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NeuroRD - latest version: Computationally efficient, neuronal reaction-diffusion stochastic simulator (pronounced NeurRDS). Java program which runs on any platform. The algorithm is based on Gillespie's tau-leap reaction algorithm, and the stochastic diffusion algorithm of Blackwell. It uses XML-based model specifications. README with instructions for creating models. An example to get you started will be helpful, and additional examples to explain branching if you are ambitious. More complicated simulation files are available either from modelDB or associated with specific publications. A collection of the models used for software validation is available here.

Programs (in Python and c++) used to process the output data Postprocessing

Chemesis is a set of libraries for GENESIS to enable simulation of reaction-diffusion systems, including calcium release. The latest release of GENESIS (version 2.4) includes the most useful chemesis libraries. If you want to compile chemesis with older versions of GENESIS, download chemesis2.4. The main difference between 2.4 and previous versions is the addition of unit fields to allow using SI or any other units.

After downloading and extracting, editing the makefile for your OS and GENESIS installaion, then type make. Or you can install a pre-compiled version following the directions from here. Email me if you want Chemesis2.1, which was used to run all the published Hermissenda simulations.

Free Online Tutorials
How do I model biochemical reactions and diffusion?

  • Reaction-Diffusion Tutorial in powerpoint. Explains why signaling pathways are important in neuron models, and presents the equations and Chemesis objects for modeling signaling pathways.
  • Calcium Modeling Tutorial in powerpoint. Explains why calcium dynamics are critical in neuron models, presents the equations for modeing calcium, as well as GENESIS and Chemesis objects
  • NeuroRD Tutorial in powerpoint. Expalins how to use NeuroRD to model reaction and diffusion underlying signaling pathways.
  • Old Tutorial on modeling calcium dynamics and second messenger pathways. The first part presents the theory; the second part explains Chemesis, Kinetikit, and Genesis objects to implement the models. The Scripts2014 below are updated versions of the scripts linked from the article and work with Chemesis 2.4.
  • An explanation of how to use chemesis is in Neuroscience Databases: A Practical Guide, Ed. R. Kotter. Kluwer Academic Publishers, Norwell, MA (2002). The chapter is called Modeling the dynamics of second messenger pathways by Blackwell K.T. and Hellgren Kotaleski J. PDF file
Models and simulation files
To help you learn how to model signaling pathways
  • Scripts2015 to teach how to develop models of second messenger reactions and calcium dynamics. The second messenger reactions are implemented in XPPAUT, Kinetikit, and Chemesis; the calcium dynamics in both Chemesis and GENESIS. Use with the tutorials listed. Or, you can download only the calcium modeling scripts here
  • NeuroRD example files (with reactions implemented in NeuroRD). Same reactions implemented in pyMoose, chemesis, xppaut (and similar reactions in smoldyn) mglu-ip3 example. Use with the NeuroRD tutorial as well as the Reaction-Diffusion Tutorial.

  • Genesis simulation illustrating Donnan Equilibrium. This simulation uses Chemesis objects to help explain Figure 2.2 in Foundations of Cellular Neurophysiology by Johnston and Wu, MIT Press. The graphical interface allows students to explore the effects of concentration changes.

    Other simulation files, used for published research, are available through ModelDB or from the publications page.


This software was developed under the generous support of the National Institute of Mental Health under grant K21-MH01141, the National Science Foundation under grant IBN 0077509, the CRCNS program under grants R01 AA16022 and AA18066, and HFSP.

Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author and do not necessarily reflect the views of the National Science Foundation.




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