  Targeting signaling pathways that drive cancer cell migration or
proliferation is a common therapeutic approach. A popular experimental
technique, the scratch assay, measures the migration and proliferation-driven
cell monolayer formation. Scratch assay analyses do not differentiate between
migration and proliferation effects and do not attempt to measure dynamic
effects. To improve upon these methods, we combine high-throughput scratch
assays, continuous video microscopy, and variational system identification
(VSI) to infer partial differential equation (PDE) models of cell migration and
proliferation. We capture the evolution of cell density fields over time using
live cell microscopy and automated image processing. We employ VSI techniques
to identify cell density dynamics modeled with first-order kinetics of
advection-diffusion-reaction systems. We present a comparison of our methods to
results obtained using traditional inference approaches on previously analyzed
1-dimensional scratch assay data. We demonstrate the application of this
pipeline on high throughput 2-dimensional scratch assays and find that
decreasing serum levels can decrease random cell migration by approximately
20%. Our integrated experimental and computational pipeline can be adapted for
automatically quantifying the effect of biological perturbations on cell
migration and proliferation in various cell lines.
