  Toxoplasma gondii is a parasitic protozoan that causes dis- seminated
toxoplasmosis, a disease that afflicts roughly a third of the worlds
population. Its virulence is predicated on its motility and ability to enter
and exit nucleated cells; therefore, studies elucidating its mechanism of
motility and in particular, its motility patterns in the context of its lytic
cycle, are critical to the eventual development of therapeutic strate- gies.
Here, we present an end-to-end computational pipeline for identifying T. gondii
motility phenotypes in a completely unsupervised, data-driven way. We track the
parasites before and after addition of extracellular Ca2+ to study its effects
on the parasite motility patterns and use this information to parameterize the
motion and group it according to similarity of spatiotemporal dynamics.
