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Dopamine pp 61-75 | Cite as

Modeling Spatial Aspects of Intracellular Dopamine Signaling

  • Kim T. BlackwellEmail author
  • Lane J. Wallace
  • BoHung Kim
  • Rodrigo F. Oliveira
  • Wonryull Koh
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 964)

Abstract

Dopamine binding to various dopamine receptors activates multiple intracellular signaling molecules, some of which interact with calcium activated signaling pathways. Many experiments measure agonist-stimulated elevations in signaling molecules using prolonged, diffuse application, whereas the response of neurons to transient and spatially localized stimuli is more important. Computational modeling is an approach for investigating the spatial extent, time course, and interaction of postsynaptic signaling molecules activated by dopamine and other transmembrane receptors. NeuroRD is a simulation algorithm which can simulate large numbers of pathways and molecules in multiple spines attached to a dendrite. We explain how to gather the information needed to develop computational models, to implement such models in NeuroRD, to perform simulations, and to analyze the simulated data from these models.

Key words

Computer model Signaling pathways Dopamine signaling Reactions Diffusion 

Notes

Acknowledgements

This work was supported by the NIH-NSF CRCNS program on Collaborative Research in Computational Neuroscience through NIH grants R01 AA18060 and R01 AA16022.

References

  1. 1.
    Wichmann T, DeLong MR (1998) Models of basal ganglia function and pathophysiology of movement disorders. Neurosurg Clin N Am 9:223–236PubMedGoogle Scholar
  2. 2.
    Goldberg JA, Rokni U, Boraud T, Vaadia E, Bergman H (2004) Spike synchronization in the cortex/basal-ganglia networks of Parkinsonian primates reflects global dynamics of the local field potentials. J Neurosci 24:6003–6010PubMedCrossRefGoogle Scholar
  3. 3.
    Kitai ST, Surmeier DJ (1993) Cholinergic and dopaminergic modulation of potassium conductances in neostriatal neurons. Adv Neurol 60(40–52):40–52PubMedGoogle Scholar
  4. 4.
    Kotaleski JH, Blackwell KT (2010) Modelling the molecular mechanisms of synaptic plasticity using systems biology approaches. Nat Rev Neurosci 11:239–251PubMedCrossRefGoogle Scholar
  5. 5.
    Oliveira RO, Terrin A, Di Benedetto G, Cannon RC, Koh W, Zaccolo M, Blackwell KT (2010) The role of type 4 phosphodiesterases in generating microdomains of cAMP: large scale stochastic simulations. PLoS One 5:e11725PubMedCrossRefGoogle Scholar
  6. 6.
    Andrews SS, Addy NJ, Brent R, Arkin AP (2010) Detailed simulations of cell biology with Smoldyn 2.1. PLoS Comput Biol 6:e1000705PubMedCrossRefGoogle Scholar
  7. 7.
    Byrne MJ, Waxham MN, Kubota Y (2010) Cellular dynamic simulator: an event driven molecular simulation environment for cellular physiology. Neuroinformatics 8:63–82PubMedCrossRefGoogle Scholar
  8. 8.
    Kerr RA, Bartol TM, Kaminsky B, Dittrich M, Chang JC, 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 30:3126PubMedCrossRefGoogle Scholar
  9. 9.
    Stenesh J (1993) Core topics in biochemistry. Cogno Press, Michigan, p 283Google Scholar
  10. 10.
    Bower JM, Beeman D (1998) The book of genesis: exploring realistic neural models with the GEneral NEural SImulation System, 2nd edn. Springer, New YorkGoogle Scholar
  11. 11.
    Cheng Y, Prusoff WH (1973) Relationship between the inhibition constant (K1) and the concentration of inhibitor which causes 50 per cent inhibition (I50) of an enzymatic reaction. Biochem Pharmacol 22:3099–3108PubMedCrossRefGoogle Scholar
  12. 12.
    Cer RZ, Mudunuri U, Stephens R, Lebeda FJ (2009) IC50-to-Ki: a web-based tool for converting IC50 to Ki values for inhibitors of enzyme activity and ligand binding. Nucleic Acids Res 37:W441–W445PubMedCrossRefGoogle Scholar
  13. 13.
    Garzon M, Vaughan RA, Uhl GR, Kuhar MJ, Pickel VM (1999) Cholinergic axon terminals in the ventral tegmental area target a subpopulation of neurons expressing low levels of the dopamine transporter. J Comp Neurol 410:197–210PubMedCrossRefGoogle Scholar
  14. 14.
    Xie Z, Adamowicz WO, Eldred WD, Jakowski AB, Kleiman RJ, Morton DG, Stephenson DT, Strick CA, Williams RD, Menniti FS (2006) Cellular and subcellular localization of PDE10A, a striatum-enriched phosphodiesterase. Neuroscience 139:597–607PubMedCrossRefGoogle Scholar
  15. 15.
    Rice ME, Cragg SJ (2004) Nicotine amplifies reward-related dopamine signals in striatum. Nat Neurosci 7:583–584PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2013

Authors and Affiliations

  • Kim T. Blackwell
    • 1
    Email author
  • Lane J. Wallace
    • 2
  • BoHung Kim
    • 1
  • Rodrigo F. Oliveira
    • 1
  • Wonryull Koh
    • 1
  1. 1.The Krasnow Institute for Advanced StudyGeorge Mason UniversityFairfaxUSA
  2. 2.College of PharmacyOhio State UniversityColumbusUSA

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