Data-Driven Computational Simulation in Bone Mechanics.

The data-driven approach was formally introduced in the field of computational mechanics just a few years ago, but it has gained increasing interest and application as disruptive technology in many other fields of physics and engineering. Although the fundamental bases of the method have been already settled, there are still many challenges to solve, which are often inherently linked to the problem at hand. In this paper, the data-driven methodology is applied to a particular problem in tissue biomechanics, a context where this approach is particularly suitable due to the difficulty in establishing accurate and general constitutive models, due to the intrinsic intra and inter-individual variability of the microstructure and associated mechanical properties of biological tissues. The problem addressed here corresponds to the characterization and mechanical simulation of a piece of cortical bone tissue. Cortical horse bone tissue was mechanically tested using a biaxial machine. The displacement field was obtained by means of digital image correlation and then transformed into strains by approximating the displacement derivatives in the bone virtual geometric image. These results, together with the approximated stress state, assumed as uniform in the small pieces tested, were used as input in the flowchart of the data-driven methodology to solve several numerical examples, which were compared with the corresponding classical model-based fitted solution. From these results, we conclude that the data-driven methodology is a useful tool to directly simulate problems of biomechanical interest without the imposition (model-free) of complex spatial and individually-varying constitutive laws. The presented data-driven approach recovers the natural spatial variation of the solution, resulting from the complex structure of bone tissue, i.e. heterogeneity, microstructural hierarchy and multifactorial architecture, making it possible to add the intrinsic stochasticity of biological tissues into the data set and into the numerical approach.

Computational modelling and analysis of mechanical conditions on cell locomotion and cell-cell interaction.

Between other parameters, cell migration is partially guided by the mechanical properties of its substrate. Although many experimental works have been developed to understand the effect of substrate mechanical properties on cell migration, accurate 3D cell locomotion models have not been presented yet. In this paper, we present a novel 3D model for cells migration. In the presented model, we assume that a cell follows two main processes: in the first process, it senses its interface with the substrate to determine the migration direction and in the second process, it exerts subsequent forces to move. In the presented model, cell traction forces are considered to depend on cell internal deformation during the sensing step. A random protrusion force is also considered which may change cell migration direction and/or speed. The presented model was applied for many cases of migration of the cells. The obtained results show high agreement with the available experimental and numerical data.

Cell migration and cell-cell interaction in the presence of mechano-chemo-thermotaxis.

Although there are several computational models that explain the trajectory that cells take during migration, till now little attention has been paid to the integration of the cell migration in a multi-signaling system. With that aim, a generalized model of cell migration and cell-cell interaction under multisignal environments is presented herein. In this work we investigate the spatio-temporal cell-cell interaction problem induced by mechano-chemo-thermotactic cues. It is assumed that formation of a new focal adhesion generates traction forces proportional to the stresses transmitted by the cell to the extracellular matrix. The cell velocity and polarization direction are calculated based on the equilibrium of the effective forces associated to cell motility. It is also assumed that, in addition to mechanotaxis signals, chemotactic and thermotactic cues control the direction of the resultant traction force. This model enables predicting the trajectory of migrating cells as well as the spatial and temporal distributions of the net traction force and cell velocity. Results indicate that the tendency of the cells is firstly to reach each other and then migrate towards an imaginary equilibrium plane located near the source of the signal. The position of this plane is sensitive to the gradient slope and the corresponding efficient factors. The cells come into contact and separate several times during migration. Adding other cues to the substrate (such as chemotaxis and/or thermotaxis) delays that primary contact. Moreover, in all states, the average local velocity and the net traction force of the cells decrease while the cells approach the cues source. Our findings are qualitatively consistent with experimental observations reported in the related literature.