Wound opening in a thin incompressible viscoelastic tissue

Sublinear drag regime at mesoscopic scales in viscoelastic materials

Stressed soft materials commonly present viscoelastic signatures in the form of power-law or exponential decay. Although exponential responses are the most common, power-law time dependencies arise peculiarly in complex soft materials such as living cells. Understanding the microscale mechanisms that drive rheologic behaviors at the macroscale shall be transformative in fields such as material design and bioengineering. Using an elastic network model of macromolecules immersed in a viscous fluid, we numerically reproduce those characteristic viscoelastic relaxations and show how the microscopic interactions determine the rheologic response. The macromolecules, represented by particles in the network, interact with neighbors through a spring constant k and with fluid through a non-linear drag regime. The dissipative force is given by γvα, where v is the particle's velocity, and γ and α are mesoscopic parameters. Physically, the sublinear regime of the drag forces is related to micro-deformations of the macromolecules, while α ≥ 1 represents rigid cases. We obtain exponential or power-law relaxations or a transitional behavior between them by changing k, γ, and α. We find that exponential decays are indeed the most common behavior. However, power laws may arise when forces between the macromolecules and the fluid are sublinear. Our findings show that in materials not too soft not too elastic, the rheological responses are entirely controlled by α in the sublinear regime. More specifically, power-law responses arise for 0.3 ⪅ α ⪅ 0.45, while exponential responses for small and large values of α, namely, 0.0 ⪅ α ⪅ 0.2 and 0.55 ⪅ α ⪅ 1.0.

Combining experiments and in silico modeling to infer the role of adhesion and proliferation on the collective dynamics of cells

The collective dynamics of cells on surfaces and interfaces poses technological and theoretical challenges in the study of morphogenesis, tissue engineering, and cancer. Different mechanisms are at play, including, cell–cell adhesion, cell motility, and proliferation. However, the relative importance of each one is elusive. Here, experiments with a culture of glioblastoma multiforme cells on a substrate are combined with in silico modeling to infer the rate of each mechanism. By parametrizing these rates, the time-dependence of the spatial correlation observed experimentally is reproduced. The obtained results suggest a reduction in cell–cell adhesion with the density of cells. The reason for such reduction and possible implications for the collective dynamics of cancer cells are discussed.