Discovery by Virtual Screening of Ethionamide Boosters for Tuberculosis Treatment

Tuberculosis remains the world’s deadliest communicable bacterial disease with an unacceptably high death rate. In 2013 an estimated 1.5 million people died as a direct result of TB, and nine million new cases were reported. Multi-drug resistant (MDR) and extensively drug-resistant (XDR) tuberculosis cases are on the rise and without novel approaches to combat their spread, tuberculosis will continue to claim the lives of millions worldwide. One such novel approach is to rejuvenate the use of the second-line antibiotic ethionamide. Ethionamide is a structural analogue of the first-line pro-drug isoniazid, which is used widely and to which there is growing resistance. Ethionamide was introduced in the 1960s and primarily used in cases of drug-resistant TB due to its severe adverse effects. This makes ethionamide an exploitable target for small-molecule booster drugs. Expression of the enzyme responsible for ethionamide activation, EthA, is regulated by a transcriptional repressor EthR which can be inhibited to improve ethionamide activation and so reduce ethionamide treatment doses and bring an old drug new life in the clinic. EthR inhibitors are currently in development; here, chemoinformatic pipelining and virtual screening in GOLD were used to identify hits with novel scaffolds for hit-to-lead efforts from an initial library of over six million drug-like molecules. Thermal shift assays were used to identify EthR-binding molecules and SPR was utilised to confirm and potentially quantify binding affinities. Herein are reported the co-crystal structures of several hit molecules, used to confirm and characterise the EthR-ligand complexes. Through the application of computational, biophysical and crystallographic methods, this thesis presents several novel scaffolds for development against EthR. These novel hits will be developed to expand our arsenal against the growing, global problem of drug-resistant TB

An Optimisation-based Framework for Complex Business Process: Healthcare Application

The Irish healthcare system is currently facing major pressures due to rising demand, caused by population growth, ageing and high expectations of service quality. This pressure on the Irish healthcare system creates a need for support from research institutions in dealing with decision areas such as resource allocation and performance measurement. While approaches such as modelling, simulation, multi-criteria decision analysis, performance management, and optimisation can – when applied skilfully – improve healthcare performance, they represent just one part of the solution. Accordingly, to achieve significant and sustainable performance, this research aims to develop a practical, yet effective, optimisation-based framework for managing complex processes in the healthcare domain. Through an extensive review of the literature on the aforementioned solution techniques, limitations of using each technique on its own are identified in order to define a practical integrated approach toward developing the proposed framework. During the framework validation phase, real-time strategies have to be optimised to solve Emergency Department performance issues in a major hospital. Results show a potential of significant reduction in patients average length of stay (i.e. 48% of average patient throughput time) whilst reducing the over-reliance on overstretched nursing resources, that resulted in an increase of staff utilisation between 7% and 10%. Given the high uncertainty in healthcare service demand, using the integrated framework allows decision makers to find optimal staff schedules that improve emergency department performance. The proposed optimum staff schedule reduces the average waiting time of patients by 57% and also contributes to reduce number of patients left without treatment to 8% instead of 17%. The developed framework has been implemented by the hospital partner with a high level of success

Evaluation of production control strategies for the co-ordination of work-authorisations and inventory management in lean supply chains

A decision support framework is proposed for assisting managers and executives to possibly utilise lean production control strategies to coordinate work authorisations and inventory management in supply chains. The framework allows decision makers to evaluate and compare the suitability of various strategies to their system especially when considering conflicting objectives, such as maximising customer service levels while minimising Work in Process (WIP) in a business environment distressed by variabilities and uncertainties in demand stemmed from customer power. Also, the framework provides decision guidance in selecting and testing optimal solutions of selected policies control parameters. The framework is demonstrated by application to a four-node serial supply-chain operating under three different pull-based supply chain strategies; namely CONWIP, Kanban, and Hybrid Kanban-CONWIP and exhibiting low, medium, and high variability in customer demand (i.e., coefficient of variation of 25%, 112.5%, and 200%). The framework consists of three phases; namely Modelling, Optimisation and Decision Support; and is applicable to both Simulation-Based and Metamodel-Based Optimisation. The Modelling phase includes conceptual modelling, discrete event simulation modelling and metamodels development. The Optimisation phase requires the application of multi-criteria optimisation methods to generate WIP-Service Level trade-off curves. The Curvature and Risk Analysis of the trade-off curves are utilised in the Decision Support phase to provide guidance to the decision maker in selecting and testing the best settings for the control parameters of the system. The inflection point of the curvature function indicates the point at which further increases in Service Level are only achievable by incurring an unacceptably higher cost in terms of average WIP. Risk analysis quantifies the risk associated with designing a supply chain system under specific environmental parameters. This research contributes an efficient framework that is applicable to solve real supply chain problems and better understanding of the potential impacts and expected effectiveness of different pull control mechanisms, and offers valuable insights on future research opportunities in this field to production and supply chain managers

Integrating the finite element method and genetic algorithms to solve structural damage detection and design optimisation problems

This thesis documents fundamental new research in to a specific application of structural box-section beams, for which weight reduction is highly desirable. It is proposed and demonstrated that the weight of these beams can be significantly reduced by using advanced, laminated fibre-reinforced composites in place of steel. Of the many issues raised during this investigation two, of particular importance, are considered in detail; (a) the detection and quantification of damage in composite structures and (b) the optimisation of laminate design to maximise the performance of loaded composite structuress ubject to given constraints. It is demonstrated that both these issues can be formulated and solved as optimisation problems using the finite element method, in which an appropriate objective function is minimised (or maximised). In case (a) the difference in static response obtained from a loaded structure containing damage and an equivalent mathematical model of the structure is minimised by iteratively updating the model. This reveals the damage within the model and subsequently allows the residual properties of the damaged structure to be quantified. Within the scope of this work is the ability to resolve damage, that consists of either penny-shaped sub-surface flaws or tearing damage of box-section beams from surface experimental data. In case (b) an objective function is formulated in terms of a given structural response, or combination of responses that is optimised in order to return an optimal structure, rather than just a satisfactory structure. For the solution of these optimisation problems a novel software tool, based on the integration of genetic algorithms and a commercially available finite element (FE) package, has been developed. A particular advantage of the described method is its applicability to a wide range of engineering problems. The tool is described and its effectiveness demonstrated with reference to two inverse damage detection and quantification problems and one laminate design optimisation problem. The tool allows the full suite of functions within the FE software to be used to solve non-convex optimisation problems, formulated in terms of both discrete and continuous variables, without explicitly stating the form of the stiffness matrix. Furthermore, a priori knowledge about the problem may be readily incorporated in to the method

An integrated operation and maintenance framework for offshore renewable energy

Offshore renewable devices hold a large potential as renewable energy sources, but their deployment costs are still too high compared to those of other technologies. Operation and maintenance, as well as management of the assets, are main contributors to the overall costs of the projects, and decision-support tools in this area are required to decrease the final cost of energy.\\ In this thesis a complete characterisation and optimisation framework for the operation, maintenance and assets management of an offshore renewable farm is presented. The methodology uses known approaches, based on Monte Carlo simulation for the characterisation of the key performance indicators of the offshore renewable farm, and genetic algorithms as a search heuristic for the proposal of improved strategies. These methods, coupled in an integrated framework, constitute a novel and valuable tool to support the decision-making process in this area. The methods developed consider multiple aspects for the accurate description of the problem, including considerations on the reliability of the devices and limitations on the offshore operations dictated by the properties of the maintenance assets. Mechanisms and constraints that influence the maintenance procedures are considered and used to determine the optimal strategy. The models are flexible over a range of offshore renewable technologies, and adaptable to different offshore farm sizes and layouts, as well as maintenance assets and configurations of the devices. The approaches presented demonstrate the potential for cost reduction in the operation and maintenance strategy selection, and highlight the importance of computational tools to improve the profitability of a project while ensuring that satisfactory levels of availability and reliability are preserved. Three case studies to show the benefits of application of such methodologies, as well as the validity of their implementation, are provided. Areas for further development are identified, and suggestions to improve the effectiveness of decision-making tools for the assets management of offshore renewable technologies are provided.European CommissionMojo Ocean Dynamics Ltd. T/A Mojo Maritime Lt

Understanding the Molecular Mechanism of Single-Strand Annealing Homologous DNA Recombination in Viruses, by Cryo-Electron Microscopy

The single-strand annealing homologous recombination (SSA) is one of the dsDNA break repair pathways, and albeit its importance from bacteria to bacteriophages, its molecular function is still unknown. The SSA reaction is catalysed by the enzyme complexes known as Exonuclease Annealase Two-component Recombinase (EATRs). The RecT and ORF6 proteins are single-stranded DNA-binding and annealing proteins expressed in E. coli and Kaposi’s sarcoma-associated herpesvirus (KSHV), respectively. RecT has already been shown to catalyse the SSA reaction. Although ORF6 has been shown to bind to ssDNA, further experimental evidence is needed to solidify its annealase activity. Since structure can dictate the function, this thesis aimed to determine the structure of the annealases RecT and ORF6 using a state-in-art cryo-electron microscopy technique. Furthermore, the shadow-casting EM technique has been established by optimising it for the equipment available at UOW, which is helpful for imaging the substrate DNA intermediates and the nucleoprotein complexes formed during SSA to better understand the molecular mechanistic details of this reaction. This thesis includes the details about RecT and ORF6 proteins’ cloning, expression, and purification, which were further optimised for purity and homogeneity for cryo-electron microscopy with the help of negative staining electron microscopy (NSEM). Additionally, based on several NSEM analyses, the C-terminal His-tag containing RecT (RecTCH) oligomerisation on ssDNA was studied, and a general mechanism of its oligomerisation is described. Unfortunately, during the RecTCH protein’s cryo-EM sample optimisation, the LiRecT structure was published by another group. Therefore, work on that project was ceased at that point. Several novel findings on ORF6 are reported in this thesis. Primarily, the concentration of the purified protein was increased 3 times more than the reports in the literature. Based on the NSEM and preliminary cryo-EM map of ORF6, it is shown that the ORF6 structure overall resembles the HSV1-ICP8 protein. Further, based on the steady-state and time-resolved fluorescence resonance energy transfer (FRET) experiments, a model for the ORF6 annealing mechanism is suggested. Towards generating a high-resolution structure, ORF6 monomers and filaments were optimised and imaged by using cryo-EM. Processing a data set obtained from a monomeric ORF6 sample showed the presence of conformational heterogeneity in the particles, which was expected as the ORF6 AlphaFold model shows that the N-terminal and C-terminal domains are connected by an 18 amino acids long loop, allowing C-terminal domain to be relatively flexible to move around. Processing of another data set obtained from a sample containing ORF6 filaments generated 2-dimensional averages that look promising for generating a high-resolution structure. This thesis also shows the details related to the installation and optimisation of the shadowing technique using a modern material, graphene oxide (GO), as a support film. This technique involves optimising both sample preparation and instrumentation for metal evaporation and deposition. For sample preparation, GO was deposited on cryo-EM holey grids, on which the sample was mounted. For instrumentation optimisation, a DENTON brand evaporator was used. The grid stage was re-engineered using AutoCAD to achieve the finest metal evaporation, and parameters such as amperage, vacuum, metal thickness, and angles were optimised. The optimised parameters were used to shadow-cast different lengths of DNA and their complexes with proteins, and good contrast images were acquired for qualitative and quantitative analyses. Overall, this thesis presents two main novel findings. First, RecTCH monomers oligomerise into an open ring-shaped structure, which stacks together to generate short filaments. Second, to anneal two complementary ssDNA strands, ORF6 first forms filaments with both ssDNA, which then come in contact with each other rapidly to anneal the complementary strands. Once the annealing finishes, the annealed dsDNA is released from the filaments as the filaments fall apart into monomers. We also found that ORF6 monomers oligomerise to form the helical and non-helical filaments in the presence of DTT+Mg2+ and DTT-containing buffer, respectively

Bi-level optimisation and machine learning in the management of large service-oriented field workforces.

The tactical planning problem for members of the service industry with large multi-skilled workforces is an important process that is often underlooked. It sits between the operational plan - which involves the actual allocation of members of the workforce to tasks - and the strategic plan where long term visions are set. An accurate tactical plan can have great benefits to service organisations and this is something we demonstrate in this work. Sitting where it does, it is made up of a mix of forecast and actual data, which can make effectively solving the problem difficult. In members of the service industry with large multi-skilled workforces it can often become a very large problem very quickly, as the number of decisions scale quickly with the number of elements within the plan. In this study, we first update and define the tactical planning problem to fit the process currently undertaken manually in practice. We then identify properties within the problem that identify it as a new candidate for the application of bi-level optimisation techniques. The tactical plan is defined in the context of a pair of leader-follower linked sub-models, which we show to be solvable to produce automated solutions to the tactical plan. We further identify the need for the use of machine learning techniques to effectively find solutions in practical applications, where limited detail is available in the data due to its forecast nature. We develop neural network models to solve this issue and show that they provide more accurate results than the current planners. Finally, we utilise them as a surrogate for the follower in the bi-level framework to provide real world applicable solutions to the tactical planning problem. The models developed in this work have already begun to be deployed in practice and are providing significant impact. This is along with identifying a new application area for bi-level modelling techniques

Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions

Copyright © 2014 Elsevier. NOTICE: this is the author’s version of a work that was accepted for publication in Environmental Modelling and Software. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Environmental Modelling and Software Vol. 62 (2014), DOI: 10.1016/j.envsoft.2014.09.013The development and application of evolutionary algorithms (EAs) and other metaheuristics for the optimisation of water resources systems has been an active research field for over two decades. Research to date has emphasized algorithmic improvements and individual applications in specific areas (e.g. model calibration, water distribution systems, groundwater management, river-basin planning and management, etc.). However, there has been limited synthesis between shared problem traits, common EA challenges, and needed advances across major applications. This paper clarifies the current status and future research directions for better solving key water resources problems using EAs. Advances in understanding fitness landscape properties and their effects on algorithm performance are critical. Future EA-based applications to real-world problems require a fundamental shift of focus towards improving problem formulations, understanding general theoretic frameworks for problem decompositions, major advances in EA computational efficiency, and most importantly aiding real decision-making in complex, uncertain application contexts

Development of genetic algorithm for optimisation of predicted membrane protein structures

Due to the inherent problems with their structural elucidation in the laboratory, the computational prediction of membrane protein structure is an essential step toward understanding the function of these leading targets for drug discovery. In this work, the development of a genetic algorithm technique is described that is able to generate predictive 3D structures of membrane proteins in an ab initio fashion that possess high stability and similarity to the native structure. This is accomplished through optimisation of the distances between TM regions and the end-on rotation of each TM helix. The starting point for the genetic algorithm is from the model of general TM region arrangement predicted using the TMRelate program. From these approximate starting coordinates, the TMBuilder program is used to generate the helical backbone 3D coordinates. The amino acid side chains are constructed using the MaxSprout algorithm. The genetic algorithm is designed to represent a TM protein structure by encoding each alpha carbon atom starting position, the starting atom of the initial residue of each helix, and operates by manipulating these starting positions. To evaluate each predicted structure, the SwissPDBViewer software (incorporating the GROMOS force field software) is employed to calculate the free potential energy. For the first time, a GA has been successfully applied to the problem of predicting membrane protein structure. Comparison between newly predicted structures (tests) and the native structure (control) indicate that the developed GA approach represents an efficient and fast method for refinement of predicted TM protein structures. Further enhancement of the performance of the GA allows the TMGA system to generate predictive structures with comparable energetic stability and reasonable structural similarity to the native structure