Postsynaptic signal transduction models for long-term potentiation and depression

• Main Authors: Tiina Manninen, Katri Hituri, Jeanette eHellgren Kotaleski, Kim T. Blackwell, Marja-Leena Linne
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2010-12-01
• Series: Frontiers in Computational Neuroscience
• Subjects: Long-Term Potentiation; computational model; plasticity; Long-term depression; kinetic model; postsynaptic signal transduction model
• Online Access: http://journal.frontiersin.org/Journal/10.3389/fncom.2010.00152/full

More than a hundred biochemical species, activated by neurotransmitters binding to transmembrane receptors, are important in long-term potentiation and depression. 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 (long-term potentiation or long-term depression) 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 method). 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.