Document Detail

Postsynaptic signal transduction models for long-term potentiation and depression.
MedLine Citation:
PMID:  21188161     Owner:  NLM     Status:  PubMed-not-MEDLINE    
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.
Tiina Manninen; Katri Hituri; Jeanette Hellgren Kotaleski; Kim T Blackwell; Marja-Leena Linne
Publication Detail:
Type:  Journal Article     Date:  2010-12-13
Journal Detail:
Title:  Frontiers in computational neuroscience     Volume:  4     ISSN:  1662-5188     ISO Abbreviation:  Front Comput Neurosci     Publication Date:  2010  
Date Detail:
Created Date:  2010-12-28     Completed Date:  2011-07-14     Revised Date:  2014-09-09    
Medline Journal Info:
Nlm Unique ID:  101477956     Medline TA:  Front Comput Neurosci     Country:  Switzerland    
Other Details:
Languages:  eng     Pagination:  152     Citation Subset:  -    
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