A simulation that recapitulates the dynamics of PER-directed colloidal assembly

TL;DR

This paper presents a particle-based reaction-diffusion simulation that models long-term DNA-coated colloid self-assembly, accurately reproducing experimental structures and aiding in the design of hierarchical materials.

Contribution

It introduces a novel simulation approach capable of capturing long time-scale colloidal assembly dynamics, bridging a gap in existing computational methods.

Findings

Simulation reproduces core-shell structures observed experimentally.

Model captures emergence of compositional heterogeneity.

Simulation aids in designing self-assembly protocols.

Abstract

The self-assembly of DNA-coated colloids controlled by enzymatic reactions has the potential to enable the formation of materials with hierarchical organization and switchable configurations. However, the problem of designing such self-assembly is complex, and an effective simulation is necessary to assist in searching for appropriate design protocols. Typical computational methodologies such as molecular dynamics and Brownian dynamics have limited ability to access the long time scales required for these hierarchical self-assembly processes. Here we adopt a particle-based reaction-diffusion algorithm to model the spatial-temporal evolution of hundreds to thousands of micron-scale DNA-coated colloid self-assembly process over hours. In order to demonstrate the capability of this digital twin, we compared its predicted core-shell assembly process to results from experiments. The model can qualitatively reproduce the core-shell structures observed in experiment by recapitulating the emergence of compositional heterogeneity when delays between distinct assembly times are introduced. These results support the idea that this approach can successfully capture dynamics over long time scales and the appropriate scale of structure formation. We then use the model to explore different protocols for structure evolution, suggesting how this tool can aid in the design of complex self-organization processes.