ZIH-Kolloquium - Image-based modelling of problems in cell motility

Image-based modelling of problems in cell motility
Mathematical modelling has been key to understanding the mechanics of cell shapes and theoretical principles behind the complex spatio-temporal biochemical regulation of directed cell motility. Although live cell fluorescence microscopy allows to visualise cellular dynamics in great detail, most models are phenomenological in nature, because extracting quantitative data from time series images of moving cells is challenging. We have been developing image analysis software (QuimP, Baniukiewicz et al., 2018) to map cell shape changes and fluorescence distributions of cytoskeletal proteins in the actin cortex of Dictyostelium cells. I am going to present work on fitting experimental data obtained by QuimP to mathematical models of 1) cells undergoing reorientation in response to alternating gradients of hydrodynamic shear flow (Lockley et al., 2015) and 2) blebbing in cells moving under a sheet of agarose (Collier et al., 2017). Here, a main result is that cell geometry (negative curvature) plays a previously under-appreciated role in determining where cells bleb. This will be followed by an outlook on challenges arising from 3D imaging of single cells obtained using light sheet microscopy (diSPIM and lattice light sheet), and GPU based approaches for image segmentation. Deep convolutional neural networks for creating synthetic 3D images of cells with protein distributions learned from experimental data are turning out to be a novel, useful tool for validating different segmentation routines.