Encyclopedia of Computational Neuroscience

Living Edition
| Editors: Dieter Jaeger, Ranu Jung

MCell

  • Thomas M. Bartol
  • Markus Dittrich
  • James R. Faeder
Living reference work entry
DOI: https://doi.org/10.1007/978-1-4614-7320-6_256-1

Definition

MCell (Monte Carlo Cell) is a program for simulating spatially resolved cell models using particle-based Monte Carlo algorithms.

Detailed Description

Biological processes at the cell level take place in small and often complex geometries and frequently involve only a small number of molecular players (tens to thousands). A prime example of a process in which this “microphysiology” plays a central role is neurotransmission at chemical synapses in the brain and in the peripheral nervous system (Stiles et al. 2001; Stiles and Bartol 2001). At such small subcellular scales, the familiar macroscopic concept of concentration breaks down and stochastic behavior dominates. MCell uses optimized Monte Carlo algorithms to track discrete molecules in space and time as they diffuse and interact with other effector molecules such as membrane channels, receptors, transporters, or enzymes (Bartol et al. 1991; Stiles and Bartol 2001; Kerr et al. 2008).

The first version of MCell, released in...

Keywords

Monte Carlo Algorithm Bimolecular Reaction Unimolecular Reaction Model Syntax Global Time Step 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Notes

Acknowledgment

We gratefully acknowledge the funding from NIH/NIGMS grant P41GM103712. In addition we thank Jacob Czech for his help with the figure preparation; the members of the MCell development team, including Dipak Barua, Jacob Czech, Leonard Harris, Bob Kuczewski, and Jose Juan Tapia, for the helpful discussions; and Terry Sejnowski for the support and inspiration. We dedicate this entry to the memory of Joel R. Stiles.

References

  1. Bartol TM, Land BR, Salpeter EE, Salpeter MM (1991) Monte Carlo simulation of miniature endplate current generation in the vertebrate neuromuscular junction. Biophys J 59:1290–1307PubMedCrossRefPubMedCentralGoogle Scholar
  2. Beenhakker MP, Huguenard JR (2010) Astrocytes as gatekeepers of GABAB receptor function. J Neurosci 30:15262–15276PubMedCrossRefPubMedCentralGoogle Scholar
  3. Coggan JS, Bartol TM, Esquenazi E, Stiles JR, Lamont S, Martone ME, Berg DK, Ellisman MH, Sejnowski TJ (2005) Evidence for ectopic neurotransmission at a neuronal synapse. Science 309:446–451PubMedCrossRefPubMedCentralGoogle Scholar
  4. Dittrich M, Pattillo JM, King JD, Cho S, Stiles JR, Meriney SD (2013) An excess-calcium-binding-site model predicts neurotransmitter release at the neuromuscular junction. Biophys J 104:2751–2763PubMedCrossRefGoogle Scholar
  5. Kerr RA, Levine H, Sejnowski TJ, Rappel W (2006) Division accuracy in a stochastic model of Min oscillations in Escherichia coli. Proc Natl Acad Sci USA 103:347–352PubMedCrossRefPubMedCentralGoogle Scholar
  6. Kerr R, Bartol TM, Kaminsky B, Dittrich M, Chang JCJ, Baden S, Sejnowski TJ, Stiles JR (2008) Fast Monte Carlo simulation methods for biological reaction-diffusion systems in solution and on surfaces. SIAM J Sci Comput 30:3126–3149PubMedCrossRefPubMedCentralGoogle Scholar
  7. Nadkarni S, Bartol TM, Sejnowski TJ, Levine H (2010) Modelling vesicular release at hippocampal synapses. PLoS Comput Biol 6(11):e1000983PubMedCrossRefPubMedCentralGoogle Scholar
  8. Nadkarni S, Bartol TM, Stevens CF, Sejnowski TJ, Levine H (2012) Short-term plasticity constrains spatial organization of a hippocampal presynaptic terminal. Proc Natl Acad Sci USA 109(36):14657–14662PubMedCrossRefPubMedCentralGoogle Scholar
  9. Rappel WJ, Levine H (2008) Receptor noise limitations on chemotactic sensing. Proc Natl Acad Sci USA 105:19270PubMedCrossRefPubMedCentralGoogle Scholar
  10. Scimemi A, Diamond JS (2012) The number and organization of Ca2+ channels in the active zone shapes neurotransmitter release from Schaffer collateral synapses. J Neurosci 32:18157–18176PubMedCrossRefPubMedCentralGoogle Scholar
  11. Stiles JR, Bartol TM (2001) Monte Carlo methods for simulating realistic synaptic microphysiology using MCell. In: De Schutter E (ed) Computational neuroscience: realistic modeling for experimentalists. CRC Press, Boca Raton, pp 87–127Google Scholar
  12. Stiles JR, Van Helden D, Bartol TM, Salpeter EE, Salpeter MM (1996) Miniature endplate current rise times less than 100 microseconds from improved dual recordings can be modeled with passive acetylcholine diffusion from a synaptic vesicle. Proc Natl Acad Sci USA 93:5747–5752PubMedCrossRefPubMedCentralGoogle Scholar
  13. Stiles JR, Bartol TM, Salpeter MM, Salpeter EE, Sejnowski TJ (2001) Synaptic variability: new insights from reconstructions and Monte Carlo simulation with MCell. In: Cowan M, Sudhof TC, Stevens CF (eds) Synapses. Johns Hopkins University Press, Baltimore/London, pp 681–731Google Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Thomas M. Bartol
    • 2
  • Markus Dittrich
    • 3
  • James R. Faeder
    • 1
  1. 1.Department of Computational and Systems BiologyUniversity of Pittsburgh School of MedicinePittsburghUSA
  2. 2.Neurobiology LaboratorySalk Institute for Biological StudiesLa JollaUSA
  3. 3.Biomedical Applications Groups, Pittsburgh Supercomputing CenterCarnegie Mellon UniversityPittsburghUSA