Investigation of the role of long non-coding RNAs in oncogene induced cellular senescence

Cellular senescence is a stable arrest of cell proliferation induced by several factors such as activated oncogenes, oxidative stress and shortening of telomeres. Senescence acts as a tumour suppression mechanism to halt the progression of cancer. However, senescence may also impact negatively upon tissue regeneration, thus contributing to the effects of ageing. The eukaryotic genome is controlled by various modes of transcriptional and translational regulation. Focus has therefore centred on the role of long non- coding RNAs (lncRNAs) in regulating the genome. Accordingly, understanding how lncRNAs function to regulate the senescent genome is integral to improving our knowledge and understanding of tumour suppression and ageing. Within this study, I set out to investigate the expression of lncRNAs’ expression within models of senescence. Through a custom expression array, I have shown that expression of multiple different lncRNAs is up-regulated and down regulated in IMR90 replicative senescent fibroblasts and oncogene-induced senescent melanocytes. LncRNA expression was determined to be specific to stable senescence-associated cell arrest and predominantly within the nucleus of senescent cells. In order to examine the function of lncRNA expression in senescence, I selected lncRNA transcript ENST0000430998 (lncRNA_98) to focus my investigations upon. LncRNA_98 was robustly upregulated within multiple models of senescence and efficiently depleted using anti-sense oligonucleotide technology. Characterisation and unbiased RNA-sequencing of lncRNA_98 deficient senescent cells highlighted a list of genes that are regulated by lncRNA_98 expression in senescent cells and may regulate aspects of the senescence program. Specifically, the formation of SAHF was impeded upon depletion of lncRNA_98 expression and levels of total pRB protein expression severely decreased. Validation and recapitulation of consequences of pRB depletion was confirmed through lncRNA_98 knock-out cells generated using CRISPR technology. Surprisingly, inhibition of ATM kinase functions permitted the restoration of pRB protein levels within lncRNA_98 deficient cells. I propose that lncRNA_98 antagonizes the ability of ATM kinase to downregulate pRB expression at a post-transcriptional level, thereby potentiating senescence. Furthermore, lncRNA expression was detected within fibroblasts of old individuals and visualised within senescent melanocytes in human benign nevi, a barrier to melanoma progression. Conversely, mining of 337 TCGA primary melanoma data sets highlighted that the lncRNA_98 gene and its expression was lost from a significant proportion of melanoma samples, consistent with lncRNA_98 having a tumour suppressor functions. The data presented in this study illustrates that lncRNA_98 expression has a regulatory role over pRB expression in senescence and may regulate aspects of tumourigenesis and ageing.

Trajectory prediction uncertainty modelling for Air Traffic Management

The anticipated growth of air traffic worldwide requires enhanced Air Traffic Management (ATM) technologies and procedures to increase the system capacity, efficiency, and resilience, while reducing environmental impact and maintaining operational safety. To deal with these challenges, new automation and information exchange capabilities are being developed through different modernisation initiatives toward a new global operational concept called Trajectory Based Operations (TBO), in which aircraft trajectory information becomes the cornerstone of advanced ATM applications. This transformation will lead to higher levels of system complexity requiring enhanced Decision Support Tools (DST) to aid humans in the decision making processes. These will rely on accurate predicted aircraft trajectories, provided by advanced Trajectory Predictors (TP). The trajectory prediction process is subject to stochastic effects that introduce uncertainty into the predictions. Regardless of the assumptions that define the aircraft motion model underpinning the TP, deviations between predicted and actual trajectories are unavoidable. This thesis proposes an innovative method to characterise the uncertainty associated with a trajectory prediction based on the mathematical theory of Polynomial Chaos Expansions (PCE). Assuming univariate PCEs of the trajectory prediction inputs, the method describes how to generate multivariate PCEs of the prediction outputs that quantify their associated uncertainty. Arbitrary PCE (aPCE) was chosen because it allows a higher degree of flexibility to model input uncertainty. The obtained polynomial description can be used in subsequent prediction sensitivity analyses thanks to the relationship between polynomial coefficients and Sobol indices. The Sobol indices enable ranking the input parameters according to their influence on trajectory prediction uncertainty. The applicability of the aPCE-based uncertainty quantification detailed herein is analysed through a study case. This study case represents a typical aircraft trajectory prediction problem in ATM, in which uncertain parameters regarding aircraft performance, aircraft intent description, weather forecast, and initial conditions are considered simultaneously. Numerical results are compared to those obtained from a Monte Carlo simulation, demonstrating the advantages of the proposed method. The thesis includes two examples of DSTs (Demand and Capacity Balancing tool, and Arrival Manager) to illustrate the potential benefits of exploiting the proposed uncertainty quantification method.