Tutorials & E-Library

In every cell type, intracellular Ca2+ signaling plays a major role in a diverse set of activities, including process homeostasis and genetic expression. In neurons, Ca2+ wave propagation has been implicated as a major component of intracellular signaling and is complementary to membrane electrical signaling via action potentials. It follows that the cell must contain tightly-controlled intrinsic mechanisms for regulating intracellular Ca2+ content and waves; these include cytosolic buffers, storage in the ER, and sequestration into mitochondria. In this tutorial, we examine how some of these basic mechanisms interact to produce a Ca2+ wave.

Suppose we have an rxd.Reaction or rxd.Rate that should only occur when the concentration is above (or below) a certain threshold. These functions, however, only support continuous rate functions. What can we do?

We have expanded the capabilities of the NEURON reaction diffusion module to support pure 3D and 1D/3D hybrid intracellular simulations. This tutorial provides an overview of how to set up a simple travelling wave in both cases.

Proteins, ions, etc... in a cell perform signalling functions by moving, reacting with other molecules, or both. In some cases, this movement is by active transport processes, which we do not consider here. If all movement is due to diffusion (wherein a molecule moves randomly), then such systems are known as reaction-diffusion systems.

These problems are characterized by the answers to three questions: (1) Where do the dynamics occur, (2) Who are the actors, and (3) How do they interact?

Often we will want to see how the choice of initial conditions affects the dynamics. We can do this by setting the initial attribute of an rxd.Species and rerunning.

For example, suppose at a single point we have the bistable dynamics introduced in the first part of this tutorial. That is, u′=−u(1−u)(α−u)
. (Here we use u

instead of a specific molecule name to indicate that we are not describing any particular molecule's kinetics, but rather modeling a class of phenomena.)

This time, we'll use an rxd.Parameter for α
instead of a constant. This offers two advantages: (1) this allows α to vary spatially, and (2) this allows us to change the parameter values and rerun without changing the reaction/rate specification.

Before starting this one, make sure you know how to use the CellBuilder (if you haven't already done so, you might want to get the CellBuilder tutorial and work through it).

This introduces material that you will need to know before moving on to the second tutorial.

This exercise shows how to make model networks of artificial cells, such as pulse generators (network stimulator or "NetStim" cells) and integrate & fire cells, and hybrid nets that contain both artificial cells and cells that have biophysical properties (what some might call "realistic" model cells).

One feature of the Network Builder deserves special mention : it can write a hoc file that specifies the network you built with it. This hoc file shows how to use the various statements that are necessary to construct networks algorithmically. By studying such examples, you can learn how to write your own code to implement much larger nets. Another possible use of the Network Builder is to make microcircuits that can be turned into templates for a more modular approach to construction of large scale models.

Given a kinetic scheme channel specification, we add a new voltage-gated current to NEURON.

This collection of tutorials shows how to use NEURON's Channel Builder. The Channel Builder is a GUI tool for creating voltage- and ligand-gated channels whose state transitions are described by kinetic schemes and/or HH-style differential equations.

Given an HH-style channel specification, we add a new voltage-gated current to NEURON. This introduces material that will be helpful in the "kinetic scheme" tutorial.

Early versions of the Channel Builder could only handle the continuous system approximation to channel gating. More recent versions are capable of efficient simulation of stochastic channel gating, in which the gating states and net conductance make abrupt transitions between discrete levels. To do this, the Channel Builder must be configured as a point process, and gating dynamics must be specified in terms of a kinetic scheme.

It is most convenient to start from a Channel Builder implementation of deterministic gating. For this example, our starting point will be the three state kinetic model of a potassium channel that we built in Creating a channel from a kinetic scheme specification.