Genomic Methods for Studying the Post-Translational Regulation of Transcription Factors

The spatiotemporal coordination of gene expression is a fundamental process in cellular biology. Gene expression is regulated, in large part, by sequence-specific transcription factors that bind to DNA regions in the proximity of each target gene. Transcription factor activity and specificity are, in turn, regulated post-translationally by protein-modifying enzymes. High-throughput methods exist to probe specific steps of this process, such as protein-protein and protein-DNA interactions, but few computational tools exist to integrate this information in a principled, model-oriented manner. In this work, I develop several computational tools for studying the functional implications of transcription factor modification. I establish the first publicly accessible database for known and predicted regulatory circuits that encompass modifying enzymes, transcription factors, and transcriptional targets. I also develop a model-based method for integrating heterogeneous genomic and proteomic data for the inference of modification-dependent transcriptional regulatory networks. The model-based method is thoroughly validated as a reliable and accurate computational genomic tool. Additionally, I propose and demonstrate fundamental improvements to computational proteomic methods for identifying modified protein forms. In summary, this work contributes critical methodological advances to the field of regulatory network inference

Video surveillance using deep transfer learning and deep domain adaptation: Towards better generalization

Recently, developing automated video surveillance systems (VSSs) has become crucial to ensure the security and safety of the population, especially during events involving large crowds, such as sporting events. While artificial intelligence (AI) smooths the path of computers to think like humans, machine learning (ML) and deep learning (DL) pave the way more, even by adding training and learning components. DL algorithms require data labeling and high-performance computers to effectively analyze and understand surveillance data recorded from fixed or mobile cameras installed in indoor or outdoor environments. However, they might not perform as expected, take much time in training, or not have enough input data to generalize well. To that end, deep transfer learning (DTL) and deep domain adaptation (DDA) have recently been proposed as promising solutions to alleviate these issues. Typically, they can (i) ease the training process, (ii) improve the generalizability of ML and DL models, and (iii) overcome data scarcity problems by transferring knowledge from one domain to another or from one task to another. Although the increasing number of articles proposed to develop DTL- and DDA-based VSSs, a thorough review that summarizes and criticizes the state-of-the-art is still missing. To that end, this paper introduces, to the best of the authors' knowledge, the first overview of existing DTL- and DDA-based video surveillance to (i) shed light on their benefits, (ii) discuss their challenges, and (iii) highlight their future perspectives.This research work was made possible by research grant support (QUEX-CENG-SCDL-19/20-1) from Supreme Committee for Delivery and Legacy (SC) in Qatar. The statements made herein are solely the responsibility of the authors. Open Access funding provided by the Qatar National Library.Scopu

Visualization and analysis strategies for dynamic gene-phenotype relationships and their biological interpretation

The complexity of biological systems is one of their most fascinating and, at the same time, most cryptic aspects. Despite the progress of technology that has enabled measuring biological parameters at deeper levels of detail in time and space, the ability to decipher meaning from these large amounts of heterogeneous data is limited. In order to address this challenge, both analysis and visualization strategies need to be adapted to handle this complexity. At system-wide level, we are still limited in our ability to infer genetic and environmental causes of disease, or consistently compare and link phenotypes. Moreover, despite the increasing availability of time-resolved experiments, the temporal context is often lost. In my thesis, I explored a series of analysis and visualization strategies to compare and connect dynamic phenotypic outcomes of cellular perturbations in a genetic and network context. More specifically, in the first part of my thesis, I focused on the cell cycle as one of the best examples of a complex, highly dynamic process. I applied analysis and data integration methods to investigate phenotypes derived from cell division failure. I examined how such phenotypes may arise as a result of perturbations in the underlying network. To this purpose, I investigated the role of short structural elements at binding interfaces of proteins, called linear motifs, in shaping the cell division network. I assessed their association to different phenotypes, in the context of local perturbations and of disease. This analysis enabled a more detailed understanding of the regulatory mechanisms beyond the malfunctioning of cell division processes, but the ability to compare phenotypes and track their evolution was limited. Exploring large-scale, time-resolved phenotypic screens is still a bottleneck, especially in the visualization area. To help address this question, in the subsequent parts of the thesis I proposed novel visualization approaches that would leverage pattern discovery in such heterogeneous, dynamic datasets and enable the generation of new hypotheses. First, I extended an existing visualization tool, Arena3D, to enable the comparison of phenotypes in a genetic and network context. I used this tool to continue the exploration of phenotype-wide differences between outcomes of gene function suppression within mitosis. I also applied it to an investigation of systemic changes in the network of embryonic stem cell fate determinants upon downregulation of the pluripotency factor Nanog. Second, time-resolved tracking of phenotypes opens up new possibilities in exploring how genetic and phenotypic connections evolve through time, an aspect that is largely missing in the visualization area. I developed a novel visualization approach that uses 2D/3D projections to enable the discovery of genetic determinants linking phenotypes through time. I used the resulting tool, PhenoTimer, to investigate the patterns of transitions between phenotypes in cell populations upon perturbation of cell division and the timing of cancer-relevant transcriptional events. I showed the potential of discovering drug synergistic effects by visual mapping of similarities in their mechanisms of action. Overall, these approaches help clarify aspects of the consequences of cell division failure and provide general visualization frameworks that should be of interest to the wider scientific community, for use in the analysis of multidimensional phenotypic screens

Hot section components life usage analyses for industrial gas turbines

Industrial gas turbines generally operate at a bit stable power levels and the hot section critical components, especially high pressure turbine blades are prone to failure due to creep. In some cases, plants are frequently shut down, thus, in addition to creep low cycle fatigue failure equally sets in. Avoiding failure calls for proper monitoring of how the lives of these components are being consumed. Efforts are thus being made to estimate the life of the critical components of the gas turbine, but, the accuracy of the life prediction methods employed has been an issue. In view of the above observations, in this research, a platform has been developed to simultaneously examine engine life consumption due to creep, fatigue and creep-fatigue interaction exploiting relative life analysis where the engine life calculated is compared to a reference life in each failure mode. The results obtained are life analysis factors which indicate how well the engine is being operated. The Larson-Miller Parameter method is used for the creep life consumption analysis, the modified universal slopes method is applied in the low cycle fatigue life estimation while Taira's linear accumulation method is adopted for creep-fatigue interaction life calculation. Fatigue cycles counting model is developed to estimate the fatigue cycles accumulated in any period of engine operation. Blade thermal and stress models are developed together with a data acquisition and pre-processing module to make the life calculations possible. The developed models and the life analysis algorithms are implemented in PYTHIA, Cranfield University's in-house gas turbine performance and diagnostics software to ensure that reliable simulation results are obtained for life analysis. The developed life analysis techniques are applied to several months of real engine operation data, using LM2500+ engine operated by Manx Utilities at the Isle of Man to test the applicability and the feasibility of the methods. The developed algorithms provide quick evaluation and tracking of engine life. The lifing algorithms developed in this research could be applied to different engines. The relative influences of different factors affecting engine life consumption were investigated by considering each effect on engine life consumtion at different engine operation conditions and it was observed that shaft power level has significant impact on engine life consumption while compressor degradation has more impact on engine life consumption than high pressure turbine degradation. The lifing methodologies developed in this work will help engine operators in their engine conditions monitoring and condition-based maintenance

The Potato Crop

Life sciences; Agriculture; Nutrition   ; Plant breeding; Food—Biotechnology; Agricultural economic