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:

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.

System Requirements

1) MATLAB v6.5 (release 13) or later required for running simulations.

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.

2) Optimization Toolbox required for regression analysis.

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

Manufacturer

Manufacturer Web Site gfit

Price Contact manufacturer.

G6G Abstract Number 20442

G6G Manufacturer Number 104069