Mobility quantification of single cells and cellular processes in dense cultures is a challenge, because single cell tracking is impossible. We developed a software for cell structure segmentation and implemented 2 algorithms to measure motility speed. Complex algorithms were tested to separate cells and cellular components, an important prerequisite for the acquisition of meaningful motility data. Plasma membrane segmentation was performed to measure membrane contraction dynamics and organelle trafficking. The discriminative performance and sensitivity of the algorithms were tested on different cell types and calibrated on computer-simulated cells to obtain absolute values for cellular velocity. Both motility algorithms had advantages in different experimental setups, depending on the complexity of the cellular movement. The correlation algorithm (COPRAMove) performed best under most tested conditions and appeared less sensitive to variable cell densities, brightness and focus changes than the differentiation algorithm (DiffMove). In summary, our software can be used successfully to analyze and quantify cellular and subcellular movements in dense cell cultures.

We present a multiscale modeling framework to predict the adhesion of a model particles from nano-to-micron scales of different shapes and compliances that mimic functionalized nanoparticles to biological cells by virtue of being decorated with receptors/antibodies in their surface. The model derives cell surface receptor interactions with functionalized peptides using atomistic simulations. These interaction potentials are then fit to predefined functions which are used to characterize the interaction between the particle/cells with substrates and to measure the free energy of adhesion/binding constants. Further, in this framework we also account for variations in the receptor ligand interactions due to the inherent compliance in the bilayer membranes on which the receptors are tethered. These calculations are performed using a standalone framework for estimating effect of membrane shape/curvature on the flexibility of the receptors. This membrane-compliance effect shows that decrease in membrane area results in increased receptor rigidity which ends up limiting the overall adhesion of the receptors to the functionalized surfaces or in other words much weaker dissociation constants (i.e. mM as opposed to microM). In this work we show a computational tool where we couple chemistry of peptide, membrane fluctuations and cellular sizes to estimate the overall binding affinities of these model cells to functionalized surfaces. We also showcase the power of this framework in simulating realistic heterogeneity in cell surface receptors by demonstrating effect of a combination of weak and strong receptors in modulating the overall binding affinity of the cell. The model as it stands can be a tool for development of functionalized surfaces aimed at cell based assays and or cell based therapies.