Computer-based technologies are becoming one of the most promising novel approaches due to continuously accelerated growth of both hardware processing power and software algorithm efficiency. One recent example includes machine learning algorithms that revolutionised data analysis in computer science, and lead to new computer games, visual recognition, and other applications that overtake human performance in many cases. Here, we propose to perform atomistic molecular simulations using novel enhanced sampling algorithms. Most biologically important processes take place on significantly longer timescales than those accessible to current computer simulations. Therefore, to obtain meaningful and accurate results regarding the kinetics and conformational dynamics of complex molecular systems, we use algorithms that enhance the sampling using parallel calculations with different biases. Developing more optimal biasing algorithms will allow us to model faster and more accurately the key biological processes of interest, including ligand binding, protein conformations, etc. Here we aim to use statistical algorithms inspired by machine learning to develop novel enhanced sampling methods for molecular simulations. Novel algorithms can be applied to a wide range of molecular modeling problems. We will focus on phosphate catalytic enzymes, and study key DNA processing enzymes to reveal the catalytic mechanism in these systems. Due to the essential nature of phosphate catalytic enzymes in most biological processes, a large number of drugs in current clinical practice also target phosphate-processing enzymes treating a wide range of diseases. Examples include reverse transcriptase and integrase inhibitors used against HIV and hepatitis B, proton pump inhibitors used in gastric diseases, kinase, PARP and topoisomerase inhibitors used against a large number of cancers. Studying phosphate catalytic systems with modern molecular modeling methods will enable fundamental advances in our current knowledge of the molecular basis of life. It will also create opportunities for rational development of better drugs to fight diseases.

The Oxford-Warwick Statistics Programme will train a new cohort of at least 50 graduates in the theory, methods and applications of Statistical Science for 21st Century data-intensive environments and large-scale models. This is joint project lead by the Statistics Departments of Oxford and Warwick. These two departments, ranked first and second for world leading research in the last UK research assessment exercise, can provide a wonderful stimulating training environment for doctoral students in statistics. The Centre's pool of supervisors are known for significant international research contributions in modern computational statistics and related fields, contributions recognised by over 20 major National and International Awards since 2008. Oxford and Warwick attract students with competitively won international scholarships. The programme leaders expect to expand the cohort to 11 or 12 per year by bringing these students into the CDT, and raising their funding up to CDT-level using £188K in support from industry and £150K support from donors. The need to engage in large-scale highly structured statistical models has been recognized for some time within areas like genomics and brain-imaging technologies. However, the UK's leading industries and sciences are now also increasingly aware of the enormous potential that data-driven analysis holds. These industries include the engineering, manufacturing, pharmaceutical, financial, e-commerce, life-science and entertainment sectors. The analysis bottleneck has moved from being able to collect and record relevant data to being able to interpret and exploit vast data collections. These and other businesses are critically dependent on the availability of future leaders in Statistics, able to design and develop statistical approaches that are scalable to massive data. The UK can take a world lead in this field, being a recognized international leader in Statistics; and OxWaSP is ideally placed to realize the potential of this opportunity. The Centre is focused on a new type of training for a new type of graduate statistician in statistical methodology and computation that is scalable to big data. We will bring a new focus on training for research, by teaching directly from the scientific literature. Students will be thrown straight into reading and summarizing journal papers. Lecture-format contact is used sparingly with peer-to-peer learning central to the training approach. This is teaching and learning for research by doing research. Cohort learning will be enhanced via group visits to companies, small groups reproducing results from key papers, student-orientated paper discussions, annual workshops and a three-day off-site retreat. From the second year the students will join their chosen supervisors in Warwick and Oxford, five in each Centre coming together regularly for research group meetings that overlap Oxford and Warwick, for workshops and retreats, and teaching and mentoring of students in earlier years. The Centre is timely and ambitious, designed to attract and nurture the brightest graduate statisticians, broadening their skills to meet the new challenge and allowing them to flourish in a focused, communal, research-training environment. The strategic vision is to train the next generation of statisticians who will enable the new data-intensive sciences and industries. The Centre will offer a vehicle to bring together industrial partners from across the two departments to share ideas and provide an important perspective to our students on the research challenges and opportunities within commercial and social enterprises. Student's training will be considerably enhanced through the Centre's visits, lectures, internships and co-supervision from global partners including Amazon, Google, GlaxoSmithKline, MAN and Novartis, as well as smaller entrepreneurial start-ups Deepmind and Optimor.

One of the hallmarks of eukaryotic cells is the presence of membrane-bound compartments (organelles), which create different optimised environments to promote various metabolic reactions required to sustain life. To adapt to the changing physiological requirements of a cell or organism, organelles have to constantly adjust their number, shape, position, and metabolic functions accordingly. This requires dynamic processes which modulate organelle abundance by organelle formation (biogenesis), degradation (autophagy), or inheritance (cell division). Peroxisomes are multifunctional subcellular organelles that are essential for human health and development. Vital, protective roles of peroxisomes in lipid metabolism, signalling, the combat of oxidative stress and ageing have emerged recently. Our work has revealed that peroxisomes are extremely dynamic and can form from pre-existing organelles in a multistep process which requires remodelling of the peroxisomal membrane, the formation of tubular membrane extensions which subsequently constrict and divide into several new peroxisomes. Defects in peroxisome dynamics and multiplication have been linked to age related disorders involving neurodegeneration, loss of sight and deafness. Despite their fundamental importance to cell physiology, the mechanisms that mediate and regulate peroxisome membrane dynamics and abundance in humans are poorly understood and a biophysical model is missing. Understanding these mechanisms is not only important for comprehending fundamental physiological processes but also for understanding pathogenic processes in disease etiology. The overall aim of this project is to acquire novel insights into the mechanism and regulation of peroxisome abundance, membrane dynamics and organelle cooperation in normal and disease conditions. In this research project, we will (1) assess the role of key proteins in peroxisome division to unveil the molecular mechanisms modulating peroxisome abundance, (2) apply biophysical approaches to investigate protein-lipid interaction and membrane remodelling, (3) identify mechanisms to modulate expression of key proteins and peroxisome dynamics for improvement of cell performance, and (4) develop a biophysical/mathematical model to understand and predict peroxisome dynamics in health and disease conditions. In summary, in this interdisciplinary project we will combine unique complementary expertise in organelle-biology and organelle-based disorders with biophysical and mathematical approaches as well as novel tools and models in human cell biology. We will apply molecular cell biology, biophysical, biochemical and screening approaches, mathematical modelling and cutting edge imaging techniques to reveal the molecular mechanisms and pathways that mediate and regulate organelle membrane dynamics and organelle abundance. Specifically, this research project will improve our understanding of organelle dynamics/abundance and its impact on healthy ageing and common, degenerative disorders. We will generate new tools and models for assessing and modulating organelle dynamics, which may help to improve cell performance. Understanding how to modulate organelle dynamics and abundance and to use the protective functions of organelles will be of significant biological and medical importance. It may contribute to the development of new therapeutic approaches in healthy ageing and age-related disorders.

Alcohol related liver disease (ALD) is responsible for more than 6000 deaths a year in the UK and costs the NHS £3.5 billion. Alcoholic hepatitis is a florid presentation of ALD in which patients present with jaundice and liver failure. Unfortunately, around 30% of people admitted to hospital with this condition will die within 3 months. The treatment of alcoholic hepatitis is complicated by the fact that there is tremendous inflammation within the liver whilst the patient is very susceptible to infection. As a result treatment with drugs, such as steroids, which suppress the immune system may exacerbate the risk of infection. In our recent trial we demonstrated that prednisolone (a steroid) reduced mortality by a small amount one month after admission but the advantage was lost at three months. Therefore, at present there is no effective treatment for this condition. The aim of this research is to develop clinical tests (biomarkers) which improve the management of alcoholic hepatitis and which help the pharmaceutical industry to run trials in this area. Firstly, we will use a test which measures the amount of bacterial DNA in blood to stratify the risk of infection. Identifying patients who are at high risk of infection will allow us to modify treatment, either by avoiding steroids or adding in prophylactic antibiotics. This test will also identify a group of patients who would benefit from new treatment options. Our second aim is to improve the way in which we predict the outcome of this disease. We have previously shown that low transferrin (a serum protein) and a variant of the gene PNPLA3 are associated with a poor prognosis. An existing blood test (ELF), which is a good prognostic test in chronic hepatitis, will be tested in alcoholic hepatitis patients. We propose to combine the new biomarkers with routine clinical data and, using sophisticated statistical techniques, generate a more accurate prognostic scoring system. This will allow us to select patients more carefully for clinical trials, for intensive care and for liver transplantation. Although it is possible to make a diagnosis of alcoholic hepatitis based on the clinical presentation, we sometimes need to perform a liver biopsy to confirm the diagnosis. Furthermore, a biopsy is usually required in clinical trials. We are planning to develop a blood test based on the levels of a bile acid, taurocholate, which will reduce or eliminate the need for liver biopsy. In patients with alcoholic hepatitis the immune system is impaired making them susceptible to infections that increase the risk of dying. Analysis of the characteristics of immune cells in the blood will allow us to identify immune profiles which confer susceptibility to infection. We will use these immune profiles to evaluate new drugs in order to assess whether they are likely to increase the risk of infection either by testing the drugs on immune cells in the laboratory or by conducting immune profiling in the early stages of clinical trials. If our programme of research is successful we should be able to use existing drugs more effectively by avoiding complication such as infection. In addition we will encourage and facilitate pharmaceutical companies to invest in this disease area where there is a substantial unmet medical need.

Theory and modelling plays an increasingly central role in chemistry and related sciences, providing the means to understand, predict and design new molecules and new materials. It is of central scientific importance now, and is vital for key high-tech sectors of economic importance to the UK. The field is interdisciplinary - building on chemical, physical, mathematical and computational principles - but is founded on a common set of ideas and research methods. Yet in the UK at large graduate training in the field is generally inadequate, for three main reasons: 1. Research in theory and modelling requires knowledge and skills that chemical scientists do not acquire during their undergraduate studies. 2. Training has traditionally taken place within the confines of particular sub-areas (quantum chemistry, materials modelling, bio-molecular simulation, statistical mechanics and so on). 3. Most UK universities lack the staff required to provide coherent doctoral training across the subject, and none individually can deliver the breadth of our proposed CDT. In consequence, such training as currently takes place is usually piecemeal and inefficient, operates within inhibiting confines, and fails to expose students to both the breadth and unity of the field. For these reasons a structured, cohort-based approach to graduate training is imperative. The need for improved breadth and depth of training is further demanded by industry, and is necessary for the UK to leverage competitive advantage from significant recent investment in high performance computing. Together with our industrial partners, we have thus designed an innovative, student-centred training programme that will deliver the doctoral scientists needed by academia and industry to capitalise on the central role of modelling in chemical and allied sciences, and to develop the theory needed to realise a new generation of computational techniques. Our training will follow a 1+3 model, with all year-1 students based in Oxford, and awarded an MSc at the end of year-1. Considerable training will take place before student-led choice of PhD projects, enabling an informed choice to be made. Delivered by 21 'core' faculty from O-B-S, the course will contain both core modules containing underpinning material all students must know, ranging from basic theory and mathematics, to software engineering and methods of computer simulation; together with a diverse range of option modules, enabling students to tailor a learning-route to their evolving interests. Modules will include formal lectures, plus an integrated range of student-centred learning and skills-training activities, often peer-to-peer, and including problem-based and computer-aided learning. The course will further contain a series of seminars, and a team-based creative workshop, on Software Development; two 6-week projects under the supervision of a CDT staff member (some in collaboration with industrial partners); and a series of Research Showcase Seminars aimed in particular at exposing CDT students to the research of potential doctoral advisors. Cohort-centred training activities will also take place in years 2-4, including HPC training in year-2, exploiting the excellent infrastructure and expertise available in our universities, and delivered in partnership with external providers; a 3-day Annual CDT Symposium; a Residential Teamworking Course; a series of cohort-centred, informal student seminars; a further range of cohort-tailored transferable skills training, and public engagement activities; and an Industry Careers Forum. Engagement with the wider UK community is central to our vision for the CDT. Four students p/a will be able to participate in our year-1 CDT programme, before proceeding to a PhD in a university outside O-B-S. The National Training Schools in Theoretical Chemistry will also become a flagship CDT outreach activity.