“Combined experimental and modeling approach to cell motility and its role in tumor growth dynamics”

Abstract

In this talk, we describe recent progress in understanding cancer cell migration using tracking experiments, and predicting tumorigenesis and cancer invasion using mathematical models derived from direct experimental observations. In the first part of the work, cell migration paths of mammary epithelial cells were analyzed within a novel bimodal framework that is a generalization of the run-and-tumble description applicable to bacterial migration. The mammalian cell trajectories were segregated into two types of alternating modes, namely, the “directional mode” (mode I, the more persistent mode, analogous to the bacterial run phase) and the “re-orientation mode” (mode II, the less persistent mode, analogous to the bacterial tumble phase). Higher resolution (more pixel information, relative to cell size) and smaller sampling intervals (time between images) were found to give a better estimate of the deduced single cell dynamics (such as directional-mode time and turn-angle distribution) of the various cell types from the bimodal analysis. The bimodal analysis tool permits the deduction of short-time dynamics of cell motion such as the turn angle distributions and turn frequencies during the course of cell migration compared to standard methods of cell migration analysis. We find that the two-hour mammalian cell tracking data do not fall into the diffusive regime implying that the often-used random motility expressions for mammalian cell motion (based on assuming diffusive motion) are invalid over the time steps (fraction of minute) typically used in modeling mammalian cell migration. In the second part of the work, we describe a new off-lattice hybrid discrete-continuum (OLHDC) model of tumor growth and invasion. The continuum part of the OLHDC model describes microenvironmental components such as matrix-degrading enzymes (MDEs), nutrient or oxygen, and extracellular matrix (ECM) concentrations, while the discrete part of the OLHDC presents individual cellular behavior such as cell cycle and cell motility using the well-known framework of a persistent random walk, which can be described by the Langevin equation. In this way, we develop a phenomenally realistic and mechanically relevant model that couples cell motility to experimental observation where mean-square displacement of cells usually shows super-diffusive motion over a short period of time. When systemic simulations based on the OLHDC are performed, tumor growth and its morphology are found to be strongly affected by cell-cell adhesion and haptotaxis. i.e. there is a combination of the degree of cell-cell adhesion and haptotaxis where finger-like shapes of tumor are observed.