Building computational models in any discipline has many challenges starting at inclusion (what goes in, what’s left out), through to representation (are we keeping track of aggregate numbers, or actual individuals), implementation (efficiency, cost) and finally  verification and validation (is it correct?).  Creating entire modeling softwareplatforms intended for end-user scientists within a discipline brings an entirely new level of challenge.  Cognitive issues of representation within the modeling platform – always present when trying to communicate the content of a model to others – become one of the most central challenges.  To create modeling platforms that, say, a biologist might want to use, requires paying close attention to the idioms and metaphors used at the most granular level of biology: at the whiteboard, the bench, or even in the field.

Constructing such software with appropriate metaphors, visual or otherwise, requires close collaboration with working scientists at every…