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    gfit

    Category  Cross-Omics>Agent-Based Modeling/Simulation/Tools

    Abstract  gfit is a tool for building ‘computational models’ of various
    systems and for connecting them with experimental measurements of
    different types to perform global (simultaneous) regression analysis.

    gfit is particularly useful for studying various systems in Biophysics,
    Biochemistry and Cell Biology.

    With gfit one can create a model for virtually any type of system using a
    minimal amount of computer code.

    The interface between models and data is rule-based. It allows models
    to be re-used for many related problems.

    Using gfit one can globally fit many experiments to a suitable model of
    your system. gfit was designed for difficult cases of data analysis.

    Use gfit to globally analyze experiments of different types, to implement
    complex models (any algorithm can be used), to apply statistical
    weights to measured data, etc.

    gfit is designed for model-based analysis of experimental data. Its goal
    is to provide a wide array of statistical tools that can be used with
    various computational models and collections of experimental data.

    To achieve this flexibility, gfit employs a modular design providing
    interfaces for computational models, optimization engines, and other
    statistical tools.

    The current version of gfit allows simultaneous fitting of different
    experiments to a gfit model for MATLAB (see Note 1).

    The types of experiments that can be analyzed and the parameters that
    can be estimated are defined by the model.

    The gfit model can be any program that uses certain input variables to
    calculate its output variables.

    A gfit model should contain Meta information describing its input and
    output variables.

    Each variable can contain a single number or an array of numbers. In
    other words, variables contain N-dimensional arrays with N ranging
    from zero (0) and up.

    For example, a variable may represent the initial concentration of a
    reacting specie (0D-array), a dissociation constant (0D), a column of
    measurement times (1D), or concentrations of many reacting species
    at different times (2D-array).

    The description of a gfit model defines dimensions of each variable and
    the kind of values it may contain. gfit guaranties that the input data
    provided to the model for simulation are within the limits specified by
    the model description.

    The collection of input variables received by gfit model should provide
    sufficient information for simulating one experiment.

    The model does Not distinguish between the variables that are
    precisely known experimental conditions and the variables representing
    unknown parameters. The distinction is made by gfit.

    Based on the description of the model and on the user-supplied
    experimental data, gfit uses some variables for generating fitting
    parameters.

    Each parameter can be linked to one or many numerical elements of a
    variable from one or more experiments.

    gfit features/capabilities include:

    1) gfit is designed to handle difficult cases of 'regression analysis', for
    example:

    a) A different number of measurements in each experiment; b)
    Statistical weights; c) Experiment conditions described by many
    variables;

    d) Different types of experiments; e) Complex simulation algorithms;
    and f) Optimization parameters applied globally to all experiments or to
    a subset of them.

    2) All 'data analysis' tasks are done simply through the gfit Graphical
    User Interface (GUI) and require No programming. Building new
    models involves programming, although this task is simplified by gfit
    providing valid input variables for each experiment.

    3) Types of systems that can be modeled with gfit -- gfit is Not limited to
    any particular type of system. A gfit model can be created as long as
    there is some idea about the system's underlying mechanism.

    In biology gfit has been used to study kinetics and thermodynamics
    (equilibrium) of molecular species in vitro and in vivo. gfit has also been
    applied to other disciplines.

    4) Experimental data requirements -- Any kind of quantitative
    deterministic data can be plugged into a gfit model.

    5) Main difference between gfit and other software for ‘biological
    modeling’ and data analysis -
    gfit takes a more general approach to computational models.

    It does Not consider the model's algorithm or structure. Instead, gfit
    uses a detailed formal description of the model's inputs and outputs.
    This has both negative and positive consequences. Knowing nothing
    about a model's underlying physics and biology, gfit does Not provide
    assistance for formulating internals of a model.

    On the other hand, by controlling the flow of data in and out of a model,
    gfit can effectively connect it with experimental data, analysis tools or
    other models.

    6) Why gfit analyzes data globally --

    Global analysis - simultaneous analysis of different experiments related
    to the same system or process - has many advantages.

    If the goal is to learn parameters of the model, the accuracy of
    estimation increases when many experiments are fit globally.

    Increased accuracy makes it possible to resolve concurrent processes
    and to quantitate them. Global fitting allows quantitating things that are
    Not even apparent from the same experiments taken separately.

    Model testing and validation is more thorough if based globally on all
    available data.

    For example, if you build a model of a duck, you have to find a set of
    parameters for your model so that the model walks like a duck, quacks
    like a duck, and looks like a duck.

    This means that you have to globally fit at least three (3) types of data:
    dynamics, sound, and shape.

    Note 1: MATLAB is a high-level language and interactive environment
    that enables you to perform computationally intensive tasks faster than
    with traditional programming languages such as C, C++, and FORTRAN.

    Note 2: Optimization Toolbox™ extends the MATLAB® technical
    computing environment with tools and widely used algorithms for
    standard and large-scale optimization.

    System Requirements  

    1) MATLAB v6.5 (release 13) or later required for running simulations.
    2) Optimization Toolbox (see Note 2) required for regression analysis.

    Manufacturer   

    gfit is currently being developed in the laboratory of John Carson at the
    Center for Cell Analysis and Modeling
    University of Connecticut Health Center

    Manufacturer's Web Site   

    http://gfit.sourceforge.net/

    Price   Contact manufacturer

    G6G Product Number  20442

    G6G Manufacturer Number 104069
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