MSE Research Project Database

Nanoparticle dosimetry prediction

Project Leader: Edmund Crampin
Staff: Frank Caruso
Student: Matt Faria
Sponsors: ARC Centre of Excellence in Convergent Bio Nano Science and Technology
Primary Contact: Edmund Crampin (
Keywords: nanotechnology; numerical modelling; systems biology
Disciplines: Biomedical Engineering,Chemical & Biomolecular Engineering
Research Centre: Systems Biology Laboratory

Determining the affinity of a nanoparticle for a cell is an important step in characterizing the biological activity of that nanoparticle. This is usually determined in vitro by incubating cells with a solution of particles and then measuring cellular association or uptake. Comparing these measurements for different particle types is desirable. Unfortunately, it does not make sense to compare them directly, as the physical properties of the system can lead to substantially different amounts of particles being presented to the cell. For instance, consider a solution with particles that sediment rapidly compared to a well-dispersed colloidal suspension of particles. Clearly, if incubated in vitro with cells at the bottom of a well, the former solution will sediment onto the cells and a greater percentage of particles will be presented. This is recognized as a problem in the field, but attempts to characterize, control for, or model these effects are rare. We’re building computational models using partial differential equations to predict bulk movement of particles in solution. This will allows us to predict the presentation of particles to cells based on their physical properties, and by extension, to control for these physical effects and compare different particles. We currently incorporate the physical effects of a sphere moving in fluid, but this work provides a framework to investigate and include additional parameters, such as electrostatic interactions and variations due to cell type.

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