Methods and applications in social networks analysis

The Social Network Analysis perspective has proven the ability to develop a significant breadth of theoretical and methodological issues witnessed by the contribution of an increasing number of scholars and the multiplication of empirical applications in a wide range of scientific fields. One of the disciplinary areas in which this development has occurred, among others, is certainly that of computational social science, by virtue of the developing field of online social networks and the leading role of information technologies in the production of scientific knowledge. The complex nature of social phenomena enforced the usefulness of the network perspective as a wealth of theoretical and methodological tools capable of penetrating within the dimensions of that complexity. The book hosts eleven contributions that within a sound theoretical ground, present different examples of speculative and applicative areas where the Social Network Analysis can contribute to explore, interpret and predict social interaction between actors. Some of the contributions were presented at the ARS’19 Conference held in Vietri sul Mare (Salerno, Italy) in October, 29-31 2019; it was the seventh of a biennial meetings series started in 2007 with the aim to promote relevant results and the most recent methodological developments in Social Network Analysis

Optimal Information Transmission in Organizations: Search and Congestion

We propose a stylized model of a problem-solving organization whose internal communication structure is given by a fixed network. Problems arrive randomly anywhere in this network and must find their way to their respective “specialized solvers” by relying on local information alone. The organization handles multiple problems simultaneously. For this reason, the process may be subject to congestion. We provide a characterization of the threshold of collapse of the network and of the stock of floating problems (or average delay) that prevails below that threshold. We build upon this characterization to address a design problem: the determination of what kind of network architecture optimizes performance for any given problem arrival rate. We conclude that, for low arrival rates, the optimal network is very polarized (i.e. star-like or “centralized”), whereas it is largely homogenous (or “decentralized”) for high arrival rates. We also show that, if an auxiliary assumption holds, the transition between these two opposite structures is sharp and they are the only ones to ever qualify as optimal. Keywords: Networks, information transmission, search, organization design.Networks, Information transmission, Search, Organization design

Methods and applications in social networks analysis

The Social Network Analysis perspective has proven the ability to develop a significant breadth of theoretical and methodological issues witnessed by the contribution of an increasing number of scholars and the multiplication of empirical applications in a wide range of scientific fields. One of the disciplinary areas in which this development has occurred, among others, is certainly that of computational social science, by virtue of the developing field of online social networks and the leading role of information technologies in the production of scientific knowledge. The complex nature of social phenomena enforced the usefulness of the network perspective as a wealth of theoretical and methodological tools capable of penetrating within the dimensions of that complexity. The book hosts eleven contributions that within a sound theoretical ground, present different examples of speculative and applicative areas where the Social Network Analysis can contribute to explore, interpret and predict social interaction between actors. Some of the contributions were presented at the ARS’19 Conference held in Vietri sul Mare (Salerno, Italy) in October, 29-31 2019; it was the seventh of a biennial meetings series started in 2007 with the aim to promote relevant results and the most recent methodological developments in Social Network Analysis

Meta-stochastic simulation for systems and synthetic biology using classification

PhD ThesisTo comprehend the immense complexity that drives biological systems, it is necessary to generate hypotheses of system behaviour. This is because one can observe the results of a biological process and have knowledge of the molecular/genetic components, but not directly witness biochemical interaction mechanisms. Hypotheses can be tested in silico which is considerably cheaper and faster than “wet” lab trialand- error experimentation. Bio-systems are traditionally modelled using ordinary differential equations (ODEs). ODEs are generally suitable for the approximation of a (test tube sized) in vitro system trajectory, but cannot account for inherent system noise or discrete event behaviour. Most in vivo biochemical interactions occur within small spatially compartmentalised units commonly known as cells, which are prone to stochastic noise due to relatively low intracellular molecular populations. Stochastic simulation algorithms (SSAs) provide an exact mechanistic account of the temporal evolution of a bio-system, and can account for noise and discrete cellular transcription and signalling behaviour. Whilst this reaction-by-reaction account of system trajectory elucidates biological mechanisms more comprehensively than ODE execution, it comes at increased computational expense. Scaling to the demands of modern biology requires ever larger and more detailed models to be executed. Scientists evaluating and engineering tissue-scale and bacterial colony sized biosystems can be limited by the tractability of their computational hypothesis testing techniques. This thesis evaluates a hypothesised relationship between SSA computational performance and biochemical model characteristics. This relationship leads to the possibility of predicting the fastest SSA for an arbitrary model - a method that can provide computational headroom for more complex models to be executed. The research output of this thesis is realised as a software package for meta-stochastic simulation called ssapredict. Ssapredict uses statistical classification to predict SSA performance, and also provides high performance stochastic simulation implementations to the wider community.Newcastle University & University of Nottingham Computing Science department

Analysis of orthopaedic device development in South Africa: Mapping the landscape and understanding the drivers of knowledge development and knowledge diffusion through networks

An orthopaedic medical device refers to a part, implant, prosthetic or orthotic which is used to address damage to the body's musculoskeletal system, primarily by providing stability and mobility. Orthopaedic medical devices play a role in injury-related disorders, which have been highlighted as a key element of the quadruple burden of disease in South Africa. In this thesis, orthopaedic devices are conceptualised as a technological field and a technological innovation system (TIS) framework is applied to understand orthopaedic device development in South Africa. Knowledge development and knowledge diffusion are fundamental components of any innovation system. The thesis hypothesises that the functions “knowledge development” and “knowledge diffusion through networks” of the orthopaedic devices TIS are influenced by contextual factors. The objectives of the study are: to identify the actors who generate knowledge for orthopaedic device development and to characterise the relationships between them; to identify focus areas of orthopaedic device development; to provide insight into the drivers and barriers to knowledge development and diffusion in the TIS; and to identify the contextual factors that influence knowledge dynamics in the TIS. These objectives are investigated using social network analysis based on bibliometric data (scientific publications and patents), keyword networks, a review of institutions, and a set of case studies where the primary data source are interviews with actors. Actors producing knowledge were from the university, healthcare, industry and science council sectors, although science councils played a small role. International actors were shown to bring new ideas into the TIS. The networks were fragmented, illustrating that knowledge diffusion through the networks was limited. This was especially the case in the patent networks as many actors patent in isolation. The keyword networks highlighted unrealised collaboration potential between actors based on their common research interests. The case studies revealed features of cross-sector interaction for orthopaedic device development not evident from network analysis based on bibliometric data. Drivers of knowledge development and knowledge diffusion were: inter-sectoral collaboration; the availability of resources; the affordability of available devices; and the positive externalities of allied TISs. The main barrier to knowledge development and diffusion was in the form of barriers to intersectoral collaboration. These include unmatched expectations from partners in collaboration, different views on intellectual property ownership, and burdensome university administrative processes. The orthopaedic devices TIS was structurally coupled to the embedded TIS and sectoral contexts, and externally linked and structurally coupled to its political context. Knowledge development and diffusion was found to be positively enhanced by innovation in the additive manufacturing TIS, with shared structural elements and resources. Knowledge development and diffusion was influenced by sectoral dynamics of the university, healthcare and industry sectors. This thesis makes the following contributions. First, it applies the TIS framework to a new focus area, namely medical device development, in a developing country context. Second, it makes two unique methodological contributions: it presents an index to capture the extent of sectoral collaboration in a network; and it develops a method for determining the collaboration potential of actors in a network based on cognitive distance

Gene Regulatory Networks: Modeling, Intervention and Context

abstract: Biological systems are complex in many dimensions as endless transportation and communication networks all function simultaneously. Our ability to intervene within both healthy and diseased systems is tied directly to our ability to understand and model core functionality. The progress in increasingly accurate and thorough high-throughput measurement technologies has provided a deluge of data from which we may attempt to infer a representation of the true genetic regulatory system. A gene regulatory network model, if accurate enough, may allow us to perform hypothesis testing in the form of computational experiments. Of great importance to modeling accuracy is the acknowledgment of biological contexts within the models -- i.e. recognizing the heterogeneous nature of the true biological system and the data it generates. This marriage of engineering, mathematics and computer science with systems biology creates a cycle of progress between computer simulation and lab experimentation, rapidly translating interventions and treatments for patients from the bench to the bedside. This dissertation will first discuss the landscape for modeling the biological system, explore the identification of targets for intervention in Boolean network models of biological interactions, and explore context specificity both in new graphical depictions of models embodying context-specific genomic regulation and in novel analysis approaches designed to reveal embedded contextual information. Overall, the dissertation will explore a spectrum of biological modeling with a goal towards therapeutic intervention, with both formal and informal notions of biological context, in such a way that will enable future work to have an even greater impact in terms of direct patient benefit on an individualized level.Dissertation/ThesisPh.D. Computer Science 201

Applications of Boolean modelling to study and stratify dynamics of a complex disease

Interpretation of omics data is needed to form meaningful hypotheses about disease mechanisms. Pathway databases give an overview of disease-related processes, while mathematical models give qualitative and quantitative insights into their complexity. Similarly to pathway databases, mathematical models are stored and shared on dedicated platforms. Moreover, community-driven initiatives such as disease maps encode disease-specific mechanisms in both computable and diagrammatic form using dedicated tools for diagram biocuration and visualisation. To investigate the dynamic properties of complex disease mechanisms, computationally readable content can be used as a scaffold for building dynamic models in an automated fashion. The dynamic properties of a disease are extremely complex. Therefore, more research is required to better understand the complexity of molecular mechanisms, which may advance personalized medicine in the future. In this study, Parkinson’s disease (PD) is analyzed as an example of a complex disorder. PD is associated with complex genetic, environmental causes and comorbidities that need to be analysed in a systematic way to better understand the progression of different disease subtypes. Studying PD as a multifactorial disease requires deconvoluting the multiple and overlapping changes to identify the driving neurodegenerative mechanisms. Integrated systems analysis and modelling can enable us to study different aspects of a disease such as progression, diagnosis, and response to therapeutics. Therefore, more research is required to better understand the complexity of molecular mechanisms, which may advance personalized medicine in the future. Modelling such complex processes depends on the scope and it may vary depending on the nature of the process (e.g. signalling vs metabolic). Experimental design and the resulting data also influence model structure and analysis. Boolean modelling is proposed to analyse the complexity of PD mechanisms. Boolean models (BMs) are qualitative rather than quantitative and do not require detailed kinetic information such as Petri nets or Ordinary Differential equations (ODEs). Boolean modelling represents a logical formalism where available variables have binary values of one (ON) or zero (OFF), making it a plausible approach in cases where quantitative details and kinetic parameters 9 are not available. Boolean modelling is well validated in clinical and translational medicine research. In this project, the PD map was translated into BMs in an automated fashion using different methods. Therefore, the complexity of disease pathways can be analysed by simulating the effect of genomic burden on omics data. In order to make sure that BMs accurately represent the biological system, validation was performed by simulating models at different scales of complexity. The behaviour of the models was compared with expected behavior based on validated biological knowledge. The TCA cycle was used as an example of a well-studied simple network. Different scales of complex signalling networks were used including the Wnt-PI3k/AKT pathway, and T-cell differentiation models. As a result, matched and mismatched behaviours were identified, allowing the models to be modified to better represent disease mechanisms. The BMs were stratified by integrating omics data from multiple disease cohorts. The miRNA datasets from the Parkinson’s Progression Markers Initiative study (PPMI) were analysed. PPMI provides an important resource for the investigation of potential biomarkers and therapeutic targets for PD. Such stratification allowed studying disease heterogeneity and specific responses to molecular perturbations. The results can support research hypotheses, diagnose a condition, and maximize the benefit of a treatment. Furthermore, the challenges and limitations associated with Boolean modelling in general were discussed, as well as those specific to the current study. Based on the results, there are different ways to improve Boolean modelling applications. Modellers can perform exploratory investigations, gathering the associated information about the model from literature and data resources. The missing details can be inferred by integrating omics data, which identifies missing components and optimises model accuracy. Accurate and computable models improve the efficiency of simulations and the resulting analysis of their controllability. In parallel, the maintenance of model repositories and the sharing of models in easily interoperable formats are also important

Connected Developments: The Governance of Formal Global Knowledge Networks in Sustainability Transformations

Climate change adds pressure to the international community to work cooperatively, find ways to govern technologies and expert knowledge, develop better policies, and mobilise resources, tools, and practices to deal with potential consequences and impacts. The institutional drivers underpinning current knowledge applications in globally connected spaces of sustainable development practice are increasingly complex, intertwined, and empirically understudied. In this context, this PhD thesis aims to advance our empirical understanding of why and how formal cooperation networks form, negotiate, mobilise and utilise particular technologies and expert knowledge and attempt to steer visions and pathways for change. This research combines multi-sited ethnography with social network analysis and policy analysis and investigates formal contexts of global connection. This thesis examines practices of science and technology policy through technology-driven networks in multiple locations in Europe and Southeast Asia. In particular, this thesis analyses the processes and conditions through which tools (e.g. modelling technologies), practices (e.g. climate negotiations, technology transfer activities, risk management, and environmental planning), and ways of dealing with climate-related uncertainties are implemented in a global knowledge network articulated under the UN system. The participant observation that is applied in the research is grounded in mobile contexts of project-based interactions, intergovernmental negotiations, international expert meetings, high-level advisory boards, technology assessments, implementation of technology transfer programmes, capacity-building workshops, expert discussions on anticipation and uncertainty, and the production of reports, climate policies, and procurement systems. This thesis examines how the artefacts of transfer interact in the implementation of the Technology Mechanism under the UNFCCC, drawing on cases of climate and hydrological modelling ranging from the Climate Technology Centre and Network (CTCN) to Thailand and Myanmar. It maps and analyses the global response of networked organisations, with special attention to persistent North South power dynamics imposed by global environmental governance regimes and their emergent ‘transformational claims’. This thesis delves into a critical evaluation of transformational change narratives in institutionalised knowledge systems, practices of technology transfer, and science policy spaces inside the United Nations. It contributes to a better foundational understanding of knowledge governance relating to critical social and environmental challenges, and rethinks futures of collective climate action in light of sustainability transformations theory and practice