Stochastic cycle selection in active flow networks.

Author: 

Woodhouse, F
Forrow, A
Fawcett, J
Dunkel, J

Journal: 

Proceedings of the National Academy of Sciences of the United States of America

Publication Date: 

5 July 2016

Last Updated: 

2019-04-17T08:29:08.91+01:00

Issue: 

29

DOI: 

10.1073/pnas.1603351113

Volume: 

113

page: 

8200-8205

abstract: 

Active biological flow networks pervade nature and span a wide range of scales, from arterial blood vessels and bronchial mucus transport in humans to bacterial flow through porous media or plasmodial shuttle streaming in slime molds. Despite their ubiquity, little is known about the self-organization principles that govern flow statistics in such nonequilibrium networks. Here we connect concepts from lattice field theory, graph theory, and transition rate theory to understand how topology controls dynamics in a generic model for actively driven flow on a network. Our combined theoretical and numerical analysis identifies symmetry-based rules that make it possible to classify and predict the selection statistics of complex flow cycles from the network topology. The conceptual framework developed here is applicable to a broad class of biological and nonbiological far-from-equilibrium networks, including actively controlled information flows, and establishes a correspondence between active flow networks and generalized ice-type models.

Symplectic id: 

940510

Submitted to ORA: 

Submitted

Publication Type: 

Journal Article