| With the advent of new technologies in the field of life sciences, there has been an increasing emphasis on applying quantitative and mathematical approaches to biology and medicine in order to comprehend the vast amount of data generated. A successful solution to a problem relies upon a synergistic feedback between experimental and modeling approaches. Modeling cancer invasion is one example where the influence of several factors (such as cell phenotype (mutation, cell cycle stage), cell-cell adhesion, etc) in different manners needs to be considered. A particularly relevant example in relation to this thesis, in the realm of basic biology, is the phenomenon of cell migration. Cell motility (particularly, directed motility, such as, chemotaxis) is thought to be an essential element of metastasis, which is the prime cause of mortality and morbidity associated with cancer.;Cell migration research has evolved into a well-developed subject area with a steadily growing number of papers being published over years (Fig. 1.1), that include measurement of cell motility parameters for different cell lines, as well as mathematical modeling at scales ranging from sub-cellular motility (signaling networks); cellular and population levels. In this work, we have developed a cellular scale approach to study eukaryotic cell migration by performing a model-based analysis of single cell migration experimental data, which can then be used to predict population properties. The focus will be on cellular-level migration with no particular emphasis on any sub-cellular or molecular detail. We have applied this analysis technique for both random and directed single cell migration in amoeba and in the process have tried to connect the two forms of motility that have often been studied separately. The phenotype-specific migration parameters of mammary epithelial cells were used to perform cellular dynamics simulations of these cells in order to predict population properties. Also, the search strategy of these cells in random motility conditions was explored to arrive at a bimodal-correlated random walk model. Lastly, the possibility of a temporal gradient sensing in mammary epithelial cells was briefly examined. |
