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Issue 8, 2021
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Understanding nano-engineered particle–cell interactions: biological insights from mathematical models

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Abstract

Understanding the interactions between nano-engineered particles and cells is necessary for the rational design of particles for therapeutic, diagnostic and imaging purposes. In particular, the informed design of particles relies on the quantification of the relationship between the physicochemical properties of the particles and the rate at which cells interact with, and subsequently internalise, particles. Quantitative models, both mathematical and computational, provide a powerful tool for elucidating this relationship, as well as for understanding the mechanisms governing the intertwined processes of interaction and internalisation. Here we review the different types of mathematical and computational models that have been used to examine particle–cell interactions and particle internalisation. We detail the mathematical methodology for each type of model, the benefits and limitations associated with the different types of models, and highlight the advances in understanding gleaned from the application of these models to experimental observations of particle internalisation. We discuss the recent proposal and ongoing community adoption of standardised experimental reporting, and how this adoption is an important step toward unlocking the full potential of modelling approaches. Finally, we consider future directions in quantitative models of particle–cell interactions and highlight the need for hybrid experimental and theoretical investigations to address hitherto unanswered questions.

Graphical abstract: Understanding nano-engineered particle–cell interactions: biological insights from mathematical models

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Article information


Submitted
16 Sep 2020
Accepted
08 Mar 2021
First published
09 Mar 2021

This article is Open Access

Nanoscale Adv., 2021,3, 2139-2156
Article type
Review Article

Understanding nano-engineered particle–cell interactions: biological insights from mathematical models

S. T. Johnston, M. Faria and E. J. Crampin, Nanoscale Adv., 2021, 3, 2139
DOI: 10.1039/D0NA00774A

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