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Model Search (msrch) calculates the likelihoods of all possible models of a specified number of states and conductance classes. The program gives you an ordered list of the models and their likelihood. You can rerun models with random starting values to reduce the chance of local maxima.

Graph theoretic approaches have eliminated the isomorphic models (different drawings of the same interconnection patterns). This problem explodes with the number of states and at the moment is limited to models of six states (eight states may have 2 x 106 possible connections).

While you can apply Model Search to a set of data files obtained across concentration or voltage, the program will not distribute the dependencies to particular rates. If the model is ligand dependent, then every rate is considered to be ligand dependent....a bit messy. We recommend using Model Search as a rough guide to possible state models for your data and then to explore the details more closely using MIL. (The core of Model Search is MIL).

**"## models (## without loops)"** This message is shown in the upper-right of msearch properties. If it says "0 models," the number and/or color of states in your model is not supported.

See also:

Search Params:

Trials per model | run more than one trial per connection scheme in case the first one doesn't converge |
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No loops | skip connection schemes with cycles of 3 states or more |

Balance loops | constrain all cycles to be in detailed balance |

Max connections | skip connection schemes with too many connections |

Max exit rates per state | skip connection schemes with too many connections from a single state |

Initial k0 | starting value for all pre-exponential rate constants |

Initial k1 | starting value for all exponential rate constants |

Random rates | (from FF to TT times initial value) |

Model filter | a Python function to eliminate or process models. email us to find out more |

Presets |

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