Flux balance analysis of biological systems: applications and challenges

Systems level modelling and simulations of biological processes are proving to be invaluable in obtaining a quantitative and dynamic perspective of various aspects of cellular function. In particular, constraint-based analyses of metabolic networks have gained considerable popularity for simulating cellular metabolism, of which flux balance analysis (FBA), is most widely used. Unlike mechanistic simulations that depend on accurate kinetic data, which are scarcely available, FBA is based on the principle of conservation of mass in a network, which utilizes the stoichiometric matrix and a biologically relevant objective function to identify optimal reaction flux distributions. FBA has been used to analyse genome-scale reconstructions of several organisms; it has also been used to analyse the effect of perturbations, such as gene deletions or drug inhibitions in silico. This article reviews the usefulness of FBA as a tool for gaining biological insights, advances in methodology enabling integration of regulatory information and thermodynamic constraints, and finally addresses the challenges that lie ahead. Various use scenarios and biological insights obtained from FBA, and applications in fields such metabolic engineering and drug target identification, are also discussed. Genome-scale constraint-based models have an immense potential for building and testing hypotheses, as well as to guide experimentation.

Truckies and workplace health promotion: Targeting a hard-to-reach group

- Introduction Research identifies truck drivers as being at high risk of chronic disease. For most truck drivers their workplace is their vehicle. Truck drivers’ health is impacted by the limitations of this unique working environment, including reduced opportunities for physical activity and the intake of healthy foods. Workplaces are widely recognised as effective platforms for health promotion. However, the effectiveness of traditional and contemporary health promotion interventions in truck drivers’ novel workplace is unknown. - Methods This project worked with six transport industry workplaces in Queensland, Australia over a two-year period. Researchers used Participatory Action Research (PAR) processes to engage truck drivers and workplace managers in the implementation and evaluation of six workplace health promotion interventions. These interventions were designed to support truck drivers to increase their physical activity and access to healthy foods at work. They included traditional health promotion interventions such as a free fruit initiative, a ten thousand steps challenge, personal health messages and workplace posters, and a contemporary social media intervention. Participants were engaged via focus groups, interviews and mixed-methods surveys. - Results The project achieved positive changes in truck drivers’ health knowledge and health behaviours, particularly related to nutrition. There were positive changes in truck drivers’ self-reported health rating, body mass index (BMI) and readiness to make health-related lifestyle changes. There were also positive changes in truck drivers reporting their workplace as a key source of health information. These changes were underpinned by a positive shift in the culture of participating workplaces. Truck drivers’ perceptions of their workplace valuing, encouraging, modelling and facilitating healthy nutrition and physical activity behaviours improved. PAR processes enabled researchers to develop relationships with workplace managers, contextualise interventions and deliver rigorous outcomes. Despite the novelty of truck drivers’ mobile workplace, traditional health promotion interventions were more effective than contemporary ones. - Conclusion In this workplace health promotion project targeting a ‘hard-to-reach’ group of truck drivers, a combination of well-designed traditional workplace interventions and the PAR process resulted in positive health outcomes.

Simple software processes and tests improve the reliability and usefulness of a model.

Models are abstractions of reality that have predetermined limits (often not consciously thought through) on what problem domains the models can be used to explore. These limits are determined by the range of observed data used to construct and validate the model. However, it is important to remember that operating the model beyond these limits, one of the reasons for building the model in the first place, potentially brings unwanted behaviour and thus reduces the usefulness of the model. Our experience with the Agricultural Production Systems Simulator (APSIM), a farming systems model, has led us to adapt techniques from the disciplines of modelling and software development to create a model development process. This process is simple, easy to follow, and brings a much higher level of stability to the development effort, which then delivers a much more useful model. A major part of the process relies on having a range of detailed model tests (unit, simulation, sensibility, validation) that exercise a model at various levels (sub-model, model and simulation). To underline the usefulness of testing, we examine several case studies where simulated output can be compared with simple relationships. For example, output is compared with crop water use efficiency relationships gleaned from the literature to check that the model reproduces the expected function. Similarly, another case study attempts to reproduce generalised hydrological relationships found in the literature. This paper then describes a simple model development process (using version control, automated testing and differencing tools), that will enhance the reliability and usefulness of a model.

Numerical studies of gypsum plasterboard and MGO board lined LSF walls exposed to fire

Fire resistance of cold-formed light gauge steel frame (LSF) wall systems is enhanced by lining them with single or multiple layers of wall boards with varying thermal properties. These wall boards are gypsum plasterboards or Magnesium Oxide (MgO) boards produced by different manufacturers. Thermal properties of these boards appear to show considerable variations and this can lead to varying fire resistance levels (FRL) for their wall systems. Currently FRLs of wall systems are determined using full scale fire tests, but they are time consuming and expensive. Recent research studies on the fire performance of LSF wall systems have used finite element studies to overcome this problem, but they were developed based on 1-D and 2-D finite element platform capable of performing either heat transfer or structural analysis separately. Hence in this research a 3-D finite element model was developed first for LSF walls lined with gypsum plasterboard and cavity insulation materials. Accurate thermal properties of these boards are essential for finite element modelling, and thus they were measured at both ambient and elevated temperatures. This experimental study included specific heat, relative density and thermal conductivity of boards. The developed 3-D finite element model was then validated using the available fire tests results of LSF walls lined with gypsum plasterboard, and is being used to investigate the fire performance of different LSF wall configurations. The tested MgO board exhibited significant variations in their thermal properties in comparison to gypsum plasterboards with about 50% loss of its initial mass at about 500 ºC compared to 16% for gypsum plasterboards. Hence the FRL of MgO board lined LSF wall systems is likely to be significantly reduced. This paper presents the details of this research study on the fire performance of LSF wall systems lined with gypsum plasterboard and MgO board including the developed 3-D finite element models, thermal property tests and the results.

Statistical analysis of the associations of hereditary factors with the risk of Type 1 diabetes and Diabetic Nephropathy based on the familial data collected through ascertaiment from population-based registers

In genetic epidemiology, population-based disease registries are commonly used to collect genotype or other risk factor information concerning affected subjects and their relatives. This work presents two new approaches for the statistical inference of ascertained data: a conditional and full likelihood approaches for the disease with variable age at onset phenotype using familial data obtained from population-based registry of incident cases. The aim is to obtain statistically reliable estimates of the general population parameters. The statistical analysis of familial data with variable age at onset becomes more complicated when some of the study subjects are non-susceptible, that is to say these subjects never get the disease. A statistical model for a variable age at onset with long-term survivors is proposed for studies of familial aggregation, using latent variable approach, as well as for prospective studies of genetic association studies with candidate genes. In addition, we explore the possibility of a genetic explanation of the observed increase in the incidence of Type 1 diabetes (T1D) in Finland in recent decades and the hypothesis of non-Mendelian transmission of T1D associated genes. Both classical and Bayesian statistical inference were used in the modelling and estimation. Despite the fact that this work contains five studies with different statistical models, they all concern data obtained from nationwide registries of T1D and genetics of T1D. In the analyses of T1D data, non-Mendelian transmission of T1D susceptibility alleles was not observed. In addition, non-Mendelian transmission of T1D susceptibility genes did not make a plausible explanation for the increase in T1D incidence in Finland. Instead, the Human Leucocyte Antigen associations with T1D were confirmed in the population-based analysis, which combines T1D registry information, reference sample of healthy subjects and birth cohort information of the Finnish population. Finally, a substantial familial variation in the susceptibility of T1D nephropathy was observed. The presented studies show the benefits of sophisticated statistical modelling to explore risk factors for complex diseases.

Paddock to Reef Monitoring and Evaluation - Economic analysis of ‘ABCD’ cane management for the Burdekin Delta region.

The economic analysis is based on the A, B, C and D management practice framework for water quality improvement developed in 2007/2008 by the respective natural resource management region. This document focuses on the economic implications of these management practices in the Burdekin Delta region. A review of the management practices is currently being undertaken to clarify some issues and incorporate new knowledge since the earlier version of the framework. However, this updated version is not yet complete and so the Paddock to Reef project has used the most current available version of the framework for the modelling and economics.

Sustainable management for rangelands in a variable climate: evidence and insights from northern Australia

Inter-annual rainfall variability is a major challenge to sustainable and productive grazing management on rangelands. In Australia, rainfall variability is particularly pronounced and failure to manage appropriately leads to major economic loss and environmental degradation. Recommended strategies to manage sustainably include stocking at long-term carrying capacity (LTCC) or varying stock numbers with forage availability. These strategies are conceptually simple but difficult to implement, given the scale and spatial heterogeneity of grazing properties and the uncertainty of the climate. This paper presents learnings and insights from northern Australia gained from research and modelling on managing for rainfall variability. A method to objectively estimate LTCC in large, heterogeneous paddocks is discussed, and guidelines and tools to tactically adjust stocking rates are presented. The possible use of seasonal climate forecasts (SCF) in management is also considered. Results from a 13-year grazing trial in Queensland show that constant stocking at LTCC was far more profitable and largely maintained land condition compared with heavy stocking (HSR). Variable stocking (VAR) with or without the use of SCF was marginally more profitable, but income variability was greater and land condition poorer than constant stocking at LTCC. Two commercial scale trials in the Northern Territory with breeder cows highlighted the practical difficulties of variable stocking and provided evidence that heavier pasture utilisation rates depress reproductive performance. Simulation modelling across a range of regions in northern Australia also showed a decline in resource condition and profitability under heavy stocking rates. Modelling further suggested that the relative value of variable v. constant stocking depends on stocking rate and land condition. Importantly, variable stocking may possibly allow slightly higher stocking rates without pasture degradation. Enterprise-level simulations run for breeder herds nevertheless show that poor economic performance can occur under constant stocking and even under variable stocking in some circumstances. Modelling and research results both suggest that a form of constrained flexible stocking should be applied to manage for climate variability. Active adaptive management and research will be required as future climate changes make managing for rainfall variability increasingly challenging.

Investigation of the effects of extracellular osmotic pressure on morphology and mechanical 1 properties of individual chondrocyte

It has been demonstrated that most cells of the body respond to osmotic pressure in a systematic manner. The disruption of the collagen network in the early stages of osteoarthritis causes an increase in water content of cartilage which leads to a reduction of pericellular osmolality in chondrocytes distributed within the extracellular environment. It is therefore arguable that an insight into the mechanical properties of chondrocytes under varying osmotic pressure would provide a better understanding of chondrocyte mechanotransduction and potentially contribute to knowledge on cartilage degeneration. In this present study, the chondrocyte cells were exposed to solutions with different osmolality. Changes in their dimensions and mechanical properties were measured over time. Atomic Force Microscopy (AFM) was used to apply load at various strain-rates and the force-time curves were logged. The thin-layer elastic model was used to extract the elastic stiffness of chondrocytes at different strain-rates and at different solution osmolality. In addition, the porohyperelastic (PHE) model was used to investigate the strain-rate dependent responses under the loading and osmotic pressure conditions. The results revealed that the hypo-osmotic external environment increased chondrocyte dimensions and reduced Young’s modulus of the cells at all strain-rates tested. In contrast, the hyper-osmotic external environment reduced dimensions and increased Young’s modulus. Moreover, by using the PHE model coupled with inverse FEA simulation, we established that the hydraulic permeability of chondrocytes increased with decreasing extracellular osmolality which is consistent with previous work in the literature. This could be due to a higher intracellular fluid volume fraction with lower osmolality.

Reconfiguring agriculture through the relocation of production systems for water, environment and food security under climate change

The prospect of climate change has revived both fears of food insecurity and its corollary, market opportunities for agricultural production. In Australia, with its long history of state-sponsored agricultural development, there is renewed interest in the agricultural development of tropical and sub-tropical northern regions. Climate projections suggest that there will be less water available to the main irrigation systems of the eastern central and southern regions of Australia, while net rainfall could be sustained or even increase in the northern areas. Hence, there could be more intensive use of northern agricultural areas, with the relocation of some production of economically important commodities such as vegetables, rice and cotton. The problem is that the expansion of cropping in northern Australia has been constrained by agronomic and economic considerations. The present paper examines the economics, at both farm and regional level, of relocating some cotton production from the east-central irrigation areas to the north where there is an existing irrigation scheme together with some industry and individual interest in such relocation. Integrated modelling and expert knowledge are used to examine this example of prospective climate change adaptation. Farm-level simulations show that without adaptation, overall gross margins will decrease under a combination of climate change and reduction in water availability. A dynamic regional Computable General Equilibrium model is used to explore two scenarios of relocating cotton production from south east Queensland, to sugar-dominated areas in northern Queensland. Overall, an increase in real economic output and real income was realized when some cotton production was relocated to sugar cane fallow land/new land. There were, however, large negative effects on regional economies where cotton production displaced sugar cane. It is concluded that even excluding the agronomic uncertainties, which are not examined here, there is unlikely to be significant market-driven relocation of cotton production.

A reassessment of energy and GDP relationship: The case of Australia

This paper investigates the long- and short-run relationships between energy consumption and economic growth in Australia using the bound testing and the ARDL approach. The analytical framework utilized in this paper includes both production and demand side models and a unified model comprising both production and demand side variables. The energy-GDP relationships are investigated at aggregate as well as several disaggregated energy categories, such as coal, oil, gas and electricity. The possibilities of one or more structural break(s) in the data series are examined by applying the recent advances in techniques. We find that the results of the cointegration tests could be affected by the structural break(s) in the data. It is, therefore, crucial to incorporate the information on structural break(s) in the subsequent modelling and inferences. Moreover, neither the production side nor the demand side framework alone can provide sufficient information to draw an ultimate conclusion on the cointegration and causal direction between energy and output. When alternative frameworks and structural break(s) in time series are explored properly, strong evidence of a bidirectional relationship between energy and output can be observed. The finding is true at both the aggregate and the disaggregate levels of energy consumption.

Quarantine and surveillance strategies for plant pathogen detection and control

Incursions of plant pests and diseases pose serious threats to food security, agricultural productivity and the natural environment. One of the challenges in confidently delimiting and eradicating incursions is how to choose from an arsenal of surveillance and quarantine approaches in order to best control multiple dispersal pathways. Anthropogenic spread (propagules carried on humans or transported on produce or equipment) can be controlled with quarantine measures, which in turn can vary in intensity. In contrast, environmental spread processes are more difficult to control, but often have a temporal signal (e.g. seasonality) which can introduce both challenges and opportunities for surveillance and control. This leads to complex decisions regarding when, where and how to search. Recent modelling investigations of surveillance performance have optimised the output of simulation models, and found that a risk-weighted randomised search can perform close to optimally. However, exactly how quarantine and surveillance strategies should change to reflect different dispersal modes remains largely unaddressed. Here we develop a spatial simulation model of a plant fungal-pathogen incursion into an agricultural region, and its subsequent surveillance and control. We include structural differences in dispersal via the interplay of biological, environmental and anthropogenic connectivity between host sites (farms). Our objective was to gain broad insights into the relative roles played by different spread modes in propagating an invasion, and how incorporating knowledge of these spread risks may improve approaches to quarantine restrictions and surveillance. We find that broad heuristic rules for quarantine restrictions fail to contain the pathogen due to residual connectivity between sites, but surveillance measures enable early detection and successfully lead to suppression of the pathogen in all farms. Alternative surveillance strategies attain similar levels of performance by incorporating environmental or anthropogenic dispersal risk in the prioritisation of sites. Our model provides the basis to develop essential insights into the effectiveness of different surveillance and quarantine decisions for fungal pathogen control. Parameterised for authentic settings it will aid our understanding of how the extent and resolution of interventions should suitably reflect the spatial structure of dispersal processes.

Predictive technology and natural hazards: Risk for Australian planning authorities?

Purpose The object of this paper is to examine whether the improvements in technology that enhance community understanding of the frequency and severity of natural hazards also increased the risk of potential liability of planning authorities in negligence. In Australia, the National Strategy imposes a resilience based approach to disaster management and stresses that responsible land use planning can reduce or prevent the impact of natural hazards upon communities. Design/methodology/approach This paper analyses how the principles of negligence allocate responsibility for loss suffered by a landowner in a hazard prone area between the landowner and local government. Findings The analysis in this paper concludes that despite being able to establish a causal link between the loss suffered by a landowner and the approval of a local authority to build in a hazard prone area, it would be in the rarest of circumstances a negligence action may be proven. Research limitations/implications The focus of this paper is on planning policies and land development, not on the negligent provision of advice or information by the local authority. Practical implications This paper identifies the issues a landowner may face when seeking compensation from a local authority for loss suffered due to the occurrence of a natural hazard known or predicted to be possible in the area. Originality/value The paper establishes that as risk managers, local authorities must place reliance upon scientific modelling and predictive technology when determining planning processes in order to fulfil their responsibilities under the National Strategy and to limit any possible liability in negligence.

Species-based and community-level approaches to conservation prioritization

One major reason for the global decline of biodiversity is habitat loss and fragmentation. Conservation areas can be designed to reduce biodiversity loss, but as resources are limited, conservation efforts need to be prioritized in order to achieve best possible outcomes. The field of systematic conservation planning developed as a response to opportunistic approaches to conservation that often resulted in biased representation of biological diversity. The last two decades have seen the development of increasingly sophisticated methods that account for information about biodiversity conservation goals (benefits), economical considerations (costs) and socio-political constraints. In this thesis I focus on two general topics related to systematic conservation planning. First, I address two aspects of the question about how biodiversity features should be valued. (i) I investigate the extremely important but often neglected issue of differential prioritization of species for conservation. Species prioritization can be based on various criteria, and is always goal-dependent, but can also be implemented in a scientifically more rigorous way than what is the usual practice. (ii) I introduce a novel framework for conservation prioritization, which is based on continuous benefit functions that convert increasing levels of biodiversity feature representation to increasing conservation value using the principle that more is better. Traditional target-based systematic conservation planning is a special case of this approach, in which a step function is used for the benefit function. We have further expanded the benefit function framework for area prioritization to address issues such as protected area size and habitat vulnerability. In the second part of the thesis I address the application of community level modelling strategies to conservation prioritization. One of the most serious issues in systematic conservation planning currently is not the deficiency of methodology for selection and design, but simply the lack of data. Community level modelling offers a surrogate strategy that makes conservation planning more feasible in data poor regions. We have reviewed the available community-level approaches to conservation planning. These range from simplistic classification techniques to sophisticated modelling and selection strategies. We have also developed a general and novel community level approach to conservation prioritization that significantly improves on methods that were available before. This thesis introduces further degrees of realism into conservation planning methodology. The benefit function -based conservation prioritization framework largely circumvents the problematic phase of target setting, and allowing for trade-offs between species representation provides a more flexible and hopefully more attractive approach to conservation practitioners. The community-level approach seems highly promising and should prove valuable for conservation planning especially in data poor regions. Future work should focus on integrating prioritization methods to deal with multiple aspects in combination influencing the prioritization process, and further testing and refining the community level strategies using real, large datasets.

Toward the Discovery of Vaccine Adjuvants: Coupling In Silico Screening and In Vitro Analysis of Antagonist Binding to Human and Mouse CCR4 Receptors

Background: Adjuvants enhance or modify an immune response that is made to an antigen. An antagonist of the chemokine CCR4 receptor can display adjuvant-like properties by diminishing the ability of CD4+CD25+ regulatory T cells (Tregs) to down-regulate immune responses. Methodology: Here, we have used protein modelling to create a plausible chemokine receptor model with the aim of using virtual screening to identify potential small molecule chemokine antagonists. A combination of homology modelling and molecular docking was used to create a model of the CCR4 receptor in order to investigate potential lead compounds that display antagonistic properties. Three-dimensional structure-based virtual screening of the CCR4 receptor identified 116 small molecules that were calculated to have a high affinity for the receptor; these were tested experimentally for CCR4 antagonism. Fifteen of these small molecules were shown to inhibit specifically CCR4-mediated cellmigration, including that of CCR4(+) Tregs. Significance: Our CCR4 antagonists act as adjuvants augmenting human T cell proliferation in an in vitro immune response model and compound SP50 increases T cell and antibody responses in vivo when combined with vaccine antigens of Mycobacterium tuberculosis and Plasmodium yoelii in mice.

Using Bayesian networks to model surveillance in complex plant and animal health systems

In this chapter we consider biosecurity surveillance as part of a complex system comprising many different biological, environmental and human factors and their interactions. Modelling and analysis of surveillance strategies should take into account these complexities, and also facilitate the use and integration of the many types of different information that can provide insight into the system as a whole. After a brief discussion of a range of options, we focus on Bayesian networks for representing such complex systems. We summarize the features of Bayesian networks and describe these in the context of surveillance.