Reaction-diffusion simulation for Multiscale Modeling


The role of Multiscale modeling (MSM) is becoming increasingly apparent as a partial anecdote to the necessary reductionism inherent to most of biological research.

Scientific challenges



Synaptic plasticity including STC

Genomics and proteomics

Technical challenges

This project offers a number of interfacing and development challenges. 1. Ion concentrations at the surface determine driving forces for channels, calculated as a Nernst potential or via Goldman-Hodgkin-Katz equations. These surface-layer concentrations must be in placed into equilibrium with diffusing ion concentrations within cytoplasm. 2. Potential gradients also arise across internal membranes. It has been hypothesized that the endoplasmic reticulum (ER) exists as a "neuron within the neuron" that signals both via electrical and calcium signaling. 3. One cannot simply handle the whole neuron as 1 big diffusion problem: neurons are too big and have different cytoplasmic domains that require different types of simulation: stochastic 3D for spines, deterministc 1D for major dendrites, deterministic 3D for somas. This requires developing a number of simulators within the simulator and interfacing all of them properly. 4. Standard neural tracings, which are quite adequate for electrical simulation, lack the detail required to produce watertight volumes for reaction-diffusion modeling -- we are developing algorithms to map the approximate full surface and instrument both surfaces and the enclosed volumes. 5. Reaction schemes can be extremely complex, yet often lacking in the kinetic parameters that would be needed to produce an accurate ODE representation. Therefore instead of using full kinetic descriptions we may need to use highly simplified Boolean networks (BNs) or other rule-based representations. These representations must then be interfaced with kinetic representations and with diffusion mechanisms.
Last modified: Fri Jul 5 10:04:17 EDT 2013