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Postsynaptic signal transduction models for long-term potentiation and depression

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Postsynaptic signal transduction models for long-term potentiation and depression. / Manninen, Tiina; Hituri, Katri; Hellgren-Kotaleski, Jeanette; Blackwell, Kim T.; Linne, Marja-Leena.

In: Frontiers in Computational Neuroscience, Vol. 4, No. 152, 2010, p. 1-29.

Research output: Contribution to journalReview ArticleScientificpeer-review

Harvard

Manninen, T, Hituri, K, Hellgren-Kotaleski, J, Blackwell, KT & Linne, M-L 2010, 'Postsynaptic signal transduction models for long-term potentiation and depression', Frontiers in Computational Neuroscience, vol. 4, no. 152, pp. 1-29. https://doi.org/10.3389/fncom.2010.00152

APA

Manninen, T., Hituri, K., Hellgren-Kotaleski, J., Blackwell, K. T., & Linne, M-L. (2010). Postsynaptic signal transduction models for long-term potentiation and depression. Frontiers in Computational Neuroscience, 4(152), 1-29. https://doi.org/10.3389/fncom.2010.00152

Vancouver

Manninen T, Hituri K, Hellgren-Kotaleski J, Blackwell KT, Linne M-L. Postsynaptic signal transduction models for long-term potentiation and depression. Frontiers in Computational Neuroscience. 2010;4(152):1-29. https://doi.org/10.3389/fncom.2010.00152

Author

Manninen, Tiina ; Hituri, Katri ; Hellgren-Kotaleski, Jeanette ; Blackwell, Kim T. ; Linne, Marja-Leena. / Postsynaptic signal transduction models for long-term potentiation and depression. In: Frontiers in Computational Neuroscience. 2010 ; Vol. 4, No. 152. pp. 1-29.

Bibtex - Download

@article{25785ff8e2f0412d9f29217888aaecff,
title = "Postsynaptic signal transduction models for long-term potentiation and depression",
abstract = "More than a hundred biochemical species, activated by neurotransmitters binding to transmembrane receptors, are important in long-term potentiation (LTP) and long-term depression (LTD). To investigate which species and interactions are critical for synaptic plasticity, many computational postsynaptic signal transduction models have been developed. The models range from simple models with a single reversible reaction to detailed models with several hundred kinetic reactions. In this study, more than a hundred models are reviewed, and their features are compared and contrasted so that similarities and differences are more readily apparent. The models are classified according to the type of synaptic plasticity that is modeled (LTP or LTD) and whether they include diffusion or electrophysiological phenomena. Other characteristics that discriminate the models include the phase of synaptic plasticity modeled (induction, expression, or maintenance) and the simulation method used (deterministic or stochastic). We find that models are becoming increasingly sophisticated, by including stochastic properties, integrating with electrophysiological properties of entire neurons, or incorporating diffusion of signaling molecules. Simpler models continue to be developed because they are computationally efficient and allow theoretical analysis. The more complex models permit investigation of mechanisms underlying specific properties and experimental verification of model predictions. Nonetheless, it is difficult to fully comprehend the evolution of these models because (1) several models are not described in detail in the publications, (2) only a few models are provided in existing model databases, and (3) comparison to previous models is lacking. We conclude that the value of these models for understanding molecular mechanisms of synaptic plasticity is increasing and will be enhanced further with more complete descriptions and sharing of the published models.",
author = "Tiina Manninen and Katri Hituri and Jeanette Hellgren-Kotaleski and Blackwell, {Kim T.} and Marja-Leena Linne",
note = "Contribution: organisation=sgn,FACT1=1",
year = "2010",
doi = "10.3389/fncom.2010.00152",
language = "English",
volume = "4",
pages = "1--29",
journal = "Frontiers in Computational Neuroscience",
issn = "1662-5188",
publisher = "Frontiers",
number = "152",

}

RIS (suitable for import to EndNote) - Download

TY - JOUR

T1 - Postsynaptic signal transduction models for long-term potentiation and depression

AU - Manninen, Tiina

AU - Hituri, Katri

AU - Hellgren-Kotaleski, Jeanette

AU - Blackwell, Kim T.

AU - Linne, Marja-Leena

N1 - Contribution: organisation=sgn,FACT1=1

PY - 2010

Y1 - 2010

N2 - More than a hundred biochemical species, activated by neurotransmitters binding to transmembrane receptors, are important in long-term potentiation (LTP) and long-term depression (LTD). To investigate which species and interactions are critical for synaptic plasticity, many computational postsynaptic signal transduction models have been developed. The models range from simple models with a single reversible reaction to detailed models with several hundred kinetic reactions. In this study, more than a hundred models are reviewed, and their features are compared and contrasted so that similarities and differences are more readily apparent. The models are classified according to the type of synaptic plasticity that is modeled (LTP or LTD) and whether they include diffusion or electrophysiological phenomena. Other characteristics that discriminate the models include the phase of synaptic plasticity modeled (induction, expression, or maintenance) and the simulation method used (deterministic or stochastic). We find that models are becoming increasingly sophisticated, by including stochastic properties, integrating with electrophysiological properties of entire neurons, or incorporating diffusion of signaling molecules. Simpler models continue to be developed because they are computationally efficient and allow theoretical analysis. The more complex models permit investigation of mechanisms underlying specific properties and experimental verification of model predictions. Nonetheless, it is difficult to fully comprehend the evolution of these models because (1) several models are not described in detail in the publications, (2) only a few models are provided in existing model databases, and (3) comparison to previous models is lacking. We conclude that the value of these models for understanding molecular mechanisms of synaptic plasticity is increasing and will be enhanced further with more complete descriptions and sharing of the published models.

AB - More than a hundred biochemical species, activated by neurotransmitters binding to transmembrane receptors, are important in long-term potentiation (LTP) and long-term depression (LTD). To investigate which species and interactions are critical for synaptic plasticity, many computational postsynaptic signal transduction models have been developed. The models range from simple models with a single reversible reaction to detailed models with several hundred kinetic reactions. In this study, more than a hundred models are reviewed, and their features are compared and contrasted so that similarities and differences are more readily apparent. The models are classified according to the type of synaptic plasticity that is modeled (LTP or LTD) and whether they include diffusion or electrophysiological phenomena. Other characteristics that discriminate the models include the phase of synaptic plasticity modeled (induction, expression, or maintenance) and the simulation method used (deterministic or stochastic). We find that models are becoming increasingly sophisticated, by including stochastic properties, integrating with electrophysiological properties of entire neurons, or incorporating diffusion of signaling molecules. Simpler models continue to be developed because they are computationally efficient and allow theoretical analysis. The more complex models permit investigation of mechanisms underlying specific properties and experimental verification of model predictions. Nonetheless, it is difficult to fully comprehend the evolution of these models because (1) several models are not described in detail in the publications, (2) only a few models are provided in existing model databases, and (3) comparison to previous models is lacking. We conclude that the value of these models for understanding molecular mechanisms of synaptic plasticity is increasing and will be enhanced further with more complete descriptions and sharing of the published models.

U2 - 10.3389/fncom.2010.00152

DO - 10.3389/fncom.2010.00152

M3 - Review Article

VL - 4

SP - 1

EP - 29

JO - Frontiers in Computational Neuroscience

JF - Frontiers in Computational Neuroscience

SN - 1662-5188

IS - 152

ER -