Postsynaptic signal transduction models for long-term potentiation and depression

Area of Science:

• Neuroscience
• Computational Biology
• Biochemistry

Background:

• Synaptic plasticity, including long-term potentiation (LTP) and long-term depression (LTD), involves over a hundred biochemical species activated by neurotransmitters.
• Computational postsynaptic signal transduction models are essential tools for investigating the critical species and interactions in synaptic plasticity.

Purpose of the Study:

• To review and compare over a hundred computational models of synaptic plasticity.
• To identify similarities and differences in model features and evolution.
• To highlight challenges and opportunities for future model development and utilization.

Main Methods:

• Systematic review and comparative analysis of over one hundred computational models.
• Classification of models based on plasticity type (LTP/LTD), inclusion of diffusion/electrophysiology, plasticity phase (induction/expression/maintenance), and simulation method (deterministic/stochastic).

Main Results:

• Models vary from simple single reactions to complex networks with hundreds of reactions.
• Increasing sophistication is observed, incorporating stochastic properties, electrophysiology, and molecular diffusion.
• Simpler models offer computational efficiency and theoretical analysis, while complex models allow detailed mechanistic investigation and experimental validation.

Conclusions:

• The value of computational models for understanding molecular mechanisms of synaptic plasticity is growing.
• Challenges include incomplete model descriptions, limited availability in databases, and lack of comparative analysis.
• Enhanced model descriptions and data sharing are recommended to further advance the field.