Programming Boundary Deformation Patterns in Active Networks

TL;DR

This paper presents an optical control protocol to engineer and shape the contraction dynamics of active microtubule networks, enabling programmable deformation patterns and advancing understanding of active material behavior.

Contribution

It introduces a novel optical control method to shape and manipulate active networks, revealing shape-preserving contraction and linking it to active stresses.

Findings

Active networks contract in a shape-preserving manner.

Self-similarity of contraction is linked to viscous-like active stresses.

The protocol allows programmable bending and directional control.

Abstract

Active materials take advantage of their internal sources of energy to self-organize in an automated manner. This feature provides a novel opportunity to design micron-scale machines with minimal required control. However, self-organization goes hand in hand with predetermined dynamics that are hardly susceptible to environmental perturbations. Therefore utilizing this feature of active systems requires harnessing and directing the macroscopic dynamics to achieve specific functions; which in turn necessitates understanding the underlying mechanisms of active forces. Here we devise an optical control protocol to engineer the dynamics of active networks composed of microtubules and light-activatable motor proteins. The protocol enables carving activated networks of different shapes, and isolating them from the embedding solution. Studying a large set of shapes, we observe that the active networks contract in a shape-preserving manner that persists over the course of contraction. We formulate a coarse-grained theory and demonstrate that self-similarity of contraction is associated with viscous-like active stresses. These findings help us program the dynamics of the network through manipulating the light intensity in space and time, and maneuver the network into bending in specific directions, as well as temporally alternating directions. Our work improves understanding the active dynamics in contractile networks, and paves a new path towards engineering the dynamics of a large class of active materials.