  The Cellular Potts Model (CPM) is a widely used simulation paradigm for
systems of interacting cells that has been used to study scenarios ranging from
plant development to morphogenesis, tumour growth and cell migration. Despite
their wide use, CPM simulations are considered too computationally intensive
for three-dimensional (3D) models at organ scale. CPMs have been difficult to
parallelise because of their inherently sequential update scheme. Here, we
present a Graphical Processing Unit (GPU)-based parallelisation scheme that
preserves local update statistics and is up to 3-4 orders of magnitude faster
than serial implementations. We show several examples where our scheme
preserves simulation behaviors that are drastically altered by existing
parallelisation methods. We use our framework to construct tissue-scale models
of liver and lymph node environments containing millions of cells that are
directly based on microscopy-imaged tissue structures. Thus, our GPU-based CPM
framework enables in silico studies of multicellular systems of unprecedented
scale.
