Nonequilibrium self-assembly of multiple stored targets

Many biological systems rely on the ability to self-assemble different target structures using the same set of components. Equilibrium self-assembly suffers from a limited capacity in such cases due to an increasing number of decoy states that grows rapidly with the number of targets encoded. Moreover, improving the kinetic stability of a target at equilibrium carries the price of introducing kinetic traps, leading to slower assembly. Using a toy physical model of interacting particles, we demonstrate that local driving can improve both the assembly time and kinetic stability of multitarget self-assembly. Our findings also extend to 3D simulations of patchy particle systems in biological-mimicking environments. We further show that a segmented description of the system dynamics, by the Bayesian estimator of abrupt changes (BEAST) algorithm, can be used to provide predictions of the first assembly times. Our results illustrate the role that nonequilibrium driving plays in overcoming tradeoffs inherent to equilibrium assemblies and establish a general quantitative framework for nonequilibrium systems.