR (2009) Compartmental rule-based modeling of biochemical systems. In: Proceedings of the 2009 Winter Simulation Conference (WSC), pp 908–919

    Google Scholar 

  25. Lis M, Artyomov MN, Devadas S, Chakraborty AK (2009) Efficient stochastic simulation of reaction–diffusion processes via direct compilation. Bioinformatics 25(17):2289–2291

    Article  CAS  Google Scholar 

  26. Sorokina O, Sorokin A, Armstrong JD, Danos V (2013) A simulator for spatially extended kappa models. Bioinformatics 29(23):3105–3106

    Article  CAS  Google Scholar 

  27. Andrews SS (2017) Smoldyn: particle-based simulation with rule-based modeling, improved molecular interaction and a library interface. Bioinformatics 33(5):710–717

    Article  CAS  Google Scholar 

  28. Michalski PJ, Loew LM (2016) SpringSaLaD: a spatial, particle-based biochemical simulation platform with excluded volume. Biophys J 110(3):523–529

    Article  CAS  Google Scholar 

  29. Grünert 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):307

    Article  Google Scholar 

  30. Grünert G, Dittrich P (2011) Using the SRSim software for spatial and rule-based modeling of combinatorially complex biochemical reaction systems, vol. 6501, pp 240–256

    Google Scholar 

  31. Suderman R, Mitra ED, Lin YT, Erickson KE, Feng S, Hlavacek WS (2018) Generalizing Gillespie’s direct method to enable network-free simulations. Bull Math Biol:1–27

    Google Scholar 

  32. Michalski PJ, Loew LM (2012) CaMKII activation and dynamics are independent of the holoenzyme structure: an infinite subunit holoenzyme approximation. Phys Biol 9(3):036010

    Article  Google Scholar 

  33. Hogg JS, Harris LA, Stover LJ, Nair NS, Faeder JR (2014) Exact hybrid particle/population simulation of rule-based models of biochemical systems. PLoS Comput Biol 10(4):e1003544

    Article  Google Scholar 

  34. Le Novère N, Shimizu TS (2001) STOCHSIM: modelling of stochastic biomolecular processes. Bioinformatics (Oxford, England) 17(6):575–576

    Article  Google Scholar 

  35. Colvin J, Monine MI, Gutenkunst RN, Hlavacek WS, Von Hoff DD, Posner RG (2010) RuleMonkey: software for stochastic simulation of rule-based models. BMC Bioinformatics 11:404

    Article  Google Scholar 

  36. Gupta S et al (2018) Spatial stochastic modeling with MCell and CellBlender. In: Munksy B, Hlavacek W, Tsimring L (eds) Quantitative biology: theory, computational methods and examples of models. MIT Press, Cambridge, MA

    Google Scholar 

  37. Miller CC (1924) The Stokes-Einstein Law for diffusion in solution. Proc R Soc London Ser A, Contain Pap A Math Phys Character 106(740):724–749

    Article  CAS  Google Scholar 

  38. Saffman PG, Delbrück M (1975) Brownian motion in biological membranes. Proc Natl Acad Sci U S A 72(8):3111–3113

    Article  CAS  Google Scholar 

  39. McKay BD (1981) Practical graph isomorphism. Congr Numer 30:45–87

    Google Scholar 

  40. Tapia JJ (2016) A study on systems modeling frameworks and their interoperability. University of Pittsburgh, Pittsburgh, PA

    Google Scholar 

  41. Sekar JA, Faeder JR (2012) Rule-based modeling of signal transduction: a primer. Methods Mol Biol 880:139–218

    Article  Google Scholar 

  42. Perelson AS, DeLisi C (1980) Receptor clustering on a cell surface. I. theory of receptor cross-linking by ligands bearing two chemically identical functional groups. Math Biosci 48(1–2):71–110

    Article  Google Scholar 

  43. Gilfillan AM, Rivera J (2009) The tyrosine kinase network regulating mast cell activation. Immunol Rev 228(1):149–169

    Article  CAS  Google Scholar 

  44. Goldstein B, Faeder JR, Hlavacek WS, Blinov ML, Redondo A, Wofsy C (2002) Modeling the early signaling events mediated by FcepsilonRI. Mol Immunol 38(16–18):1213–1219

    Article  CAS  Google Scholar 

  45. Faeder JR et al (2003) Investigation of early events in Fc epsilon RI-mediated signaling using a detailed mathematical model. J Immunol 170:3769–3781

    Article  CAS  Google Scholar 

  46. Falkenberg CV, Blinov ML, Azeloglu EU, Neves SR, Iyengar R, Loew LM (2012) A mathematical model for nephrin localization in podocyte foot processes. Biophys J 102(3):593a–594a

    Article  Google Scholar 

  47. Nag A, Monine MI, Faeder JR, Goldstein B (2009) Aggregation of membrane proteins by cytosolic cross-linkers: theory and simulation of the LAT-Grb2-SOS1 system. Biophys J 96(7):2604–2623

    Article  CAS  Google Scholar 

  48. Stefan MI, Bartol TM, Sejnowski TJ, Kennedy MB (2014) Multi-state modeling of biomolecules. PLoS Comput Biol 10(9):e1003844

    Article  Google Scholar 

  49. Michalski PJ (2013) The delicate bistability of CaMKII. Biophys J 105(3):794–806

    Article  CAS  Google Scholar 

  50. Zwier MC et al (2015) WESTPA: an interoperable, highly scalable software package for weighted ensemble simulation and analysis. J Chem Theory Comput 11(2):800–809

    Article  CAS  Google Scholar 

  51. Donovan RM, Sedgewick AJ, Faeder JR, Zuckerman DM (2013) Efficient stochastic simulation of chemical kinetics networks using a weighted ensemble of trajectories. J Chem Phys 139(11):115105

    Article  Google Scholar 

  52. Donovan RM et al (2016) Unbiased rare event sampling in spatial stochastic systems biology models using a weighted ensemble of trajectories. PLoS Comput Biol 12(2):e1004611

    Article  Google Scholar 

  53. Goldstein B, Perelson AS (1984) Equilibrium theory for the clustering of bivalent cell surface receptors by trivalent ligands. Application to histamine release from basophils. Biophys J 45(6):1109–1123

    Article  CAS  Google Scholar 

  54. Faeder JR et al (2003) Investigation of early events in FcεRI-mediated signaling using a detailed mathematical model. J Immunol 170(7):3769–3781

    Article  CAS  Google Scholar 

  55. Xu W, Smith AM, Faeder JR, Marai GE (2011) RULEBENDER: a visual interface for rule-based modeling. Bioinformatics 27(12):1721–1722

    Article  CAS  Google Scholar 

  56. Smith AM, Xu W, Sun Y, Faeder JR, Marai GE (2012) RuleBender: integrated modeling, simulation and visualization for rule-based intracellular biochemistry. BMC Bioinformatics 13(Suppl 8):S3

    Google Scholar 

  57. Sekar JAP, Tapia J-J, Faeder JR (2017) Automated visualization of rule-based models. PLoS Comput Biol 13(11):e1005857

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported in part by the US National Institutes of Health grants P41GM103712 and R01GM115805.

Author information

Authors and Affiliations

  1. Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA

    Jose-Juan Tapia, Ali Sinan Saglam & James R. Faeder

  2. Pittsburgh Supercomputing Center, Carnegie Mellon University, Pittsburgh, PA, USA

    Jacob Czech

  3. Howard Hughes Medical Institute, The Salk Institute for Biological Studies, La Jolla, CA, USA

    Robert Kuczewski, Thomas M. Bartol & Terrence J. Sejnowski

Authors
  1. Jose-Juan Tapia
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ali Sinan Saglam
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jacob Czech
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Robert Kuczewski
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Thomas M. Bartol
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Terrence J. Sejnowski
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. James R. Faeder
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to James R. Faeder .

Editor information

Editors and Affiliations

  1. Theoretical Biology and Biophysics Group, Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, USA

    William S. Hlavacek

Rights and permissions

Reprints and permissions

Copyright information

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

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Tapia, JJ. et al. (2019). MCell-R: A Particle-Resolution Network-Free Spatial Modeling Framework. In: Hlavacek, W. (eds) Modeling Biomolecular Site Dynamics. Methods in Molecular Biology, vol 1945. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-9102-0_9

Download citation

  • DOI: https://doi.org/10.1007/978-1-4939-9102-0_9

  • Published:

  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-9100-6

  • Online ISBN: 978-1-4939-9102-0

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation