Getting started with the Globe_Toolbox

The Globe_Toolbox provides an interactive environment to globally fit 3-Dimensional data to any desired model. Although it is specifically designed for the analysis of time-dependent spectroscopic data (i.e. absorption as function of wavelength and time), it can be used to view and analyse different sorts of correlated time-based data, such as pH or concentration as function of time and sample number.

Before you start

Make sure all required toolboxes are installed (See System Requirements ). Copy all Globe_Toolbox files to a directory, and properly index this location and its subfolders for Matlab with File>Set Path. Add the subfolders you just copied Globe_Toolbox to and save. Type Globe_Toolbox in the Command Window, and press enter to execute. Check if the provided Help files are correctly installed by pressing the Help button in the started GUI. If the documentation is not found, press Matlab's Start button>Desktop Tools>View Start button configuration files>Refresh start button. Check if the Globe_Toolbox is listed (referring to the correct location of the info.xml file). If the toolbox is not listed, try restarting Matlab, and check again if it is listed. Press Close.

Screen size

The main graphical user interface is started by executing Globe_toolbox_v1_00 in Matlab's command window. Because screen sizes and resolutions differ on each machine, it may be possible that the default settings are not appropriate for your display. The size of the main window needs to be modified if words appear clipped. If horizontal stretching of the main interface renders all text properly visible, it may be necessary to adjust the main window size. Type in the command window: open Globe_toolbox_v1_00, and press Enter. The Editor should now open. If not, set your paths first (see above). Adjust the size to match your display settings by changing the Position paramaters on line 38 (the 4 numbers are referenced to the left bottom corner of screen and in normalised units -full width of your screen corresponds to 1- and represent [horizontal_start vertical_start width height]). Change for instance the width to 0.85, and rerun Globe_toolbox_v1_00 to see if everything is now visible.

Working Method

Matlab automatically generates the differential equations for the specified model, making use of its SimBiology Toolbox. The differential equations are then solved numerically. Consequently, the concentration profiles for each species or compartment are generated, which in turn are fitted to the data via a non-linear least-squares algorithm. In other words, the concentration profiles (which change with rate constants) are iteratively fitted to the data. The amplitude of the exponential function (characterised by a rate constant) gives the spectrum (if spectroscopic data are fitted). The amplitudes are fitted linearly via mldivide.

Non-linear fitting

A least squares fitting method is used to minimise the difference between the data and the simulated dataset by changing the starting values of the user-given time constants. In addition, time zero and an offset parameter are optimised as well. All parameters are passed to Matlab's lsqnonlin optimisation algorithm (type 'doc lsqnonlin' for more information). Alternatively, a direct search method can be used to scan the parameter space for a global minimum. This takes significantly more time, but is less likely to end up in a local minimum. The patternsearch algorithm is used to achieve this (type 'doc patternsearch' for more information).

Linear fitting

The spectra are fitting using Matlab's QR decomposition (type 'doc qr' in command window), which is used to solve linear systems with more equations than unknowns: 'The qr function performs the orthogonal-triangular decomposition of a matrix. This factorization is useful for both square and rectangular matrices. It expresses the matrix as the product of a real complex unitary matrix and an upper triangular matrix.' In practice, this means that from input matrix A it produces an upper triangular matrix R of the same dimension as A and a unitary matrix Q so that A = Q*R.

Further links

- Screenshots and Demos
- How to Fit Data
- How to Create a Model
- Fitting Results
- System Requirements
- Features