  We present a framework for simulating signal propagation in geometric
networks (i.e. networks that can be mapped to geometric graphs in some space)
and for developing algorithms that estimate (i.e. map) the state and functional
topology of complex dynamic geometric net- works. Within the framework we
define the key features typically present in such networks and of particular
relevance to biological cellular neural networks: Dynamics, signaling,
observation, and control. The framework is particularly well-suited for
estimating functional connectivity in cellular neural networks from
experimentally observable data, and has been implemented using graphics
processing unit (GPU) high performance computing. Computationally, the
framework can simulate cellular network signaling close to or faster than real
time. We further propose a standard test set of networks to measure performance
and compare different mapping algorithms.
