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.

Re-thinking how we plan to address technically complex problems : drawing on planning theory to guide practice in revision of land use plans in coastal high hazard zones

Queensland's new State Planning Policy for Coastal Protection, released in March and approved in April 2011 as part of the Queensland Coastal Plan, stipulates that local governments prepare and implement adaptation strategies for built up areas projected to be subject to coastal hazards between present day and 2100. Urban localities within the delineated coastal high hazard zone (as determined by models incorporating a 0.8 meter rise in sea level and a 10% increase in the maximum cyclone activity) will be required to re-evaluate their plans to accommodate growth, revising land use plans to minimise impacts of anticipated erosion and flooding on developed areas and infrastructure. While implementation of such strategies would aid in avoidance or minimisation of risk exposure, communities are likely to face significant challenges in such implementation, especially as development in Queensland is so intensely focussed upon its coasts with these new policies directing development away from highly desirable waterfront land. This paper examines models of planning theory to understand how we plan when faced with technically complex problems towards formulation of a framework for evaluating and improving practice.

An actor-network research frame for analysing complex socio-technical situations

This paper presents a “research frame” which we have found useful in analyzing complex socio- technical situations. The research frame is based on aspects of actor-network theory: “interressment”, “enrollment”, “points of passage” and the “trial of strength”. Each of these aspects are described in turn, making clear their purpose in the overall research frame. Having established the research frame it is used to analyse two examples. First, the use of speech recognition technology is examined in two different contexts, showing how to apply the frame to compare and contrast current situations. Next, a current medical consultation context is described and the research frame is used to consider how it could change with innovative technology. In both examples, the research frame shows that the use of an artefact or technology must be considered together with the context in which it is used.

Ageing and use of complex product interfaces

This paper presents an experiment designed to investigate if redundancy in an interface has any impact on the use of complex interfaces by older people and people with low prior-experience with technology. The important findings of this study were that older people (65+ years) completed the tasks on the Words only based interface faster than on Redundant (text and symbols) interface. The rest of the participants completed tasks significantly faster on the Redundant interface. From a cognitive processing perspective, sustained attention (one of the functions of Central Executive) has emerged as one of the important factors in completing tasks on complex interfaces faster and with fewer of errors.

Systematics of the Ustilago-Sporisorium-Macalpinomyces complex of smut fungi

Smut fungi are important pathogens of grasses, including the cultivated crops maize, sorghum and sugarcane. Typically, smut fungi infect the inflorescence of their host plants. Three genera of smut fungi (Ustilago, Sporisorium and Macalpinomyces) form a complex with overlapping morphological characters, making species placement problematic. For example, the newly described Macalpinomyces mackinlayi possesses a combination of morphological characters such that it cannot be unambiguously accommodated in any of the three genera. Previous attempts to define Ustilago, Sporisorium and Macalpinomyces using morphology and molecular phylogenetics have highlighted the polyphyletic nature of the genera, but have failed to produce a satisfactory taxonomic resolution. A detailed systematic study of 137 smut species in the Ustilago-Sporisorium- Macalpinomyces complex was completed in the current work. Morphological and DNA sequence data from five loci were assessed with maximum likelihood and Bayesian inference to reconstruct a phylogeny of the complex. The phylogenetic hypotheses generated were used to identify morphological synapomorphies, some of which had previously been dismissed as a useful way to delimit the complex. These synapomorphic characters are the basis for a revised taxonomic classification of the Ustilago-Sporisorium-Macalpinomyces complex, which takes into account their morphological diversity and coevolution with their grass hosts. The new classification is based on a redescription of the type genus Sporisorium, and the establishment of four genera, described from newly recognised monophyletic groups, to accommodate species expelled from Sporisorium. Over 150 taxonomic combinations have been proposed as an outcome of this investigation, which makes a rigorous and objective contribution to the fungal systematics of these important plant pathogens.

An approximate Bayesian computation approach for estimating parameters of complex environmental processes in a cellular automata

Modelling an environmental process involves creating a model structure and parameterising the model with appropriate values to accurately represent the process. Determining accurate parameter values for environmental systems can be challenging. Existing methods for parameter estimation typically make assumptions regarding the form of the Likelihood, and will often ignore any uncertainty around estimated values. This can be problematic, however, particularly in complex problems where Likelihoods may be intractable. In this paper we demonstrate an Approximate Bayesian Computational method for the estimation of parameters of a stochastic CA. We use as an example a CA constructed to simulate a range expansion such as might occur after a biological invasion, making parameter estimates using only count data such as could be gathered from field observations. We demonstrate ABC is a highly useful method for parameter estimation, with accurate estimates of parameters that are important for the management of invasive species such as the intrinsic rate of increase and the point in a landscape where a species has invaded. We also show that the method is capable of estimating the probability of long distance dispersal, a characteristic of biological invasions that is very influential in determining spread rates but has until now proved difficult to estimate accurately.