Structural hierarchy confers error tolerance in biological tissues

TL;DR

This paper presents a generalized mechanical model demonstrating that hierarchical structures in biological tissues inherently confer error tolerance, explaining their robustness despite assembly complexities.

Contribution

It introduces a new simulation-based model extending network connectivity concepts to hierarchical tissues, revealing reduced sensitivity to assembly errors with increased hierarchy levels.

Findings

Stiffness depends similarly on each hierarchical level.

Error sensitivity decreases as hierarchy levels increase.

Model predictions are analytically derived and likely broadly applicable.

Abstract

Structural hierarchy, in which materials possess distinct features on multiple length scales, is ubiquitous in nature; diverse biological materials, such as bone, cellulose, and muscle, have as many as ten hierarchical levels. Structural hierarchy confers many mechanical advantages, including improved toughness and economy of material. However, it also presents a problem: each hierarchical level adds a new source of assembly errors, and substantially increases the information required for proper assembly. This seems to conflict with the prevalence of naturally occurring hierarchical structures, suggesting that a common mechanical source of hierarchical robustness may exist. However, our ability to identify such a unifying phenomenon is limited by the lack of a general mechanical framework for structures exhibiting organization on disparate length scales. Here, we use simulations to substantiate a generalized model for the tensile stiffness of hierarchical, stretching-stabilized, filamentous networks with a nested, dilute triangular lattice structure. Following seminal work by Maxwell and others on criteria for stiff frames, we extend the concept of connectivity in network mechanics, and find a mathematically similar dependence of material stiffness upon each hierarchical level. Using this model, we find that the stiffness of such networks becomes less sensitive to errors in assembly with additional levels of hierarchy; though surprising, we show that this result is analytically predictable from first principles, and thus likely model-independent. More broadly, this work helps account for the success of hierarchical, filamentous materials in biology and materials design, and offers a heuristic for ensuring that desired material properties are achieved within the required tolerance.