Ray Walshe

A Comparison of On-Premise to Cloud Migration Approaches

While cloud computing has certainly gained attention, the potential for increased uptake of the technology is still large. As a consequence, how to move and migrate to the cloud is an unanswered question for many organisations. Gaining an understanding of cloud migration processes from on-premise architectures is our aim here. For this purpose, we look at three provider-driven case studies based on the common three layers of cloud computing: Infrastructure (IaaS), platform (PaaS) and software (SaaS) as a service. These shall be complemented by a fourth, independent systems integration perspective. We extract common migration process activities for the layer-specific processes and discuss commonalities, differences and open issues. The results presented are based on expert interviews and focus groups held with major international cloud solution providers and independent consultants.

Pol II promoter prediction using characteristic 4-mer motifs: a machine learning approach

BackgroundEukaryotic promoter prediction using computational analysis techniques is one of the most difficult jobs in computational genomics that is essential for constructing and understanding genetic regulatory networks. The increased availability of sequence data for various eukaryotic organisms in recent years has necessitated for better tools and techniques for the prediction and analysis of promoters in eukaryotic sequences. Many promoter prediction methods and tools have been developed to date but they have yet to provide acceptable predictive performance. One obvious criteria to improve on current methods is to devise a better system for selecting appropriate features of promoters that distinguish them from non-promoters. Secondly improved performance can be achieved by enhancing the predictive ability of the machine learning algorithms used.ResultsIn this paper, a novel approach is presented in which 128 4-mer motifs in conjunction with a non-linear machine-learning algorithm utilising a Support Vector Machine (SVM) are used to distinguish between promoter and non-promoter DNA sequences. By applying this approach to plant, Drosophila, human, mouse and rat sequences, the classification model has showed 7-fold cross-validation percentage accuracies of 83.81%, 94.82%, 91.25%, 90.77% and 82.35% respectively. The high sensitivity and specificity value of 0.86 and 0.90 for plant; 0.96 and 0.92 for Drosophila; 0.88 and 0.92 for human; 0.78 and 0.84 for mouse and 0.82 and 0.80 for rat demonstrate that this technique is less prone to false positive results and exhibits better performance than many other tools. Moreover, this model successfully identifies location of promoter using TATA weight matrix.ConclusionThe high sensitivity and specificity indicate that 4-mer frequencies in conjunction with supervised machine-learning methods can be beneficial in the identification of RNA pol II promoters comparative to other methods. This approach can be extended to identify promoters in sequences for other eukaryotic genomes.

A computational model of antibiotic-resistance mechanisms in methicillin-resistant Staphylococcus aureus (MRSA).

An agent-based model of bacteria-antibiotic interactions has been developed that incorporates the antibiotic-resistance mechanisms of Methicillin-Resistant Staphylococcus aureus (MRSA). The model, called the Micro-Gen Bacterial Simulator, uses information about the cell biology of bacteria to produce global information about population growth in different environmental conditions. It facilitates a detailed systems-level investigation of the dynamics involved in bacteria-antibiotic interactions and a means to relate this information to traditional high-level properties such as the Minimum Inhibitory Concentration (MIC) of an antibiotic. The two main resistance strategies against beta-lactam antibiotics employed by MRSA were incorporated into the model: beta-lactamase enzymes, which hydrolytically cleave antibiotic molecules, and penicillin-binding proteins (PBP2a) with reduced binding affinities for antibiotics. Initial tests with three common antibiotics (penicillin, ampicillin and cephalothin) indicate that the model can be used to generate quantitatively accurate predictions of MICs for antibiotics against different strains of MRSA from basic cellular and biochemical information. Furthermore, by varying key parameters in the model, the relative impact of different kinetic parameters associated with the two resistance mechanisms to beta-lactam antibiotics on cell survival in the presence of antibiotics was investigated.

Modeling bacterial growth patterns in the presence of antibiotic

With recent growth in systems biology research there has been a significant increase in complex systems modeling research relating to biological systems. Multi-drug resistant (MDR) organisms are a threat not only as hospital-acquired infections, but also now as community-acquired infections. Multilocus sequence typing (MLST) can genetically characterize clones of several bacterial pathogens, allowing the tracking of hypervirulent/ antibiotic resistant lineages and the extent of acquisition and horizontal movement of the resistance genes by Feil, E.J., et al, (2004). This paper describes the initial research using an agent based cellular automata approach to model the complex sub-cellular processes in bacteria growth. Rules derived from a biological background simulate the growth of bacteria under a number of conditions including the presence of antibiotic. Altering the level of antibiotic in the bacteria environment and effects on the growth curves was explored and verified. Bacterial survival under a number of conditions (pH, temperature, nutrient concentration) emergent growth patterns and collective behaviour were also studied. A case study using the parameters reflecting the bacterium Escherichia coli was simulated and the results were validated. The software provides an in silico laboratory where bacteria can be grown under a variety of rules and conditions thereby learning the underlying mechanisms of behaviour at a local level, which collectively generate the global behaviour of interest.

Modeling Antibiotic Resistance in Bacterial Colonies Using Agent-Based Approach

The agent-based approach to modelling bacterial population growth and development is a powerful tool for understanding the relationships between changes at the individual cellular level and overall population dynamics. Agent-based models are designed from the “bottom-up”, with rules and parameters created for the individual components of the simulation rather than for the population as a whole. The behaviour of the system is therefore an emergent property of the interactions between its constituent parts. In this chapter, an agent-based model called Micro-Gen is described, which can be used to investigate the effects of antibiotic resistance mechanisms on the response of bacteria to antibiotic treatment. The agent-based approach provides a rational framework for tracing back high-level pharmacodynamic parameters, such as the MIC (Minimum Inhibitory Concentration) of an antibiotic, to low-level biochemical information about the individual molecular components. The studies were carried out on a clinically significant species of bacteria called methicillin-resistant Staphylococcus aureus (MRSA), which are characterised by their increased resistance to many commonly prescribed β-lactam antibiotics.

MODELING THE POPULATION DYNAMICS OF ANTIBIOTIC-RESISTANT BACTERIA: AN AGENT-BASED APPROACH

The response of bacterial populations to antibiotic treatment is often a function of a diverse range of interacting factors. In order to develop strategies to minimize the spread of antibiotic resistance in pathogenic bacteria, a sound theoretical understanding of the systems of interactions taking place within a colony must be developed. The agent-based approach to modeling bacterial populations is a useful tool for relating data obtained at the molecular and cellular level with the overall population dynamics. Here we demonstrate an agent-based model, called Micro-Gen, which has been developed to simulate the growth and development of bacterial colonies in culture. The model also incorporates biochemical rules and parameters describing the kinetic interactions of bacterial cells with antibiotic molecules. Simulations were carried out to replicate the development of methicillin-resistant S. aureus (MRSA) colonies growing in the presence of antibiotics. The model was explored to see how the properties of the system emerge from the interactions of the individual bacterial agents in order to achieve a better mechanistic understanding of the population dynamics taking place. Micro-Gen provides a good theoretical framework for investigating the effects of local environmental conditions and cellular properties on the response of bacterial populations to antibiotic exposure in the context of a simulated environment.

MODELING THE IMPACT OF SPATIAL STRUCTURE ON GROWTH DYNAMICS OF INVASIVE PLANT SPECIES

Invasive nonindigenous plant species can have potentially serious detrimental effects on local ecosystems and, as a result, costly control efforts often have to be put in place to protect habitats. An example of an invasive problem on a global scale involves the salt marsh grass species from the genus Spartina. The spread of Spartina anglica in Europe and Asia has drawn much concern due to its ability to convert coastal habitats into cord-grass monocultures and to alter the native food webs. However, the patterns of invasion of Spartina species are amenable to spatially-explicit modeling strategies that take into account both temporal and spatio-temporal processes. In this study, an agent-based model of Spartina growth on a simulated mud flat environment was developed in order to study the effects of spatial pattern and initial seedling placement on the invasion dynamics of the population. The spatial pattern of an invasion plays a key role in the rate of spread of the species and understanding this can lead to significant cost savings when designing efficient control strategies. We present here a model framework that can be used to explicitly represent complex spatial and temporal patterns of invasion in order to be able to predict quantitatively the impact of these factors on invasion dynamics. This would be a useful tool for assessing eradication strategies and choosing optimal control solutions in order to be able to minimize future control costs.

The Origin of the Speeches: Language Evolution through Collaborative Reinforcement Learning

This research focuses on the artificial creation of the conditions that were necessary or facilitated language in its evolution. I propose a model for a system that has the capability to allow a complex communication protocol to evolve. In human language learning, the adult humans have already mastered the language and use their knowledge to teach infant humans. 50.000 B.C. (or whenever) when there were no adult masters of language, what conditions were necessary for the language-less homo sapiens to start developing the first language? Can machines evolve a language in a similar manner across generations? This research deals with facilitating the genesis of a communication system using evolutionary computation and reinforcement learning when initially none of the conspirators have mastered the system.

Artificial Neural Network Simulation on CUDA

The advent of low cost GPU hardware and user friendly parallel programming APIs, such as NVIDIA CUDA means that affordable, programmable, high-performance computing environments for simulation are now attainable for development of scientific simulations. In this paper the authors present the Mine Hunter program, a parallel simulation of neural networks on NVIDIA CUDA. The simulation consists of 128 mine hunters in a mine field of 8192 mines, running on an Intel Quad Core i5-2500 3.3GHz 2 x Nvidia GeForce GTX 480. The results presented demonstrate that CUDA improves performance by up to 80% compared with the equivalent CPU implementation.

Performance Evaluation of Cloud-Based Parallel Computing

As computational models in fields such as medicine and engineering get more refined, resource requirements are increased. In a first instance, these needs have been satisfied using parallel computing and HPC clusters. However, such systems are often costly and lack flexibility. HPC users are therefore tempted to move to elastic HPC using cloud services. One difficulty in making this transition is that HPC and cloud systems are different, and performance may vary. The purpose of this study is to evaluate cloud services as a means to minimise both cost and computation time for large-scale simulations, and to identify which system properties have the most significant impact on performance. Our simulation results show that, while the performance of Virtual CPU (VCPU) is satisfactory, network throughput may lead to difficulties.