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Title: Stochastic modelling of defective ribosomal products generation
Author: Bonnin, Pierre J.
ISNI:       0000 0004 9353 676X
Awarding Body: University of Aberdeen
Current Institution: University of Aberdeen
Date of Award: 2017
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Higher eukaryotic cells have an intracellular surveillance mechanism that signals the immune system when infected. This corresponds to the processing and presentation of a short polypeptide (i.e. epitope, 9 amino acids long) on the Major Histocompatibility Complex (MHC) class I. However, it is yet unknown which mechanism the cell achieves to rapidly degrade the intracellular protein and generate an epitope. The epitope candidates are believed to arise from Defective Ribosomal Products (DRiPs). In this thesis we attempt to predict the generation of DRiPs based on the roles attributed to the Ribosome Associated Complex (RAC) and the Nascent polypeptide Associated Complex (NAC) during translation. RAC and NAC are thought to have a protective role during protein synthesis (preventing degradation and misfolding of the growing polypeptide). We have developed a mathematical model that predicts the generation of DRiPs. They are aberrant proteins that undergo fast degradation and believed to be candidates for epitope selection process. The model describes the translation of mRNAs by both ribosome bound to RAC and NAC and ribosomes unbound to RAC and NAC. Ribosomes that are not bound to RAC and NAC can drop off the mRNA prematurely, i.e., before reaching the stop codon, thereby generating DRiPs. Our stochastic model is based on the Totally Asymmetric Exclusion Process (TASEP) but it considers two different populations of particles, representing the two different kinds of ribosomes, and it allows the particles representing ribosomes unbound to RAC and NAC to drop off before reaching the stop codon. We find that the model is best described by dividing it into 2 simpler but novel models, a TASEP model with premature termination on one side and another with 2 ribosome populations on the other. We have developed analytical solutions and numerical validations for each of the elementary models and provide a comprehensive understanding of the combined model predicting the generation of DRiPs through numerical stochastic calculations. We not only find that the signatures found in the elementary models are recovered in the former one but we also find novel system behaviour due to the combination of both elementary models.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Ribosomes ; Stochastic models