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  5. Lab-on-a-chip and numerical tools for a better understanding and prediction of chemokine gradients
 
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Lab-on-a-chip and numerical tools for a better understanding and prediction of chemokine gradients
File(s)
Bonneuil-W-2022-PhD-Thesis.pdf (26.2 MB)
Thesis
Author(s)
Bonneuil, Willy V.
Type
Thesis or dissertation
Abstract
In spite of the multiple roles played by chemokines in acute and chronic inflammation, therapeutic attempts at targeting chemokine gradients have suffered from the difficulty of quantiying the gradients in vivo, from the paucity of suitable tools to reproduce gradient complexity in vitro, and from the consequences of limited quantification on gradients simulated by theoretical models. Fluid flow is a key contributor to the chemokine fields experienced by cells, yet it and its effects are incompletely characterised. I first demonstrated the inclusion of parallel diffusion and advection at finely adjustable and physiologic velocities in a microfluidic device suitable for cell culture. This combination of transport mechanisms, coupled with constant boundary chemokine concentrations, yielded stable gradients at Péclet numbers adjustable on-the-chip. I then designed an in situ small-world network growth algorithm and used it to explore the link between the small-world topology of lymphoid conduit networks and their function as preferential advective-diffusive transport pathways for small molecules. Small-worldness was shown to benefit transport efficiency, both by diffusion and advection, as well as transport homogeneity, by diffusion. Those conduit networks then formed part of an investigation of the fluid flow characteristics of peripheral arterial tissue, where chemokine gradients can trigger the formation of tertiary lymphoid organs in certain atherosclerotic contexts. That investigation highlighted the strong senstivity of peri-arterial pressure and fluid pathways on the local balance between blood and lymphatic microvasculature and pointed to the lack of fluid mixing in conduit networks being a key contributor to the efficiency of the networks in information delivery.
Version
Open Access
Date Issued
2022-01
Date Awarded
2022-08
URI
http://hdl.handle.net/10044/1/114055
DOI
https://doi.org/10.25560/114055
Copyright Statement
Creative Commons Attribution NonCommercial Licence
License URL
https://creativecommons.org/licenses/by-nc/4.0/
Advisor
Moore, James
Sponsor
British Heart Foundation
Grant Number
FS/17/41/32976
Publisher Department
Bioengineering
Publisher Institution
Imperial College London
Qualification Level
Doctoral
Qualification Name
Doctor of Philosophy (PhD)
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