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.

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.

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.

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.

The term "dementia" is used to describe a syndrome that results, initially, in cognitive function impairment and in many cases, a descending staircase of psychological dysfunction, leading eventually to death. It is a major socio-economic challenge with care costs approaching 1% of global GDP. Several conditions that lead to serious loss of cognitive ability are grouped under this syndrome, including Alzheimer's disease (AD), Vascular Dementia (VaD), Frontotemporal Dementia, etc. A high publicity announcement was made in 2012, by the Prime Minister, emphasising the high priority that should be given to dementia-related research and that funding will more than double in the immediate future, to partially remedy the fact that the overwhelming impact of the syndrome has been over-looked (Guardian, 26/3/12). On Dec 2013, the G8 Summit hosted in London brought together G8 ministers, researchers, pharmaceutical companies, and charities to develop co-ordinated global action on dementia. Dementia has marked adverse effects on the quality of life of tens of millions of people (both patients and carers) and exerts tremendous pressure on healthcare systems, especially when clear trends towards an ageing population, changing environmental influences and contemporary lifestyle choices are considered. Ca. 35M people suffer from dementia worldwide, a figure to quadruple by 2050. Europe and North America share a disproportionally high burden: the effects of ageing are particularly stark for these regions, exacerbating the healthcare provision implications. The Clinical Relevance: Vascular Cognitive Impairment (VCI). VCI defines alterations in cognition attributable to cerebrovascular causes, ranging from subtle or fixed deficits to full-blown dementia. VCI is a wide and accepted term referring to the "syndrome with evidence of clinical stroke or subclinical vascular brain injury and cognitive impairment affecting at least one cognitive domain", with resulting VaD being its most severe form. VaD is responsible for at least 20% of dementias, second only to AD, with a prevalence doubling every 5. 3 years. Several trials examined cholinesterase inhibitors for the treatment of vascular dementia, but the benefits are very modest, except in the individuals with a combination of AD and VaD. Vascular changes result in white matter (WM) damage (leukoaraiosis), which profoundly affect the fidelity of the information transfer underlying brain function and cognitive health8. Cerebral Magnetic Resonance Imaging (MRI) of Diffusion and Perfusion. MRI is a medical imaging technique affording non-invasive investigation of anatomy and tissue function, which is particularly suited to studying cognitive disorders due to its sensitivity and reliability. Our main interest is to characterise vascular and non-vascular tissues using quantitative diffusion and perfusion MR. Our overall aim is to characterise and quantify early differential alterations in brain blood transport and subsequent microstructural tissue damage using one-stop-shop perfusion/diffusion MR GSI incorporating novel MR signal models and optimal MR sequence design based on new human brain histomorphometric data in health and disease.