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Table of Contents

• FAQ
• Cite
• Install
• Video Tutorials

FAQ

• Why should I use Kinetic Model Builder? Graphical interface for rapid model construction, constraint, simulation, analysis and optimization of arbitrary kinetic models in response to complex patterns of user defined time dependent stimuli and stimulus-response summaries across varying stimulus conditions.
• When should I NOT use Kinetic Model Builder? Although Kinetic Model Builder's monte carlo functions can be used to generate single molecule data, the analysis and fitting of single molecule records is best left to other software such as QUB or HJCFIT.
• Are there alternatives to Kinetic Model Builder? Several software packages exist that provide a graphical interface to various kinetic modeling features, including QUB, HJCFIT, ChannelLab, and IonChannelLab.
• How do I cite Kinetic Model Builder? If you use Kinetic Model Builder to generate data for a publication, please cite http://dx.doi.org/10.1085/jgp.201411183.
• What if I want a feature that does not currently exist? No promises, but it doesn't hurt to ask.

Cite

If you use Kinetic Model Builder to generate data for a publication, please cite http://dx.doi.org/10.1085/jgp.201411183.

Install

• Mac OS X 10.7 or later. Download and open the disk image (.dmg) and simply drag and drop the application (.app) to your Applications folder (or wherever).
• Windows Download the ZIP file with -win32- in the filename, unzip and then run the executable (.exe). This windows version was compiled by Kevin Ogden (ogdenkev@gmail.com), please contact him with any questions.
• Other OS. Apologies, but currently a binary version is only available for Mac OS X version 10.7 or later.

Video Tutorials (For Version 3.0.0)

For additional details not covered in the following tutorials, please download the User's Guide.

1. Tutorial: Building a model. This tutorial shows how to build a kinetic model comprised of either 1) discrete system states and allowed transitions between states, or 2) interacting allosteric elements (see doi:10.1085/jgp.201411183).
2. Tutorial: Variables and constraints. This tutorial show how to define and constrain model variables. User defined constraint equations are parsed using EigenLab.
3. Tutorial: Defining a stimulus protocol. This tutorial shows how to define the MxN matrix of stimulus conditions for which a model's response will be simulated.
4. Tutorial: Running a simulation. This tutorial shows how to simulate a model's response to a stimulus protocol and how to visualize and export the resulting simulation. !!! NOTE: A model's response is simulated for all open protocol windows simultaneously.
5. Tutorial: Grouping model states with shared properties. This tutorial shows how to define and visualize groups of specified model states.
6. Tutorial: Stimulus vs. response summary. This tutorial shows how to generate a summary of a model's response across varying stimulus conditions within a protocol. !!! NOTE: Summary parameters can differ across stimulus conditions exactly as described for stimulus parameters.
7. Tutorial: Optimizing a model to fit user data. This tutorial shows how to optimize a model's parameters to fit user supplied data. !!! NOTE: A model is optimized by minimizing the fit cost across all open protocol windows. Thus, models can be simultaneously optimized for multiple protocols.
8. Tutorial: Simultaneous optimization of 'wild-type' and 'mutant' models. This tutorial shows how to optimize a model's parameters to simultaneously fit two different data sets by allowing only certain specified parameters to vary between the data sets (e.g. model's for a wild-type and mutant isoform of a biological protein).

Project Admins:

• Marcel Goldschen-Ohm