Integration of Rule-Based Models and Compartmental Models of Neurons
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.
KeywordsHybrid stochastic-deterministic simulations Hybrid spatial-nonspatial simulations Multiscale simulation Rule-based models Compartmental models Computational neuroscience
- 9.Faeder, J., Blinov, M., Hlavacek, W.: Rule-based modeling of biochemical systems with BioNetGen. In: Maly, I.V. (ed.) Systems Biology, Methods in Molecular Biology, vol. 500, pp. 113–167. Humana Press (2009)Google Scholar
- 21.Ray, S., Bhalla, U.S.: PyMOOSE: interoperable scripting in python for MOOSE. Front. Neuroinform. vol. 2(6) (2008)Google Scholar
- 26.Stiles, J.R., Bartol, T.M.: Monte Carlo methods for simulating realistic synaptic microphysiology using MCell. In: De Schutter, E. (ed.) Computational Neuroscience: Realistic Modeling for Experimentalists, Chap. 4, pp. 87–127. CRC Press, Boca Raton (2001)Google Scholar