http://theses.ncl.ac.uk/jspui/handle/10443/5223 2026-05-08T16:37:17Z http://theses.ncl.ac.uk/jspui/handle/10443/6762 Title: Advancing scientific knowledge representation : standardisation and integration in tolerogenic therapies Authors: Sahar, Ayesha Abstract: In this thesis, we use data integration and analysis methods and examine the impact of data standardisation to enhance our understanding of tolerogenic dendritic cell (tolDC) therapies. Standardisation and structuring of the data are extremely valuable for it to be useful and accessible. Emerging biological fields face unique difficulties, including limited data availability, a lack of standardisation and challenges in knowledge management from different studies due to varied methodologies. These issues demand the development and application of specialised techniques and strategies tailored to their specific data handling and management needs. This thesis focuses on one such emerging field, “tolerogenic Dendritic Cell Therapy”, which has demonstrated significant potential. Like all biomedical experiments, developing these therapies involves several crucial steps that must be well-documented for comparison and replication purposes. Reporting frameworks, like Minimum Information Models can aid in standardising these descriptions; Minimum Information about Tolerogenic AntigenPresenting cells (MITAP) was created in 2016 in this field for this purpose. We evaluate MITAP’s impact on the field of tolDC therapies by analysing a selection of literature. We found that MITAP is utilised in a minority of relevant papers (14%), but where it is applied, there is slightly more metadata available. This suggests that while MITAP has had some success, further efforts are needed for standardised reporting to become widespread in the discipline. In order to further aid the comparison, re-purposing and re-use of data about tolDC therapies, we built a method to identify and integrate the most significant information related to tolerogenic dendritic cell therapies into a knowledge graph structure. A key aspect of the knowledge graph is ensuring that the merged data is relevant to the field. We employ knowledge extraction techniques to identify and collect relevant information from research articles, integrating this with publicly available datasets to enrich the knowledge base. We successfully embedded this data into a comprehensive knowledge graph comprising 120k entities extracted from full-text articles and additional integration of 92k relationships from other relevant databases. The use of knowledge extraction techniques from research articles ensured the relevance of the integrated data to the field. It also allowed us to gain more insights from publications with unpublished experimental data, as shown in the example queries. This knowledge graph can act as a base for the generation of further hypotheses as well as a database for the storage and retrieval of relevant information about tolDC therapies. Having built the knowledge graph our focus shifts to considering queries about the tolDC therapies that give us a better understanding of the degree of standardisation, about the underlying biology and the social environment in the field. We formulated diverse queries encompassing heterogeneity concerns. The results demonstrated the effectiveness of tolKG in promptly addressing these queries, a task that would either necessitate specialised expertise or significant manual scrutiny if pursued conventionally. Through the utilisation of tolKG, we streamline tasks such as comparison and analysis and even facilitate the generation of novel hypotheses. In summary, we found that a knowledge graph is an effective way to integrate data. Moreover, the addition of data from the literature makes it more meaningful, especially for emerging fields where there is a lack of experimental data sharing. Text mining from literature enables the extraction of more relationships that are specific to a field. As a result, it can help to perform an effective analysis and comparison of the tolDC therapy field. Together, this work helps establish the groundwork for applying data science methods in tolDC therapies making several kinds of comparisons possible which are not possible without it. The methodologies employed are specifically tailored to the data sources of tolDC therapies. Nonetheless, these strategies are not restricted to this particular domain; they primarily depend on the input data sources, which makes them usable in other areas of biology as well. Description: PhD Thesis 2025-01-01T00:00:00Z http://theses.ncl.ac.uk/jspui/handle/10443/6731 Title: Automated design, build, test, learn workflows to engineer synthetic genetic networks Authors: Vidal Peña, Gonzalo Andrés Abstract: Synthetic biology is an interdisciplinary field that pursues the engineering of biological systems. The design, build, test, learn (DBTL) cycle is at the core of engineering disciplines and is iterated until a desired goal is achieved. Synthetic biology is still defining abstractions, standards and developing a software ecosystem to iterate the DBTL cycle. The aim is to work in a similar way as other engineering disciplines, making designs with a computational aided design (CAD) tool that can simulate the expected behaviour of the designed biological system, and that can communicate to build tools to create a physical implementation of the biological system. After the biological system is built, it is tested by taking measurements of its behaviour. The test has to be automated, calibrated and standardised to get high quantity and quality data that can inform the learn stage properly. Given the diversity of synthetic biology and its applications the DBTL cycle could have different needs when the researcher needs to engineer a genetic network, a metabolic pathways, a strain or a protein, among others. The focus of this work is in creating DBTL cycle workflows for engineering synthetic genetic network dynamics, because it allows to control the logic of a system and how that logic state is reached and maintained over time with direct applications in biochemical production, drug dosage, and the study of pattern formation and developmental biology. Existing tools for engineering genetic network dynamics do not cover the whole DBTL cycle and lack connections, leaving several gaps. Most tools do not use standardised inputs and outputs hindering the connectivity between tools and slowing the research process. To iterate faster through the DBTL cycle it has to be closed and automated by leveraging software tools and liquid handling robots. Software tools have to be compatible with standards to make them useful and accessible for the community, promoting the use of best practices. The workflow has to be flexible to accommodate different needs and resources, to be used for researchers without a wetlab, with non-automated wetlab and with lab automation. Here I have created a set of software tools tackling different DBTL cycle stages that are modular and leverage standards to connect and automate the DBTL cycle for genetic network engineering. The workflows developed in this work provides novel teaching and research tools available for different needs. Description: Ph. D. Thesis. 2025-01-01T00:00:00Z http://theses.ncl.ac.uk/jspui/handle/10443/6729 Title: Computational Approaches to Drug Repurposing Through Probabilistic Functional Integration of Disease-Gene networks and Graph Neural Networks Authors: Alsobhe, Aoesha Abstract: Drug discovery is a time-consuming, costly, high-risk, and complex process. An alternative to traditional drug development is drug repurposing, which aims to find new uses for existing drugs. This approach significantly reduces time and cost, as much of the safety evaluation has already been completed. Computational approaches to drug repurposing help generate hypotheses about potential drug-disease indications, which can later be validated experimentally in the lab. Network integration is a common computational technique in drug repurposing applications. These approaches combine multiple diverse data sources into a single heterogeneous biomedical integrated network. Such networks combine various types of biological data, including drugs, diseases, genes, and proteins, into a unified framework where biomedical entities are represented as nodes and their interactions as edges. Integrating diverse data sources is essential to gain a comprehensive picture of interconnected biological entities, which can then be mined to infer new hypotheses about drug repurposing opportunities. The quality of these integrated networks is highly dependent on the experimental data they include. However, biomedical data is often noisy and incomplete, leading to a high rate of false results in existing networks. Therefore, there is an important need for methods to reduce noise during network integration. One proposed technique to produce accurate integrated networks is Probabilistic Functional Integrated Networks (PFINs), which assess data quality and generate confidence scores to filter out low-quality data before mining these networks for drug repurposing opportunities. Disease-Gene Association (DGA) networks, where nodes represent diseases and genes and edges represent their associations, are the major building blocks for most biomedical integrated networks used in drug repurposing applications. Unfortunately, many available DGA networks contain a high rate of false results due to the quality of the biomedical data, which faces numerous challenges, including incorrect entries, missing values, inconsistencies, duplication, and various forms of bias. For instance, high-throughput experimental studies, which are commonly used to generate biological data, often produce incomplete and noisy data containing both false positives and false negatives. Although methods exist to score the confidence of DGAs, they are often unreliable. Many of these scoring approaches rely on heuristic strategies that do not assess data quality prior to integration. For example, they often overlook the impact of duplicated data, which can artificially inflate confidence scores and distort the strength of associations. To address this gap, we investigated the applicability of PFINs to DGA networks by researching and developing novel strategies to build and evaluate DGA PFINs. These accurate integrated DGA networks can be employed in various computational drug repurposing applications, including deep learning techniques. Deep learning has become the leading technique in most in silico applications for drug repurposing. Among deep learning methods, Graph Neural Networks (GNNs) have gained considerable attention due to their ability to learn complex relationships between drugs and related biological entities from heterogeneous biomedical integrated networks. Existing GNN applications in drug repurposing often overlook important aspects of data quality, such as noise and incompleteness. Given that the performance of GNNs is highly dependent on the quality of the integrated networks used for training, incorporating PFINs with GNNs could enhance their performance by reducing noise during network integration. To address these issues, we investigated the impact of incorporating the PFINs approach within GNNs on their performance. The constructed DGA PFIN was integrated with an existing network and used to train GNN models. Another factor impacting the performance of GNNs, beyond data quality, is the lack of diverse data types in the integrated networks. Most existing GNN approaches are trained on networks with a limited number of node and edge types, often ignoring node features in the training process. We explored the impact of adding various types of nodes and edges to the integrated networks on GNN performance, as well as incorporating node features in the training process. The results showed that the performance of GNN models improved by incorporating these additional types of nodes and edges into the training networks. Furthermore, the proposed GNN models demonstrated significant enhancement by incorporating node features. Finally, the proposed GNN models were employed to predict drug-disease indications, and these predictions were validated and supported by the literature. Description: PhD Thesis 2025-01-01T00:00:00Z http://theses.ncl.ac.uk/jspui/handle/10443/6728 Title: IoT-Enhanced Vehicular Networks: Simulation Frameworks for Energy Efficiency and Cyber-Security in Smart Cities Authors: Almutairi, Reham Mutlaq Abstract: The Internet of Things (IoT) has rapidly evolved over the past two decades, transforming the way we interact with the environment through a network of interconnected devices. The purpose of this thesis is to explore the integration of IoT with Vehicular Ad-Hoc Net works (VANETs) in order to enhance intelligent transportation systems (ITS) and smart city infrastructure through the use of IoT. VANETs, characterized by high mobility and dynamic topology, play a crucial role in enhancing traffic safety, efficiency, and vehicu lar services. They improve traffic safety by enabling real-time communication between vehicles and roadside infrastructure, allowing the sharing of critical information such as accident warnings and road conditions to prevent collisions and enhance emergency re sponse times. VANETs boost traffic efficiency through intelligent traffic management, optimizing signal timings and route planning based on real-time data to reduce con gestion and travel times. Additionally, they provide enhanced vehicular services such as infotainment, navigation assistance, and maintenance alerts, thereby improving the overall driving experience and vehicle performance monitoring. This research addresses the significant challenges of simulating VANET environments, particularly the high mobility of vehicles and the need for realistic traffic scenarios. Ex isting VANET simulators, while advanced, often lack support for new technologies and comprehensive security systems, highlighting the necessity for more comprehensive sim ulation frameworks. The primary aim of this PhD thesis is to integrate IoT and traffic simulations to accurately evaluate vehicular energy efficiency and overall network perfor mance. Therefore, this thesis presents multilateral research towards optimization, mod eling, and simulation of VANET and IoT environments. Several tools and algorithms have been proposed, implemented, and evaluated, considering various environments and applications. The main contributions of this thesis are as follows: • Conducting a review of current IoT simulators highlights their strengths and lim itations, particularly their inability to address energy depletion security concerns. The survey identified a lack of support for renewable energy sources or VANET integration, which are essential for modern IoT applications. The absence of a ver-- i satile, generic IoT simulator is noted, as existing tools often specialize in specific applications and lack flexibility. • Conducting an in-depth performance evaluation of emerging VANET technologies, this survey addresses the urgent need for updated reviews considering electric vehi cles, self-driving cars, SDN, edge computing, and 5G. The survey identifies critical gaps, including the lack of support for renewable energy, dynamic battery recharg ing, and encryption impact. • Conducting a feasibility study on coupling IoT simulators with traffic simulators to enhance VANET simulations, this toward study introduces the novel SUMO toOsmosis framework. Investigating the integration of IoTSim-Osmosis for IoT simulations with SUMO for traffic simulations, SUMOtoOsmosis marks a first in the literature. The proposed system, tested with the Hamburg dataset, focuses on communication time, this framework enables the simulation of traffic environments based on IoT infrastructure. • Proposing and modeling a new holistic framework that simulates real-world traffic scenarios for electric vehicles, SimulatorBridger. Its flexible architecture allows for integration with any traffic simulator. Preliminary results validate its accuracy in simulating vehicular battery consumption and network performance, highlighting the need for efficient communication policies. This platform supports policymakers in optimizing VANET performance and developing energy-efficient transportation networks. • Introducing SimulatorBridgerDfT, a novel simulator platform extending Simulator Bridger to support different formats of real traffic data, enhances the flexibility and applicability of urban traffic simulations by integrating IoTSim-OsmosisRES with DfT traffic data. Evaluating the impact of SUMO car traces versus static DfT data on communication delays in IoT simulations provides valuable insights for designing efficient and effective traffic simulation tools, aiding researchers and practitioners in traffic management and urban planning. • Introducing IoTSimSecure, a novel simulation framework designed to detect the security attacks, particularly battery draining attacks. IoTSimSecure supports a- ii range of detection algorithms, including threshold-based detection and Exponential Weighted Moving Average (EWMA) techniques. This flexibility allows for com prehensive analysis and testing of various security strategies, thus enhancing the simulator’s ability to develop effective countermeasures against battery-draining attacks. As a result of addressing the key challenges in IoT and VANET simulation, the results of this thesis will contribute to the development of flexible, efficient, and secure intelligent transportation systems and smart city infrastructures. Description: PhD Thesis 2025-01-01T00:00:00Z