Rigidity of Epithelial Tissues as a Double Optimization Problem
arXiv:2312.11683 · doi:10.1103/PhysRevResearch.7.013157
Abstract
How do cells tune emergent properties at the scale of tissues? One class of such emergent behaviors are rigidity transitions, in which a tissue changes from a solid-like to a fluid-like state or vice versa. Here, we introduce a new way for a tissue described by a vertex model to tune its rigidity, by using ``tunable degrees of freedom." We use the vertex model elastic energy as a cost function and the cell stiffnesses, target shapes, and target areas as different sets of degrees of freedom describing cell-cell interactions that can be tuned to minimize the cost function. We show that the rigidity transition is unaffected when cell stiffnesses are treated as tunable degrees of freedom. When preferred shapes or areas are treated as tunable degrees of freedom, however, induced spatial correlations in target cell shapes or areas shift the rigidity transition. These observations suggest that tissues can coordinate changes in cell-scale properties, treated here as tunable degrees of freedom, to achieve desired tissue-scale behaviors.
References in corpus (25)
- A density-independent glass transition in biological tissues
- Motility-driven glass and jamming transitions in biological tissues
- Strain-controlled criticality governs the nonlinear mechanics of fibre networks
- Designing allostery-inspired response in mechanical networks
- Anisotropy links cell shapes to tissue flow during convergent extension
- A geometrically controlled rigidity transition in a model for confluent 3D tissues
- A minimal-length approach unifies rigidity in under-constrained materials
- Learning without neurons in physical systems
- cellGPU: massively parallel simulations of dynamic vertex models
- Directed aging, memory and Nature's greed
- Mechanical heterogeneity in tissues promotes rigidity and controls cellular invasion
- Demonstration of Decentralized, Physics-Driven Learning
- The limits of multifunctionality in tunable networks
- Supervised learning in physical networks: From machine learning to learning machines
- Scaling theory for mechanical critical behavior in fiber networks
- Free-then-freeze: transient learning degrees of freedom for introducing function in materials
- A Geometric Tension Dynamics Model of Epithelial Convergent Extension
- Machine Learning Without a Processor: Emergent Learning in a Nonlinear Electronic Metamaterial
- Effect of aging on the non-linear elasticity and memory formation in materials
- Confluent and non-confluent phases in a model of cell tissue
- Desynchronous Learning in a Physics-Driven Learning Network
- Finite size effects in critical fiber networks
- Linking microscopic and macroscopic response in disordered solids
- The Physical Effects of Learning
- Tissue flow induces cell shape changes during organogenesis