The advent of advanced high-throughput biological technologies provides opportunities to measure the whole genome at different molecular levels in biological systems, which produces different types of omic data such as genome, epigenome, transcriptome, translatome, proteome, metabolome and interactome. Biological systems are highly dynamic and complex mechanisms which involve not only the within-level functionality but also the between-level regulation. In order to uncover the complexity of biological systems, it is desirable to integrate multi-omic data to transform the multiple level data into biological knowledge about the underlying mechanisms. Due to the heterogeneity and high-dimension of multi-omic data, it is necessary to develop effective and efficient methods for multi-omic data integration. This thesis aims to develop efficient approaches for multi-omic data integration using machine learning methods and network theory. We assume that a biological system can be represented by a network with nodes denoting molecules and edges indicating functional links between molecules, in which multi-omic data can be integrated as attributes of nodes and edges. We propose four network-based approaches for multi-omic data integration using machine learning methods. Firstly, we propose an approach for gene module detection by integrating multi-condition transcriptome data and interactome data using network overlapping module detection method. We apply the approach to study the transcriptome data of human pre-implantation embryos across multiple development stages, and identify several stage-specific dynamic functional modules and genes which provide interesting biological insights. We evaluate the reproducibility of the modules by comparing with some other widely used methods and show that the intra-module genes are significantly overlapped between the different methods. Secondly, we propose an approach for gene module detection by integrating transcriptome, translatome, and interactome data using multilayer network. We apply the approach to study the ribosome profiling data of mTOR perturbed human prostate cancer cells and mine several translation efficiency regulated modules associated with mTOR perturbation. We develop an R package, TERM, for implementation of the proposed approach which offers a useful tool for the research field. Next, we propose an approach for feature selection by integrating transcriptome and interactome data using network-constrained regression. We develop a more efficient network-constrained regression method eGBL. We evaluate its performance in term of variable selection and prediction, and show that eGBL outperforms the other related regression methods. With application on the transcriptome data of human blastocysts, we select several interested genes associated with time-lapse parameters. Finally, we propose an approach for classification by integrating epigenome and transcriptome data using neural networks. We introduce a superlayer neural network (SNN) model which learns DNA methylation and gene expression data parallelly in superlayers but with cross-connections allowing crosstalks between them. We evaluate its performance on human breast cancer classification. The SNN provides superior performances and outperforms several other common machine learning methods. The approaches proposed in this thesis offer effective and efficient solutions for integration of heterogeneous high-dimensional datasets, which can be easily applied to other datasets presenting the similar structures. They are therefore applicable to many fields including but not limited to Bioinformatics and Computer Science.

In this doctoral thesis, I present my research into applying machine learning techniques for reconstructing species interaction networks in ecology, reconstructing molecular signalling pathways and gene regulatory networks in systems biology, and inferring parameters in ordinary differential equation (ODE) models of signalling pathways. Together, the methods I have developed for these applications demonstrate the usefulness of machine learning for reconstructing networks and inferring network parameters from data. The thesis consists of three parts. The first part is a detailed comparison of applying static Bayesian networks, relevance vector machines, and linear regression with L1 regularisation (LASSO) to the problem of reconstructing species interaction networks from species absence/presence data in ecology (Faisal et al., 2010). I describe how I generated data from a stochastic population model to test the different methods and how the simulation study led us to introduce spatial autocorrelation as an important covariate. I also show how we used the results of the simulation study to apply the methods to presence/absence data of bird species from the European Bird Atlas. The second part of the thesis describes a time-varying, non-homogeneous dynamic Bayesian network model for reconstructing signalling pathways and gene regulatory networks, based on L`ebre et al. (2010). I show how my work has extended this model to incorporate different types of hierarchical Bayesian information sharing priors and different coupling strategies among nodes in the network. The introduction of these priors reduces the inference uncertainty by putting a penalty on the number of structure changes among network segments separated by inferred changepoints (Dondelinger et al., 2010; Husmeier et al., 2010; Dondelinger et al., 2012b). Using both synthetic and real data, I demonstrate that using information sharing priors leads to a better reconstruction accuracy of the underlying gene regulatory networks, and I compare the different priors and coupling strategies. I show the results of applying the model to gene expression datasets from Drosophila melanogaster and Arabidopsis thaliana, as well as to a synthetic biology gene expression dataset from Saccharomyces cerevisiae. In each case, the underlying network is time-varying; for Drosophila melanogaster, as a consequence of measuring gene expression during different developmental stages; for Arabidopsis thaliana, as a consequence of measuring gene expression for circadian clock genes under different conditions; and for the synthetic biology dataset, as a consequence of changing the growth environment. I show that in addition to inferring sensible network structures, the model also successfully predicts the locations of changepoints. The third and final part of this thesis is concerned with parameter inference in ODE models of biological systems. This problem is of interest to systems biology researchers, as kinetic reaction parameters can often not be measured, or can only be estimated imprecisely from experimental data. Due to the cost of numerically solving the ODE system after each parameter adaptation, this is a computationally challenging problem. Gradient matching techniques circumvent this problem by directly fitting the derivatives of the ODE to the slope of an interpolant. I present an inference procedure for a model using nonparametric Bayesian statistics with Gaussian processes, based on Calderhead et al. (2008). I show that the new inference procedure improves on the original formulation in Calderhead et al. (2008) and I present the result of applying it to ODE models of predator-prey interactions, a circadian clock gene, a signal transduction pathway, and the JAK/STAT pathway.

No description available.

No description available.

Research on organisational learning (OL) was mainly positioned within the psychological and sociological domains. Past and extant research on OL focused on the behavioural, cognitive and intuitive perspectives in addition to a growing track of research grounded on social theory. So far, a countless number of research studies attempted to address inter-organisational learning (IOL) from various perspectives. However, the lack of understanding of how IOL occurs in networks can be observed due to the social tensions that are created at the inter-organisational level such as free-riding and knowledge leakage. This thesis, therefore, aims to draw theoretical explanations of IOL and how it occurs in learning networks, taking into consideration similarities and contradictions amongst a network’s participating organisations. Towards this end, the thesis employs two theoretical lenses, namely structure-agency and social exchange theories to draw conclusions that provide fresh explanations of how networks are helpful in fostering or hindering learning activities in addition to how reciprocity as an efficacy device mediates IOL dynamics. Positioned within a qualitative vein, the thesis employs an interpretive perspective to collect and analyse empirical evidence. The qualitative data were developed through a mixture of participant observations, semi-structured interviews and casual conversations with network administrators and participants. The data were analysed using thematic analysis which generated codes, following which conclusions were drawn. The main contributions of this article are (1) unfolding the network as agency which provides a fresh understanding of how the agential role of networks mediates IOL and (2) drawing a framework of dimensions of reciprocal exchanges that explains how IOL occurs in networks. The first conclusion of this thesis explained how the agential role is socially constructed and how the interpretive device facilitated this construction. The second conclusion of this thesis explained how reciprocal exchanges mediate IOL and provide a framework that suggested IOL can be better understood through temporal, spatial, directional and symmetrical perspectives.

The extent to which the knowledge mobilisation potential of public health networks is actually achieved in their functioning has not been previously studied. There are prescriptions from policy documents and from research literature as to the form networks in health should take and the way they should operate. However, there has been little research connecting the nature of the networks and the manner in which they function to their knowledge mobilising ability. Constituted in 2006, the Scottish Public Health Network (ScotPHN), which is the primary vehicle in Scotland for mobilising public health knowledge and informing policy and practice, constitutes the location for this study investigating this knowledge mobilisation and how networks function in public health. Feedback from the consultation conducted prior to the formation of ScotPHN was obtained. Interviews were conducted with the members of the ScotPHN steering group, a project group and the stakeholder group. Two ScotPHN steering group meetings were also attended by the author as an observer. The consultation feedback, transcripts of the interviews and those of steering group meetings were analysed using the constructivist version of the grounded theory approach. The process involved coding and abstracting codes to categories and themes. The emerging themes were reviewed in the light of existing literature on networks and knowledge mobilisation. These themes were then used to develop a model to understand how the network operates and consequently mobilises knowledge. The study shows that prior to its formation ScotPHN was expected to address the fragmentation of the public health workforce; significantly enhance links amongst existing public health networks; support ground level knowledge exchange amongst practitioners and significantly enhance multisectorial working. None of these expectations appear to have been met. ScotPHN has, however, managed to fill the gap left by the demise of the Scottish Needs Assessment Programme (SNAP). ScotPHN’s structure and the manner in which it is controlled lead to it being akin to a policy community rather than an issue network. The generic public health concerns of the steering group and the selective nature of the project group prevent it from functioning as an issue network. The dominance of people from the medical profession also causes a social closedness in the ScotPHN steering group. The limited multisectorial participation in its activities results in: a lack of constructionist learning; limited inclusion of the social context of knowledge; and a deficit of Mode 2 knowledge mobilisation. In the context of knowledge conversion there is some evidence of externalisation but no socialisation. ScotPHN is not a network that can be classed as a community of practice. This study highlights how health policies, which have advocated the establishment of networks, could derive considerable guidance from research into how networks actually function. With respect to the knowledge mobilisation activity of these networks the study finds that top-down and prescribed structures are unable to capture the transdisciplinarity and diverse intellectual frameworks that contribute to public health knowledge. It is seen that the hierarchical network structures can undermine the engagement of actors from the less represented sectors. Additionally the study finds that the established patterns of professional power and control further hinder multisectorial engagement.

Cloud-based computing in higher education has the potential to impact institutions on a myriad of fronts, including technology governance, flexibility, financial, and intellectual property. As the demand for blended and online education increases, institutions are considering expedient approaches to implementing learning management systems (LMSs). Cloud-based e-learning models, such as personal learning environments and open learning networks, are reported to be among the next generation of LMSs. Saint Anselm College launched a cloud enhanced LMS, HawkLearn, to support several blended courses. HawkLearn was flexible, low-cost, low-maintenance, and targeted to digital natives, accustomed to using web 2.0 based tools and social media. Reporting utilized a case study approach, tracking HawkLearn's evolution from concept to reality. Results yielded data for higher education institutions, evaluating LMS strategies.

No description available.

The fifth-generation (5G) of mobile communication networks are expected to support a large number of vertical industries requiring services with diverging requirements. To accommodate this, mobile networks are undergoing a significant transformation to enable a variety of services to coexist on the same infrastructure through network slicing. Additionally, the introduction of distributed user-plane and multi-access edge computing (MEC) technology allows the deployment of virtualised applications close to the network edge. The first part of this dissertation focuses on end-to-end network slice provisioning for various vertical industries with different service requirements. Two slice provisioning strategies are explored, by formulating a mixed integer linear programming (MILP) problem. Further, a genetic algorithm (GA)-based approach is proposed with the aim to improve search-space exploration. Simulation results show that the proposed approach is effective in providing near-optimal solutions while drastically reducing computational complexity. In a later stage, the study focuses on building a measurement-based digital twin (DT) for the highly heterogeneous MEC ecosystem. The DT operates as an intermediate and collaborative layer, enabling the orchestration layer to better understand network behavior before making changes to the physical network. Assisted by proper AI/ML solutions, the DT is envisioned to play a crucial role in automated network management. The study utilizes an emulated and physical test-bed to gather network key performance indicators (KPIs) and demonstrates the potential of graph neural network (GNN) in enabling closed loop automation with the help of DT. These findings offer a foundation for future research in the area of DT models and carbon footprint-aware orchestration.

This thesis examines the collaborative development of corporate responsibility (CR) standards from the perspective of organisational learning theory. The author proposes that standards development projects can be understood as Network Learning episodes where learning is reflected in changes in structures, interpretations and practices accompanied by learning processes. Network Learning alone is seen as insufficient to reflect the diverse contributions and outcomes in the special case of CR standards. Concepts from multi-stakeholder learning like the role of dissensus in learning and the empowerment of weaker stakeholders are therefore used to create a synthesis of the two theories in a single conceptual framework. This framework is then tested against a pilot case and three case studies of corporate social responsibility (CSR) standards including the development of the new ISO international standard on social responsibility (SR). The data validates and extended this framework to yield a Multi-Stakeholder Network Learning theory capable of describing the how participants and non-participant stakeholders learn in this context. New concepts are generated from the data, like dislocated learning, which demonstrate how participants in the process and those they represent can experience quite different learning outcomes. Stakeholders whose learning is aligned with the learning of their participant representatives truly have a stake in these influential standards. However, where representatives fail to learn from those represented, the latter's stake is diminished. By shedding light on the mechanisms of effective collaborative learning this work contributes to learning theory, the practice of standardization and the normative stakeholder empowerment agenda.