A surprising number of physical interactions are essential in biological systems, and cells make full of advantage of these interactions to change shape, import and export signals, and move through their environment. Have you ever wondered how individual cells crawl over surfaces or change direction in response to their environment? Many cellular functions such as these require the coordination of many different proteins throughout the interior of the cell. The individual proteins themselves are small, typically nanometers in size, but their collective influence elicits cell-scale changes on the order of  tens of microns. One way proteins can collectively span size-scales is by forming large superstructures, such as those found in the eukaryotic cytoskeleton. The Computational Physics Lab group at Kettering University uses a combination of image analysis, mathematical modelling, and computer simulation to try to understand how individual proteins coordinate to influence cellular structure and function.

Advances in the use of fluorescent proteins as markers allow scientists to observe how proteins are organized over both space and time within living cells. Under certain conditions, scientists can even track the movement of individual proteins within a cell. Sometimes, however, the observed patterns of protein organization raise more questions than they answer! This is where theoretical models can help. Although the Computational Physics Lab group does not currently conduct experiments on live cells, measurements made with fluorescent microscopy provide us with necessary information for developing useful, meaningful models of cellular dynamics that investigate the interactions of many proteins within cells.


In addition to modelling the collective behavior of proteins within cells, the Computational Physics Lab group at Kettering University investigates pattern formation within other systems as well. Current projects investigate quantitative comparisons of the shape and morphology of macroscopic objects, such as leaves or flower petals, or microscopic entities such as cells within tissues. The use of computational methods for quantitative image analysis allows for automation of measurements, and the use of machine learning strategies streamlines both data analysis and interpretation.