Mechanical ventilation is a life-saving therapeutic tool for treating patients with impaired pulmonary function. Despite the clear benefits of mechanical ventilation, it can cause irreversible damage to the lung tissue. The main obstacles for more protective and individualized ventilation strategies are the still insufficient knowledge and understanding of complex lung mechanics in both healthy and diseased states, which is mainly due to the limited ability for in vivo measurement. To shed light into this issue, great efforts have been made in the past in the development of computational lung models. Even most of advanced work in this context only focus on investigating the effect of ventilation on tissue strains and stresses, while coupling to the pulmonary circulation is mostly neglected so far. This is despite the fact that the main function of the lungs, namely gas exchange, takes place through a dense network of pulmonary blood vessels in the alveolar walls. Hence, the coupling between the respiratory system and pulmonary circulation is crucial for getting more insight into the main purpose of ventilation: adequate oxygen supply and carbon dioxide release, while keeping the tissue at a healthy state.
In this contribution we therefore present a physics-based, coupled porous media approach to computationally model air flow, blood flow, and gas exchange in the human lungs. Motivated by the structure of the lungs, larger airways and blood vessels are modeled as discrete zero-dimensional (0D) networks that are embedded into a three-dimensional (3D), three-phase (air, blood and tissue) porous medium, representing the smaller airways, smaller blood vessels and lung tissue in a homogenized manner. Further, the respiratory gases, oxygen and carbon dioxide, are modeled as chemical subcomponents of air and blood with a suitable exchange model in the porous domain. To connect the homogenized and the discrete representations of airways and blood vessels, respectively, a 0D-3D coupling method is used, which allows a non-matching spatial discretization of both domains. The method couples fluid flow and species transport in these phases via an outflow condition from the tips of the discrete networks into the 3D porous medium and vice versa.
Such a comprehensive coupled approach allows us to study the complex interplay of tissue deformation and perfusion, and its effects on oxygenation and carbon dioxide release. Further, the underlying multiphase porous media model can easily be extended to include additional phases, so that pathological conditions such as water accumulation in pulmonary edema can be studied in future stages of the model. We consider our model to be a promising base for investigating clinically relevant questions, which might contribute to an improved treatment in respiratory care.

Mechanosensing of cells to the surrounding material is crucial for their physiological and pathological processes. The emergent dynamics of cells arise from a variety of interactions between cells and their local environment. However, materials design to guide cellular responses is largely ad hoc due to the lack of comprehensive modelling techniques for quantitative understanding. In this paper, we propose a computational model, that couples cell dynamics and cell-materials interactions, to study the mechanosensing of fibroblast cells seeded on different hydrogels.

In recent years, micromechanism-based theoretical modelling has been proposed to capture the essential biophysical characteristics, such as the generation of contractile forces in the cytoskeleton (CSK) and cell–substrate interaction. The contractile force of a cell is primarily generated by the intracellular stress fibers (SFs). SFs are formed via phosphorylation of myosin and polymerization of actin filaments. Actin filaments in cytoplasm are connected by α-actinin proteins to form actin bundles, which are crosslinked by myosin II proteins. The actin-myosin structure generates contraction forces in CSK by the ‘walking’ of myosin II along the actin filaments. Deshpande et al. (2007) proposed a bio-chemo-mechanical model to capture these key mechanisms.

In our continuum model, we consider the following important features of SF formation: (1) an activation signal is essential to trigger the formation of actin-myosin contractile units, (2) SF association rate is dependent on the activation signal and the dissociation rate is dependent on the contractile force, and (3) the dynamic contraction of SF is similar to the well-established muscle contraction model, which can be modelled by a modified Hill model.

For the cell-substrate interactions, we have considered both specific interactions (i.e., binding of membrane molecules to substrate ligands) and nonspecific interactions (e.g., van der Waals, electrostatic forces, hydrogen bonding and steric repulsion) (Bell et al., 1984). The assembly of active/specific focal adhesion (FA) is represented by the aggregation of integrins on the membrane and binding to substrate ligands. A thermodynamic model to represent the chemical equilibrium between these two integrin states (Deshpande et al., 2008; McEvoy et al., 2017), has been adapted in this work.

This coupled model allows us to predict the coupled effects of substrate stiffness and thickness on stress fiber formation and disassociation, and affinity integrin density. We also examine the effect of substrate on the cell-cell communications of fibroblast cells.

Our modeling results have revealed that a cell can sense its neighboring cell by deforming the underlying substrate. Our simulations also provide physical insights in the enhanced mechanosensing capacity of collective cells. The present modelling framework is not only important for profound understanding of cell mechanosensing, but also has the potential to guide the rationale design of biomaterials for tissue engineering and wound healing.

The physiological behaviour of the cardiovascular system is highly affected by the mechanical response of arterial segments, that is in turn dependent from both tissue histological architecture and the contractile tone of smooth muscle cells. The former depends mainly on the different amount and arrangement of constituents (mainly, elastin and collagen fibers), while the latter on chemical drivers of vasoactivity, such as nitric oxide (NO) and reactive oxygen species (e.g., ROS and PN). It is also noteworthy that the problem is highly multiscale since global hemodynamic conditions (e.g., heart rates, resistance of downstream vasculature) highly affect local flow conditions, and hence the local pressure field and the internal stresses affecting biochemical pathways governing vascular tone. Detailed high dimensional models (2D or 3D) can generally be used to simulate local hemodynamics of specific arterial sites, while the whole arterial tree is generally described through low dimensional descriptions (i.e., lumped 1D approaches).

This work presents a comprehensive multi-scale and multi-field computational framework that accounts for: i) a lumped 1D description of the macroscale arterial tree; ii) a continuum 3D model at the microscale of the local chemo-mechano-biological response of arterial tissues (accounting for passive and active tissue behavior); iii) biochemical-dependent vasoconstriction and vasodilation (the NO-ROS-PN biochemical chain), and biochemical-dependent tissue remodeling (the GFs- MMPs biochemical chain). Simulations from 3D chemo-mechano-biological models drive how parameters of the lumped description vary as function of segment dilation, as well as tissue histology and vasoconstriction.

The applicative case study investigates the relationship between arterial vasodilation and vasoconstriction with physical exercise. The obtained numerical results are consistent with available experimental data for normal and spontaneously hypertensive phenomena.

Growing trees respond to mechanical disturbances, resulting, e.g., from environmental forces or gravity, by forming so-called reaction wood. The latter is called compression wood in gymnosperms and tension wood in angiosperms. It enables the tree to control its posture by reinforcing and reorienting the axes of stems and branches, which is a key prerequisite for reaching large heights. The movement is due to asymmetric cambial activity resulting in eccentric growth and varying growth strains. While the underlying biological mechanisms of growth strain generation are not yet fully understood, various hypotheses correlating the induced macroscopic movement with the difference in cell wall structure of reaction and non-reaction wood have been proposed. On that basis, a homogenization procedure was developed for upscaling and evaluating the macroscopic effect of growth strains implemented at the cell wall level. In particular, we aim at unraveling the effect of the characteristic composition and microstructure of tension wood containing G-layers on the elastic properties and macroscopic growth strains by employing multiscale homogenization modeling techniques, based on the concept of continuum micromechanics. A three-step homogenization scheme is employed to estimate the elastic properties of the cell wall layers based on the volume fractions of elementary components. The structural organization at the cell wall level is represented by multilayered cylindrical inclusions exhibiting transversely isotropic material behavior taking into account the previously evaluated layer properties depending on the composition of their elementary constituents, such as cellulose, hemicellulose, lignin and water. We derive the stress and strain fields corresponding to four different loading conditions, which allows for constructing the complete stiffness tensor of tension wood and upscaling the growth strains induced within the G-layer. Coupling the micromechanics model with non-linear beam mechanics, applied to a growing branch inclined with respect to the vector of gravity, allows to simulate the reorientation process induced by growth strains at the cell wall level. In combination with experimental data of the branch shape evolution found in literature of specific species, growth-related parameters can be deduced, which may lead, in further consequence, to a better understanding and predictability of the growth process.