The European Physical Journal E最新文献

Tumour growth involves dynamic interactions among tumour cells, extracellular materials, and host tissue. The tumour exerts mechanical stresses on the host tissue and simultaneously experiences compression across the tumour–host interface. This article presents a mathematical model that mimics an in vivo set-up, where an avascular tumour is surrounded by healthy/normal tissue, utilizing conservation principles for the constituents in each region. Tumour and host tissues are separately treated as biphasic mixtures comprising cells and extracellular materials. This study incorporates the diffusion-dominated transport and metabolism of cell-nourishing agents (CNA), such as oxygen, nutrients, and growth factors. The mechanical impact of normal host tissue on tumour growth dynamics while maintaining stress continuity at the tumour–host interface is analysed through numerical simulations. The key findings are that when CNA levels decline below a specific threshold, the tumour cell volume fraction decreases from the periphery to the centre, resulting in necrotic cell death alongside apoptosis. This study indicates that host tissue reduces CNA tension, accelerating tumour necrosis. The increased viscosity of normal host cells indicates stronger intercellular bonds, causing the cells to adhere more tightly and stiffen the host. With increasing viscosity-induced resistance, the host tissue more effectively impedes tumour expansion, thereby slowing tumour growth due to rising compressive stress. Analytical results for a solvable scenario are also provided to explore the comparative behaviour with numerical simulations of the complete model. Furthermore, analytical results indicate that an increased viscosity of normal host tissue may delay the initiation of necrotic cell death.

Higher host cell viscosity lowers the growth rate of an in vivo tumour

Tumor development is accompanied by strong physico-chemical modifications. Among them, compressive stress can emerge in both the epithelial and stromal compartments. Using a simple two-dimensional compression assay which consisted in placing an agarose weight on top of adherent cells, we studied the impact of compressive stress on cell proliferation and motility in different pancreatic cancer cell lines. We observed a proportional reduction of both proliferation and motility in all tested cell types, with genotypes displaying a more “mesenchymal” phenotype (high velocity-to-proliferation ratio) and others related to a more “epithelial” phenotype (low velocity-to-proliferation ratio). Moreover, “mesenchymal” cells seemed more sensitive to compression, a result that was further suggested by a TGF(mathrm {beta })1 induction of epithelial-to-mesenchymal transition. Finally, we measured that the change in cell proliferation was associated with a change in intracellular macromolecular crowding, which could modulate a plethora of biochemical reactions. Our results together suggest a mechanism in which all biochemical reactions related to proliferation and motility can be modulated by a change in macromolecular crowding, itself depending on the phenotype, leading to differential sensitivity to pressure.

Stability map on the plane Rayleigh number–solutal Rayleigh Number

This study examines double-diffusive convection in a horizontal fluid layer with low thermal conductivity boundaries, where the heat flux is fixed. Using linear stability analysis and nonlinear modeling, the behavior of the system is explored under different thermal and concentration gradients. Two instability modes are identified: monotonous and oscillatory. The monotonous mode, exhibiting longwave patterns, dominates when both gradients contribute to instability. The oscillatory mode occurs when the gradients oppose each other, with stability thresholds dependent on system parameters. Nonlinear modeling confirms the linear theory, showing longwave patterns near the instability threshold and oscillatory behavior when gradients are opposed. These findings offer insights into double-diffusive convection in systems with low thermal conductivity boundaries.

Stability map on the plane Rayleigh number–solutal Rayleigh Number

Tropomyosins are central regulators of the actin cytoskeleton, controlling the binding and activity of the other actin binding proteins. The interaction between tropomyosin and actin is quite unique: single tropomyosin dimers bind weakly to actin filaments but get stabilised by end-to-end attachment with neighbouring tropomyosin dimers, forming clusters which wrap around the filament. Force spectroscopy is a powerful approach for studying protein–protein interactions, but classical methods which usually pull with pN forces on a single protein pair, are not well adapted to tropomyosins. Here, we propose a method in which a hydrodynamic drag force is applied directly to the proteins of interest, by imposing a controlled fluid flow inside a microfluidic chamber. The breaking of the protein bonds is directly visualised with fluorescence microscopy. Using this approach, we reveal that very low forces from 0.01 to 0.1 pN per tropomyosin dimer trigger the detachment of entire tropomyosin clusters from actin filaments. We show that the tropomyosin cluster detachment rate depends on the cytoplasmic tropomyosin isoform (Tpm1.6, 1.7, 1.8) and increases exponentially with the applied force. These observations lead us to propose a cluster detachment model which suggests that tropomyosins dynamically explore different positions over the actin filament. Our experimental setup can be used with many other cytoskeletal proteins, and we show, as a proof-of-concept, that the velocity of myosin-X motors is reduced by an opposing fluid flow. Overall, this method expands the range of protein–protein interactions that can be studied by force spectroscopy.

Many microswimmers are able to swim through viscous fluids by employing periodic non-reciprocal deformations of their appendages. Here we use a simple microswimmer model inspired by swimming biflagellates which consists of a spherical cell body and two small spherical beads representing the motion of the two flagella. Using reinforcement learning, we identify for different microswimmer morphologies quasi-optimized swimming strokes. For all studied cases, the identified strokes result in symmetric and quasi-synchronized beating of the two flagella beads. Interestingly, the stroke-averaged flow fields are of pusher type, and the observed swimming gaits outperform previously used biflagellate microswimmer models relying on predefined circular flagella-bead motion.

Metachronal paddling is a swimming strategy in which an organism oscillates sets of adjacent limbs with a constant phase lag, propagating a metachronal wave through its limbs and propelling it forward. This limb coordination strategy is utilized by swimmers across a wide range of Reynolds numbers, which suggests that this metachronal rhythm was selected for its optimality of swimming performance. In this study, we apply reinforcement learning to a swimmer at zero Reynolds number and investigate whether the learning algorithm selects this metachronal rhythm, or if other coordination patterns emerge. We design the swimmer agent with an elongated body and pairs of straight, inflexible paddles placed along the body for various fixed paddle spacings. Based on paddle spacing, the swimmer agent learns qualitatively different coordination patterns. At tight spacings, a back-to-front metachronal wave-like stroke emerges which resembles the commonly observed biological rhythm, but at wide spacings, different limb coordinations are selected. Across all resulting strokes, the fastest stroke is dependent on the number of paddles; however, the most efficient stroke is a back-to-front wave-like stroke regardless of the number of paddles.