The scattered light pattern from a biological cell is greatly influenced by the internal structure and optical properties of the cell. This research project examines the relationships between the morphological and scattering properties of biological cells through numerical simulations. The mains goals are: (1) to develop a procedure to analytically model biological cells, (2) to quantitatively study the effects of a range of cell characteristics on the features of the light scattering patterns, and (3) to classify cells based on the features of their light scattering patterns. A procedure to create an analytical cell model was developed which extracted structural information from the confocal microscopic images of cells and allowed for the alteration of the cell structure in a controlled and systematic way. The influence of cell surface roughness, nuclear size, and mitochondrial volume density, spatial distribution, size and shape on the light scattering patterns was studied through numerical simulations of light scattering using the Discrete Dipole Approximation. It was found that the light scattering intensity in the scattering angle range of 25° to 45° responded to changes in the surface fluctuation of the cell and the range of 90° to 110° was well suited for characterization of mitochondrial density and nuclear size. A comparison of light scattering pattern analysis methods revealed that the angular distribution of the scattered light and Gabor filters were most helpful in differentiating between the cell characteristics. In addition, a measured increase in the Gabor energy of the light scattering patterns in response to an increase in the complexity of the cell models suggested that a complex nuclear structure and mitochondria should be included when modeling biological cells for light scattering simulations. Analysis of the scattering pattern features with Gabor filters resulted in discrimination of the cell models according to cell surface roughness, nuclear size, and mitochondrial volume density and size with over 90% classification accuracy. This study suggested the location of the scattering planes that are most relevant to researchers depending on the desired information about the cell and may provide a quantitative approach to cell discrimination with practical applications in flow cytometry for the diagnosis of diseases.