dc.rights.license | CC-BY-NC-ND | |
dc.contributor.advisor | Borghans, Jose | |
dc.contributor.author | Nieuwendijk, Aldo van den | |
dc.date.accessioned | 2025-05-31T23:01:48Z | |
dc.date.available | 2025-05-31T23:01:48Z | |
dc.date.issued | 2025 | |
dc.identifier.uri | https://studenttheses.uu.nl/handle/20.500.12932/49002 | |
dc.description.abstract | The ability of the immune system to neutralise pathogens and infection is well known, but equally
as important is its ability to maintain its own homeostasis via regulation of the T-cell response.
Improper regulation of the immune response can develop into autoimmunity and tumour
development. Despite being aware of the importance of regulation, the mechanisms at play are
not yet fully understood, in part due to their complexity. To increase the understanding of the
complex mechanism, mathematical models have been used to study the different interactions
that could be of importance, so that they could be better understood. In this literature review
several models from the past few decades of research will be examined and compared. These
models vary in the mechanisms they explore, such as inhibiting activation at the APC, spatial-
dependent cytokine signalling, and the creation of a local regulatory microenvironment. By
examining the approaches of these different models and their findings, this review aims to give
an overview of the research that has been done and propose potential future research to further
the understanding of T-cell regulation. | |
dc.description.sponsorship | Utrecht University | |
dc.language.iso | EN | |
dc.subject | Discussing the advances made in the modelling of T-cell regulation and behaviour and how it allows for an increased understanding of the immune response. | |
dc.title | Modelling different mechanisms of T -cell
regulation | |
dc.type.content | Master Thesis | |
dc.rights.accessrights | Open Access | |
dc.subject.keywords | T-cell regulation; immunology; bioinformatics; modelling; | |
dc.subject.courseuu | Bioinformatics and Biocomplexity | |
dc.thesis.id | 44703 | |