BanghamLabSVN: Difference between revisions
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'''Why Matlab?''' The language suits our problems. For example, ''GFtbox'' - the reasoning goes like this. Tissue is represented by a thin 3D mesh. Growth factors levels vary spatially forming patterns, e.g. Fig. 1. Here there are two, ''A'' and ''B''. We might make the hypothesis that the growth rate is specified by ''A'' but partially inhibited by ''B'' (inhibited by an amount ''K''). This is a simple idea that can be expressed in Matlab equally simply by writing ''Growth=A .* inh(K, B)''. This is because variables ''A'' and ''B'' can represent vectors - in this case a level for each node in the mesh. We also define a general inhibition function (''inh''). It means that it is straightforward to convert our thoughts on the biology into a programmatic description of a computational model. MTtbox is similar, biological models are succinctly coded into interaction functions.<br><br> | '''Why Matlab?''' The language suits our problems. For example, ''GFtbox'' - the reasoning goes like this. Tissue is represented by a thin 3D mesh. Growth factors levels vary spatially forming patterns, e.g. Fig. 1. Here there are two, ''A'' and ''B''. We might make the hypothesis that the growth rate is specified by ''A'' but partially inhibited by ''B'' (inhibited by an amount ''K''). This is a simple idea that can be expressed in Matlab equally simply by writing ''Growth=A .* inh(K, B)''. This is because variables ''A'' and ''B'' can represent vectors - in this case a level for each node in the mesh. We also define a general inhibition function (''inh''). It means that it is straightforward to convert our thoughts on the biology into a programmatic description of a computational model. MTtbox is similar, biological models are succinctly coded into interaction functions.<br><br> | ||
Moreover, the language is well documented with lots of convenient tools. In particular, Matlab has an extensive library of portable graphical user interface (GUI) functions - and this is convenient for producing tools to visualise the mesh and patterns of growth factors. We use Windows, Mac OS and Linux. | Moreover, the language is well documented with lots of convenient tools. In particular, Matlab has an extensive library of portable graphical user interface (GUI) functions - and this is convenient for producing tools to visualise the mesh and patterns of growth factors. We use Windows, Mac OS and Linux. | ||
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In keeping with Matlab conventions, most of our functions have help comments in the first few lines of the file. This means that Matlab itself indexes this file-level help automatically. To allow you to see the scope of our work, this help has been listed on the following pages. | |||
==[[Toolboxes file level help|<span style="color: Navy">Toolboxes: File level help</span>]] == | ==[[Toolboxes file level help|<span style="color: Navy">Toolboxes: File level help</span>]] == | ||
Total 2828 functions. | Total 2828 functions. | ||
Revision as of 19:23, 21 May 2013
DArT_Toolshed
The DArT_Toolshed is a repository of software developed on BBSRC grant BB/F005555/1 A Multiscale Approach to Genes Growth and Geometry (a collaboration with the CoenLab). A 0.6 MB zipped copy (21st May 2013) is available DArT_Toolbox_Download.zip Revision 4699
Most of the software is written in Matlab. Exceptions include VolViewer which uses OpenGL extensively.
Why Matlab? The language suits our problems. For example, GFtbox - the reasoning goes like this. Tissue is represented by a thin 3D mesh. Growth factors levels vary spatially forming patterns, e.g. Fig. 1. Here there are two, A and B. We might make the hypothesis that the growth rate is specified by A but partially inhibited by B (inhibited by an amount K). This is a simple idea that can be expressed in Matlab equally simply by writing Growth=A .* inh(K, B). This is because variables A and B can represent vectors - in this case a level for each node in the mesh. We also define a general inhibition function (inh). It means that it is straightforward to convert our thoughts on the biology into a programmatic description of a computational model. MTtbox is similar, biological models are succinctly coded into interaction functions.
Moreover, the language is well documented with lots of convenient tools. In particular, Matlab has an extensive library of portable graphical user interface (GUI) functions - and this is convenient for producing tools to visualise the mesh and patterns of growth factors. We use Windows, Mac OS and Linux.
In keeping with Matlab conventions, most of our functions have help comments in the first few lines of the file. This means that Matlab itself indexes this file-level help automatically. To allow you to see the scope of our work, this help has been listed on the following pages.
Toolboxes: File level help
Total 2828 functions.
Algorithms: File level help
Total 252 functions.
Attachments: File level help
Total 107 functions.
IOMethods: File level help
Total 4 functions.
ToolBag: File level help
Total 185 functions.