Mechanistic Framework for Multicomponent Nanoparticle Assembly: Predicting RNA-lipid and PEI-DNA nanoparticle assembly
Abstract
The assembly of multicomponent nanoparticles is often kinetically controlled and exhibits strong pathway dependence. Transport, solvent exchange, nucleation/growth, and collision-driven coalescence together determine not only ensemble-averaged properties but also particle-to-particle compositional heterogeneity. Here, we present a computational modeling framework for predicting nanoparticle property distributions by coupling processing conditions, early-stage self-assembly physics, and molecular chemical details with kinetic Monte Carlo (kMC) simulations. The framework combines (i) mixing conditions with solvent-exchange-mediated particle initialization and growth, and (ii) kMC simulations that resolve stochastic collision histories, electrostatics-controlled coalescence, and composition at the level of individual particles. Applied to mRNA lipid nanoparticles, the model predicts size-loading correlations and provides insight into how processing-dependent assembly pathways lead to heterogeneous payload distributions. The kMC simulations further provide merging lineage histories, which explain the emergence of log-normal volume and payload distributions through multiplicative particle-growth pathways. The same framework is also applied to PEI-DNA polyelectrolytic complexation, yielding single-particle-resolved DNA-PEI stoichiometry distributions. The framework and its open-source implementation, FormLNP, provide a process-aware route to predicting and controlling single-particle property distributions across a broad range of multicomponent nanoparticle systems.
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BibTeX
@misc{pial2026mechanistic,
title = {{Mechanistic Framework for Multicomponent Nanoparticle Assembly: Predicting RNA-lipid and PEI-DNA nanoparticle assembly}},
author = {Turash Haque Pial and Tine Curk},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.04029},
eprint = {2609.04029},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.04029},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - UNPB
TI - Mechanistic Framework for Multicomponent Nanoparticle Assembly: Predicting RNA-lipid and PEI-DNA nanoparticle assembly
AU - Turash Haque Pial
AU - Tine Curk
PY - 2026
DA - 2026/09/03
PB - arXiv
DO - 10.48550/arxiv.2609.04029
UR - https://arxiv.org/abs/2609.04029
AB - The assembly of multicomponent nanoparticles is often kinetically controlled and exhibits strong pathway dependence. Transport, solvent exchange, nucleation/growth, and collision-driven coalescence together determine not only ensemble-averaged properties but also particle-to-particle compositional heterogeneity. Here, we present a computational modeling framework for predicting nanoparticle property distributions by coupling processing conditions, early-stage self-assembly physics, and molecular chemical details with kinetic Monte Carlo (kMC) simulations. The framework combines (i) mixing conditions with solvent-exchange-mediated particle initialization and growth, and (ii) kMC simulations that resolve stochastic collision histories, electrostatics-controlled coalescence, and composition at the level of individual particles. Applied to mRNA lipid nanoparticles, the model predicts size-loading correlations and provides insight into how processing-dependent assembly pathways lead to heterogeneous payload distributions. The kMC simulations further provide merging lineage histories, which explain the emergence of log-normal volume and payload distributions through multiplicative particle-growth pathways. The same framework is also applied to PEI-DNA polyelectrolytic complexation, yielding single-particle-resolved DNA-PEI stoichiometry distributions. The framework and its open-source implementation, FormLNP, provide a process-aware route to predicting and controlling single-particle property distributions across a broad range of multicomponent nanoparticle systems.
ER -