What is gfit?
gfit is a tool for building computational models of various systems and for connecting them with experimental measurements of different types to perform global regression analysis. gfit is particularly useful for studying various systems in Biophysics, Biochemistry and Cell Biology.
What can I do with gfit?
gfit is designed for handling difficult cases of regression analysis, for example:
What do I need to start using gfit?
Do I need to know programming to use gfit?
All data analysis tasks are done simply through gfit GUI and require no programming. Building new models involves programming, although his task is simplified by gfit providing valid input variables for each experiment.
What kind of systems can be modeled with gfit?
gfit is not limited to any particular type of a 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 of molecular species in vitro and in vivo. gfit has also been applied to other disciplines.
What kind of experimental data can be used with gfit?
Any kind of quantitative deterministic data can be plugged to a gfit model.
What is the 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 model's algorithm or structure. Instead, gfit uses a detailed formal description of model's inputs and outputs. This has negative and positive consequences. Knowing nothing about 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.
What does it mean to connect model with data?
Taken separately, computational models and experimental data are not very informative. We use models to understand and interpret data; we use data to test, estimate parameters, and validate models.
Connection between model and data is what allows us to perform these operations [Jaqaman2006].
Why do I need to analyze 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 types of data: dynamics, sound, and shape.