GFtbox

Why write GFtbox?

To understand how patterns of gene activity in biological organs influence the developing shape. A key notion is that genes may regulate growth direction independently of growth rate. We formalised our ideas in the Growing Polarised Tissue Framework (GPT-framework, ref).

Intuition is not, however, a good guide to how patterns of growth interact with each other and a continuous sheet of material to produce complex shapes. It is therefore necessary to translate intuition into a mathematical form (as a computable model) and compute the results. Rather than writing specific, custom software for each model we chose to produce a general purpose package: GFtbox within which individual computable models can be created.

This allowed us to test the veracity of the computational kernal using simple examples (Supplemental Text 1 in ref) before building a library of examples illustrating the properties of the GPT-framework (Results in ref) and embarking on modelling biological tissues (ref Green). Using GFtbox one can start with a simple sheet of tissue (the canvas), lay out experimentally observed, or hypothesised, patterns of regulator activity and then grow the canvas in 3D. Patterning can continue during growth and the final shape can be compared quantitatively with it's biological counterpart - so testing the hypotheses (ref Cui).

Limitations of GFtbox

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The following is parked here - just to remind me how it is done

[GFtbox]

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