Cancer, nano-particles tracking, 3D printing, machine learning and Zebrafish


Cancer is characterised by profound changes in the surrounding vasculature because the cancerous tissue is more energy demanding than normal tissue. As such the vasculature is multiplied in a chaotic, leaky network in the surroundings of a tumor, the so-called enhanced permeability and retention (EPR) effect. In detail, the vessels are formed by poorly aligned endothelial cells with wide fenestrations (100 -500 nm compared to 10 nm in healthy tissue and in lack of smooth muscle cells. To take advantage of this poorly structured leaky vessels that surround the tumor for drug delivery, anti cancer nanoparticles (NP) (between 10 and 500-100nm) have been proposed as carriers of drugs. Theoretically, the particles of diameter more than 10 nm would only escape the blood stream through the enlarged fenestrations of the tumor tissue, leaving healthy tissue. However, typical deliverance of administered dose of NP to tumors typically is less than 5%. To gain insight in the microcirculation of cancerous tissue, our partners at IBV have developed a zebrafish model of cancer in which the detailed flow can be studied with great resolution, see Fig 2. The subject of this project will be to develop algorithms to track the nano-particles. We begin in 3D printed geometries of vessels before we test the tracking in data obtained from zebrafish. Machine learning as well as more traditional techniques will be tested.

Learning outcome

Methods for particle tracking, machine learning, lab work, simulation


A background in mechanics and experimental work.


Kent-André Mardal

Gareth Griffiths

Federico Fenaroli

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