Building upon our existing flagship industry-linked EPSRC & MRC CDT in Systems Approaches to Biomedical Science (SABS), the new EPSRC CDT in Sustainable Approaches to Biomedical Science: Responsible and Reproducible Research - SABS:R^3 - will train a further five cohorts, each of 15 students, in cutting-edge systems approaches to biomedical research and, uniquely within the UK, in advanced practices in software engineering. Our renewed goal is to bring about a transformation of the research culture in computational biomedical science. Computational methods are now at the heart of biomedical research. From the simulation of the behaviour of complex systems, through the design and automation of laboratory experiments, to the analysis of both small and large-scale data, well-engineered software has proved capable of transforming biomedical science. Biomedical science is therefore dependent as never before on research software. Industries reliant on this continued innovation in biomedical science play a critical role in the UK economy. The biopharmaceutical and medical technology industrial sectors alone generate an annual turnover of over £63 billion and employ 233,000 scientists and staff. In his foreword to the 2017 Life Sciences Industrial Strategy, Sir John Bell noted that, "The global life sciences industry is expected to reach >$2 trillion in gross value by 2023... there are few, if any, sectors more important to support as part of the industrial strategy." The report identifies the need to provide training in skills in "informatics, computational, mathematical and statistics areas" as being of major concern for the life sciences industry. Over the last 9 years, the existing SABS CDT has been working with its consortium of now 22 industrial and institutional partners to meet these training needs. Over this same period, continued advances in information technology have accelerated the shift in the biomedical research landscape in an increasingly quantitative and predictive direction. As a result, computational and hence software-driven approaches now underpin all aspects of the research pipeline. In spite of this central importance, the development of research software is typically a by-product of the research process, with the research publication being the primary output. Research software is typically not made available to the research community, or even to peer reviewers, and therefore cannot be verified. Vast amounts of research time is lost (usually by PhD students with no formal training in software development) in re-implementing already-existing solutions from the literature. Even if successful, the re-implemented software is again not released to the community, and the cycle repeats. No consideration is made of the huge benefits of model verification, re-use, extension, and maintainability, nor of the implications for the reproducibility of the published research. Progress in biomedical science is thus impeded, with knock-on effects into clinical translation and knowledge transfer into industry. There is therefore an urgent need for a radically different approach. The SABS:R^3 CDT will build on the existing SABS Programme to equip a new generation of biomedical research scientists with not only the knowledge and methods necessary to take a quantitative and interdisciplinary approach, but also with advanced software engineering skills. By embedding this strong focus on sustainable and open computational methods, together with responsible and reproducible approaches, into all aspects of the new programme, our computationally-literate scientists will be equipped to act as ambassadors to bring about a transformation of biomedical research.

Some radioactive molecules emit gamma radiation that can be detected outside the body and so when injected into humans and animals, in safe low levels, can be used to generate images, using sophisticated scanners, of the brain for biomedical research and clinical diagnosis. However, the molecules have to be designed to target the sites of the brain to be investigated, which is done by attaching a biological compound to the radioactive molecule to create products called radiopharmaceuticals. Due to the severe lack of scientists in the UK who have the specialised skills to design and prepare these radiopharmaceuticals we plan to recruit and train a scientist to join our multidisplicinary team. To achieve this we have created a bespoke training programme which will involve learning from researchers in academia and industry, who are either developing and/or using this imaging technology. As part of this training the scientist will help design new radiopharmaceuticals that would then be used in our on-going research programmes to understand the biological mechanisms of some major diseases and disorders of the brain, and thereby identify some possible treatments. For this specific programme we would be undertaking research projects on traumatic brain injury, depression, schizophrenia, obsessive compulsive disorder and drug addiction.

Pathology is the branch of medicine that studies the cause, origin, and nature of diseases through the examination of tissue biopsies at a microscopic level. Pathology slides are traditionally handled by cutting a tissue sample into paper-thin sections, and staining them so to bring out regions of interest (RoIs). A pathologist places these paper-thin sections on a glass slide under a microscope in order to look for a range of features that aid in confirming the presence and malignancy level of the disease. For example, in the case of cancer biopsies, the pathologist analyses the shape, size and amount of abnormal and normal cell nuclei in the tissue to confirm the existence and progression of the tumour. Recent advances on whole-slide digital scanners have made possible the digitization of pathology slides, allowing their storage and manipulation in digital form. The digitized versions of pathology slides, which are called virtual slides or whole-slide images (WSIs), are complementing traditional analysis techniques that rely on pathologists looking under a microscope with techniques that rely on pathologists looking at digital images on a monitor. Moreover, digitization of these slides also allows providing telepathology services by sharing WSIs and thus reaching isolated hospitals and medical centres. For example, thanks to telepathology, pathologists would be able to send WSIs electronically to others or post them on a secure web-site making them available for consultation with other pathologists. As a consequence, more pathologists may be brought into the process of making a diagnosis, thus avoiding medical errors. Due to the high resolution required to digitize pathology slides, the resulting WSIs tend to be huge in file size, which results in heavy demands for storage and transmission resources. For example, the digitization of a single core of prostate biopsy tissue, of roughly the dimensions of a stamp, could easily result in 900 million pixels. By comparison, a photograph of 4x5 inches in size scanned at 300 dots per inch, which is the standard resolution for printing in a magazine, results in only 1.8 million pixels. So, WSIs usually require around 500 times more pixels than regular digital images. Moreover, a single pathology study normally comprises more than one biopsy sample. For example, in the case of prostate cancer studies, more than 10 biopsy samples are often required per patient, resulting in hundreds of gigabytes of imaging data per study. As a consequence, the main challenge that currently prevents telepathology from being widely used in clinical settings is the huge file size of WSIs, which makes the access and transmission of these data over different channels lengthy. Additionally, their huge file size also prevents WSIs from being widely used in current Picture Archiving and Communications Systems (PACS), which comprise a collection of software and network infrastructure used in hospitals and medical centres to store, share and display medical images. Integrating WSIs into PACS would allow pathologist to use other patient data available in PACS in order to increase the accuracy of diagnosis. Therefore, designing efficient coding methods capable of facilitating the access and transmission of WSIs for telepathology applications, while allowing integrating these data into PACS, remains a challenge. This project is mainly concerned with the design of such methods.

In our work in the current edition of the CMIH we have built up a strong pool of researchers and collaborations across the board from mathematics, statistics, to engineering, medical physics and clinicians. Our work has also confirmed that imaging data is a very important diagnostic biomarker, but also that non-imaging data in the form of health records, memory tests and genomics are precious predictive resources and that when combined in appropriate ways should be the source for AI-based healthcare of the future. Following this philosophy, the new CMIH brings together researchers from mathematics, statistics, computer science and medicine, with clinicians and relevant industrial stakeholder to develop rigorous and clinically practical algorithms for analysing healthcare data in an integrated fashion for personalised diagnosis and treatment, as well as target identification and validation on a population level. We will focus on three medical streams: Cancer, Cardiovascular disease and Dementia, which remain the top 3 causes of death and disability in the UK. Whilst applied mathematics and mathematical statistics are still commonly regarded as separate disciplines there is an increasing understanding that a combined approach, by removing historic disciplinary boundaries, is the only way forward. This is especially the case when addressing methodological challenges in data science using multi-modal data streams, such as the research we will undertake at the Hub. This holistic approach will support the Hub aims to bring AI for healthcare decision making to the clinical end users.

Changes in the environment inside cells can be considered as alterations in cellular chemistry. The cellular environment can be thought to span a spectrum between reducing conditions (often characterised by a lack of oxygen, and the presence of chemicals that contain hydrogen) and oxidising conditions (often characterised by the presence of oxygen and reactive oxygen-containing species). The spectrum of REDucing to OXidising environment is known as REDOX chemistry. The REDOX environment in the cell results from external stimuli, and affects the function of the cell. Consequently, the REDOX environment can give rise to cellular changes that result in diseases. In this work, we propose that the reverse is also true - that the REDOX state of a cell at a given time will provide predictive information on the fate of a particular cell. Therefore, if it were possible to gain a global picture of the cellular REDOX state, this would be a revolutionary way of predicting cell fate, and hence treating disease. For this new technique to work we need a range of molecular tools that tell us about a given component of the REDOX state at any given time. The aim of our work is to develop and validate tools that detect the intracellular molecules that affect the cellular REDOX state, and provide imaging feedback on that state. By combing the feedback from several of these molecular tools we can infer information on the overall REDOX state. To achieve this aim we have assembled a team of people with the wide range of skills required to make the proposed molecular tools. Our team includes synthetic inorganic and organic chemists, people skilled in a range of imaging techniques, and biological scientists who will be able to apply the molecular tools that we will make. Only by combining the skills of everybody in our team will we be able to achieve the aims of this ambitious, but potentially revolutionary, programme of research.