Tools for computational analysis of moving boundary problems in cellular mechanobiology

Three-component contour dynamics model to simulate and analyze amoeboid cell motility in two dimensions

Amoeboid cell motility is relevant in a wide variety of biomedical processes such as wound healing, cancer metastasis, and embryonic morphogenesis. It is characterized by pronounced changes of the cell shape associated with expansions and retractions of the cell membrane, which result in a crawling kind of locomotion. Despite existing computational models of amoeboid motion, the inference of expansion and retraction components of individual cells, the corresponding classification of cells, and the a priori specification of the parameter regime to achieve a specific motility behavior remain challenging open problems. We propose a novel model of the spatio-temporal evolution of two-dimensional cell contours comprising three biophysiologically motivated components: a stochastic term accounting for membrane protrusions and two deterministic terms accounting for membrane retractions by regularizing the shape and area of the contour. Mathematically, these correspond to the intensity of a self-exciting Poisson point process, the area-preserving curve-shortening flow, and an area adjustment flow. The model is used to generate contour data for a variety of qualitatively different, e.g., polarized and non-polarized, cell tracks that visually resemble experimental data very closely. In application to experimental cell tracks, we inferred the protrusion component and examined its correlation to common biomarkers: the F-actin density close to the membrane and its local motion. Due to the low model complexity, parameter estimation is fast, straightforward, and offers a simple way to classify contour dynamics based on two locomotion types: the amoeboid and a so-called fan-shaped type. For both types, we use cell tracks segmented from fluorescence imaging data of the model organism Dictyostelium discoideum. An implementation of the model is provided within the open-source software package AmoePy, a Python-based toolbox for analyzing and simulating amoeboid cell motility.

Trends in mechanobiology guided tissue engineering and tools to study cell-substrate interactions: a brief review

Sensing the mechanical properties of the substrates or the matrix by the cells and the tissues, the subsequent downstream responses at the cellular, nuclear and epigenetic levels and the outcomes are beginning to get unraveled more recently. There have been various instances where researchers have established the underlying connection between the cellular mechanosignalling pathways and cellular physiology, cellular differentiation, and also tissue pathology. It has been now accepted that mechanosignalling, alone or in combination with classical pathways, could play a significant role in fate determination, development, and organization of cells and tissues. Furthermore, as mechanobiology is gaining traction, so do the various techniques to ponder and gain insights into the still unraveled pathways. This review would briefly discuss some of the interesting works wherein it has been shown that specific alteration of the mechanical properties of the substrates would lead to fate determination of stem cells into various differentiated cells such as osteoblasts, adipocytes, tenocytes, cardiomyocytes, and neurons, and how these properties are being utilized for the development of organoids. This review would also cover various techniques that have been developed and employed to explore the effects of mechanosignalling, including imaging of mechanosensing proteins, atomic force microscopy (AFM), quartz crystal microbalance with dissipation measurements (QCMD), traction force microscopy (TFM), microdevice arrays, Spatio-temporal image analysis, optical tweezer force measurements, mechanoscanning ion conductance microscopy (mSICM), acoustofluidic interferometric device (AID) and so forth. This review would provide insights to the researchers who work on exploiting various mechanical properties of substrates to control the cellular and tissue functions for tissue engineering and regenerative applications, and also will shed light on the advancements of various techniques that could be utilized to unravel the unknown in the field of cellular mechanobiology.

A mesoscale mechanical model of cellular interactions

Computational models of cell mechanics allow the precise interrogation of cell shape change. These morphological changes are required for cells to survive in diverse tissue environments. Here, we present a mesoscale mechanical model of cell-substrate interactions using the level set method based on experimentally measured parameters. By implementing a viscoelastic mechanical equivalent circuit, we accurately model whole-cell deformations that are important for a variety of cellular processes. To effectively model shape changes as a cell interacts with a substrate, we have included receptor-mediated adhesion, which is governed by catch-slip bond behavior. The effect of adhesion was explored by subjecting cells to a variety of different substrates including flat, curved, and deformable surfaces. Finally, we increased the accuracy of our simulations by including a deformable nucleus in our cells. This model sets the foundation for further exploration into computational analyses of multicellular interactions.