Integration of rule-based models and compartmental models of neurons

TL;DR

This paper presents a hybrid modeling approach that integrates stochastic rule-based models of synaptic protein interactions with deterministic compartmental models of neuronal electrical activity, enabling more realistic simulations of synaptic plasticity.

Contribution

It introduces an algorithm and implementation that combine rule-based molecular interaction models with compartmental electrical models of neurons.

Findings

Successful integration of stochastic and deterministic models

Implementation of 'KappaNEURON' system

Enhanced realism in synaptic plasticity simulations

Abstract

Synaptic plasticity depends on the interaction between electrical activity in neurons and the synaptic proteome, the collection of over 1000 proteins in the post-synaptic density (PSD) of synapses. To construct models of synaptic plasticity with realistic numbers of proteins, we aim to combine rule-based models of molecular interactions in the synaptic proteome with compartmental models of the electrical activity of neurons. Rule-based models allow interactions between the combinatorially large number of protein complexes in the postsynaptic proteome to be expressed straightforwardly. Simulations of rule-based models are stochastic and thus can deal with the small copy numbers of proteins and complexes in the PSD. Compartmental models of neurons are expressed as systems of coupled ordinary differential equations and solved deterministically. We present an algorithm which incorporates stochastic rule-based models into deterministic compartmental models and demonstrate an implementation ("KappaNEURON") of this hybrid system using the SpatialKappa and NEURON simulators.