features such as mechanical interactions with other cells [@maclaren2015models]. Force-based models are able to reproduce multicellular behavior such as chemotaxis, wound healing, and cell aggregation [@camley2015emergent; @basan2013alignment; @janulevicius2015short]. Parameters are often directly relatable to experimental measurements, and the simplest models are often amenable to exact mathematical analysis [@camley2015emergent].

Energy-based models (Fig. \[models\]E) allow cell dynamics to emerge from the minimization of a potential energy with thermal noise (the so-called Monte Carlo scheme). A widely used example is the cellular Potts model (CPM) [@graner1992simulation; @swat2012multi], in which cells are represented as collections of co-aligned “spins” on a lattice (Fig. \[models\]E). Cells remain contiguous because it is energetically favorable for neighboring spins to be co-aligned. Biophysical features such as cell shape, cell-cell adhesion, and cell protrusions into the environment are modeled by introducing corresponding terms into the global potential energy. The CPM has successfully reproduced experimental observations of cell sorting, streaming, chemotaxis, and collective migration [@kabla2012collective; @maree2007cellular; @szabo2010collective]. In energy-based models, the parameters are set by calibrating emergent features, such as cell diffusion coefficients or average speeds, with experimental measurements [@szabo2010collective].

Although the physical limits to multicellular sensing are becoming better understood, the physical limits constraining multicellular migration are less clear. This 