Bayesian methodology for genetics of complex diseases

Genetic research of complex diseases is a challenging, but exciting, area of research. The early development of the research was limited, however, until the completion of the Human Genome and HapMap projects, along with the reduction in the cost of genotyping, which paves the way for understanding the genetic composition of complex diseases. In this thesis, we focus on the statistical methods for two aspects of genetic research: phenotype definition for diseases with complex etiology and methods for identifying potentially associated Single Nucleotide Polymorphisms (SNPs) and SNP-SNP interactions. With regard to phenotype definition for diseases with complex etiology, we firstly investigated the effects of different statistical phenotyping approaches on the subsequent analysis. In light of the findings, and the difficulties in validating the estimated phenotype, we proposed two different methods for reconciling phenotypes of different models using Bayesian model averaging as a coherent mechanism for accounting for model uncertainty. In the second part of the thesis, the focus is turned to the methods for identifying associated SNPs and SNP interactions. We review the use of Bayesian logistic regression with variable selection for SNP identification and extended the model for detecting the interaction effects for population based case-control studies. In this part of study, we also develop a machine learning algorithm to cope with the large scale data analysis, namely modified Logic Regression with Genetic Program (MLR-GEP), which is then compared with the Bayesian model, Random Forests and other variants of logic regression.

The benefits of not managing change and not communicating : towards a complex systems view of communication in evolving organisations

The relationship between change in organisations and communication about change in organisations can be analysed as a particular case of a general debate in social theory about the extent to which reality is socially constructed. Social constructivists emphasise the role of language in the construction of social realities, enacted through controlling the message agenda; material determinists assert that economic and social structural factors are more constitutive of reality as seen in strategies emphasising structural and resource interventions. Here we define a third view of language and materiality - one that leads to the potential for a reflexive, experimental approach to change based on the view that organisations are complex evolving systems.

Schooling and refugees : engaging with the complex trajectories of globalisation

This paper examines the complexities associated with educating a mobile and politically marginalised population, refugee students, in the state of Queensland, Australia. Historically, schools have been national institutions concerned with social reproduction and citizenship formation with a focus on spatially fixed populations. While education authorities in much of the developed world now acknowledge the need to prepare students for a more interconnected world of work and opportunity, they have largely failed to provide systemic support for one category of children on the move - refugees. We begin this paper with a discussion of forced migration and its links with ‘globalisation’. We then present our research findings about the educational challenges confronting individual refugee youth and schools in Queensland. This is followed with a summary of good practice in refugee education. The paper concludes with a discussion of how nation-states might play a more active role in facilitating transitions to citizenship for refugee youth.

Stochastic simulation of chemical reactions in spatially complex media

Discrete stochastic simulations, via techniques such as the Stochastic Simulation Algorithm (SSA) are a powerful tool for understanding the dynamics of chemical kinetics when there are low numbers of certain molecular species. However, an important constraint is the assumption of well-mixedness and homogeneity. In this paper, we show how to use Monte Carlo simulations to estimate an anomalous diffusion parameter that encapsulates the crowdedness of the spatial environment. We then use this parameter to replace the rate constants of bimolecular reactions by a time-dependent power law to produce an SSA valid in cases where anomalous diffusion occurs or the system is not well-mixed (ASSA). Simulations then show that ASSA can successfully predict the temporal dynamics of chemical kinetics in a spatially constrained environment.

Using complex network metrics to predict the persistence of metapopulations with asymmetric connectivity patterns

Almost all metapopulation modelling assumes that connectivity between patches is only a function of distance, and is therefore symmetric. However, connectivity will not depend only on the distance between the patches, as some paths are easy to traverse, while others are difficult. When colonising organisms interact with the heterogeneous landscape between patches, connectivity patterns will invariably be asymmetric. There have been few attempts to theoretically assess the effects of asymmetric connectivity patterns on the dynamics of metapopulations. In this paper, we use the framework of complex networks to investigate whether metapopulation dynamics can be determined by directly analysing the asymmetric connectivity patterns that link the patches. Our analyses focus on “patch occupancy” metapopulation models, which only consider whether a patch is occupied or not. We propose three easily calculated network metrics: the “asymmetry” and “average path strength” of the connectivity pattern, and the “centrality” of each patch. Together, these metrics can be used to predict the length of time a metapopulation is expected to persist, and the relative contribution of each patch to a metapopulation’s viability. Our results clearly demonstrate the negative effect that asymmetry has on metapopulation persistence. Complex network analyses represent a useful new tool for understanding the dynamics of species existing in fragmented landscapes, particularly those existing in large metapopulations.

Using participatory action research in a community-based initiative addressing complex mental health needs

Objective: This paper describes the first phase of a larger project that utilizes participatory action research to examine complex mental health needs across an extensive group of stakeholders in the community. Method: Within an objective qualitative analysis of focus group discussions the social ecological model is utilized to explore how integrative activities can be informed, planned and implemented across multiple elements and levels of a system. Seventy-one primary care workers, managers, policy-makers, consumers and carers from across the southern metropolitan and Gippsland regions of Victoria, Australia took part in seven focus groups. All groups responded to an identical set of focusing questions. Results: Participants produced an explanatory model describing the service system, as it relates to people with complex needs, across the levels of social ecological analysis. Qualitative themes analysis identified four priority areas to be addressed in order to improve the system's capacity for working with complexity. These included: (i) system fragmentation; (ii) integrative case management practices; (iii) community attitudes; and (iv) money and resources. Conclusions: The emergent themes provide clues as to how complexity is constructed and interpreted across the system of involved agencies and interest groups. The implications these findings have for the development and evaluation of this community capacity-building project were examined from the perspective of constructing interventions that address both top-down and bottom-up processes.

The contribution of leadership attributes to large scale, complex project success

Mismanagement of large-scale, complex projects has resulted in spectacular failures, cost overruns, time blowouts, and stakeholder dissatisfaction. We focus discussion on the interaction of key management and leadership attributes which facilitate leaders’ adaptive behaviors. These behaviors should in turn influence adaptive team member behavior, stakeholder engagement and successful project outcomes, outputs and impacts. An understanding of this type of management will benefit from a perspective based in managerial and organizational cognition. The research question we explore is whether successful leaders of large-scale complex projects have an internal process leading to a display of administrative, adaptive, and enabling behaviors that foster adaptive processes and enabling behaviors within their teams and with external stakeholders. At the core of the model we propose interactions of key attributes, namely cognitive flexibility, affect, and emotional intelligence. The result of these cognitive-affective attribute interactions is leadership leading to enhanced likelihood of complex project success.