  Collective cell migration in response to a chemical cue occurs in many
biological processes such as morphogenesis and cancer metastasis. Clusters of
migratory cells in these systems are capable of responding to gradients of less
than 1% difference in chemical concentration across a cell length.
Multicellular systems are extremely sensitive to their environment and while
the limits to multicellular sensing are becoming known, how this information
leads to coherent migration remains poorly understood. We develop a
computational model of multicellular sensing and migration in which groups of
cells collectively measure noisy chemical gradients. The output of the sensing
process is coupled to individual cells polarization to model migratory
behavior. Through the use of numerical simulations, we find that larger
clusters of cells detect the gradient direction with higher precision and thus
achieve stronger polarization bias, but larger clusters also induce more drag
on collective motion. The trade-off between these two effects leads to an
optimal cluster size for most efficient migration. We discuss how our model
could be validated using simple, phenomenological experiments.
