A Comparison of Instabilities and Dynamic States in Active Filament Models

TL;DR

This study compares the dynamic behaviors of active filament models under different actuation mechanisms, revealing how surface flows and actuation distribution influence stability, bifurcations, and complex motion states.

Contribution

It provides a systematic comparison of active filament models with various actuation types, highlighting how surface flows and actuation distribution affect dynamic states and bifurcation behavior.

Findings

Distributed actuation leads to a single, rotating helical state.

Tip actuation results in complex, chaotic transitions.

Differences in internal stress distributions explain dynamic variations.

Abstract

Active filaments, such as microtubules with attached cargo-carrying motor proteins, are important dynamic structures for fluid transport in and around living cells. The mathematical models of active filaments appearing in the literature typically involve combinations of follower-forces, compressive tangential forces, along the filament, and an opposite force on the fluid that generates an effective surface flow. In this paper, we present a comparative dynamical systems study of active filament models examining the differences in dynamic states that occur when actuation is through follower forces alone, or the effect of surface flows is also included. We consider cases where actuation is applied only at the filament tip, or distributed uniformly along the filament length. By varying actuation strength, we show that the first bifurcations that provide the transition between the upright, whirling and beating states appear in all models. At higher values of actuation, when beating becomes unstable, however, qualitative differences between the models emerge. Those with distributed actuation produce a single, time-dependent state, which for the surface flow model is reminiscent of a rotating helix that periodically changes handedness and rotation direction. Tip actuation, however, yields complex transitions that ultimately produce a chaotic state. We link the differences in dynamics between tip and distributed actuation to differences in their respective internal stress distributions, differences which appear as early as the first bifurcation where they affect the shapes of the unstable modes.