Saturating Receiver and Receptor Competition in Synaptic DMC: Deterministic and Statistical Signal Models

Area of Science:

• Computational Neuroscience
• Biophysics
• Molecular Communication

Background:

• Synaptic communication relies on molecular communication (MC), serving as a model for synthetic systems.
• Existing analytical models often neglect receiver saturation and non-independent binding events due to neurotransmitter (NT) competition for receptors.

Purpose of the Study:

• To develop novel deterministic and statistical models for synaptic MC that incorporate receptor saturation and NT competition.
• To accurately predict the behavior of molecular communication systems under realistic biological conditions.

Main Methods:

• A deterministic state-space model based on eigenfunction expansion of Fick's diffusion equation was developed for receptor saturation.
• A statistical model using the hypergeometric distribution was derived to account for NT-receptor competition.
• Particle-based computer simulations were used for model verification.

Main Results:

• The deterministic model shows saturation reduces expected signal peak value and accelerates NT clearance.
• The statistical model reveals how NT number, receptor availability, and binding kinetics influence signal statistics under competition.
• Model accuracy was validated through particle-based simulations.

Conclusions:

• The proposed models provide a more accurate and computationally efficient framework for analyzing synaptic MC.
• These models are crucial for understanding biological synapses and designing advanced synthetic MC systems.