In the healing of epithelial tissue wounds, a complex interplay between cellular differentiation, proliferation and migration is required. Typical attributes of cancer are perturbations and imbalances in the activation, deactivation and maintenance of these processes. Due to this tumors are often designated as "wounds that never heal". Recent attempts in cancer therapy target signaling pathways, like the selective inhibition of the EGF/EGFR pathway by small compound inhibitors. We are interested in the interplay between intracellular signaling and migratory behaviour in non-small cell lung cancer. For this we analyse data from migration assays involving lung cancer cells under different growth factor stimulations and inhibitor treatments. We use particle image velocimetry (PIV) and single cell tracking analysis to infer migratory characteristics, e.g. spatiotemporal speed distributions or correlation lengths and investigate how these change under different treatments. A collective cell migration model is used to simulate the migratory behaviour. The mechanical model describes random motility and cell-cell adhesion and is able to explain most of the observed dynamics. To get a more detailed comprehension of the intracellular processes following treatment we analyse time-resolved micro-array data, focussing on genes with a role in migration. From a subset of differentially expressed genes we infer a dynamical model of gene-regulatory interactions. Our aim is to develop a mathematical model integrating the intracellular dynamics with mechanical properties steering cellular migration.

mathematical modelling; cell migration; adhesion; particle image velocimetry, gene-regulatory interactions