Many diseases, including cancer, heart disease and diabetes, are caused by the body's cells and tissues malfunctioning. The behaviour of any cell is 'programmed' by its genes and researchers have found that cell behaviour can be altered by changing DNA, RNA and other genetic material in the cell. Gene therapies work by inserting new genetic material into malfunctioning cells, and 'reprogramming' them to function more normally, or by inserting genetic material into a normal cell to produce a new protein (e.g. for a vaccine). Gene therapy offers hope to patients with diseases that have been, up to now, incurable. The UK has excellent teams of researchers who are exploring this exciting topic. Unfortunately, some of the essential materials for gene research are in short supply in the UK, as are skilled technicians to work on the research. There is a particular shortage of viral vectors - special viruses that can be 'loaded' with genetic material and inserted into malfunctioning cells, where their 'payload' replaces genes causing the malfunction. The UK's scientists are held back by these shortages and forced to rely on overseas suppliers; or research is delayed due to difficulties in recruiting staff. This leads to delays in the discovery of new treatments for desperately ill patients. Another important challenge for UK researchers is that achieving great results in the lab is only half the story - before a new treatment can be offered to patients, it must be approved: shown to be safe, effective and affordable. These new treatments are great opportunities for new companies, new jobs and for the UK economy, so academics need support in patenting their discoveries, in undertaking clinical trials and in setting up companies to make new therapies. The MRC and LifeArc have recognised these important issues and are funding a network of hubs around the UK to generate the vital components for gene therapies and to train the skilled personnel needed. The NHSBT Innovation Hub for Gene Therapies will link with the other new hubs around the UK to address these needs. We will work with academic teams to produce the gene therapy components needed, we will train technicians and scientists in producing these at the quality needed for use in clinical trials, and we will support academics in taking their results from the lab to the hospital and the market. Our plan is as follows: 1. To agree our role in the network with the other hubs, MRC, LifeArc and Cell and Gene Therapy Catapult (C>C). We are particularly well placed for flexible viral vector production, quality control and training, and we expect to contribute this to the network, while also collaborating with C>C on production, with LifeArc on commercialisation and patenting, and with other hubs, as requested by the network coordinators.. 2. To install a new viral vector production platform, already selected by C>C as being particularly suitable to meet the needs of UK academic teams. We'll then work with the C>C to ensure it's certified for quality assurance (and so ready to produce clinical grade components). This new platform will produce a specific type of viral vector - adeno-associated-viral vectors (AAVs); this will complement our other platform, producing lentiviral vectors, as well as our existing platform for plasmids (another important gene therapy ingredient). 3. To be assigned academic clients and their projects by the coordinating committee, and then to work with these teams to put in place a production line and training programme that meets their needs. A flexible and customised approach is important, as every academic project will be different. 4. To build a reputation and customer base that makes the NHSBT Hub a 'go to' destination for viral vector services. As a not-for-profit NHS organisation, our aim is to be sustainable, provide excellent value for the benefit of the UK gene therapy community and, ultimately, to save and improve more lives.

Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.

Kidney failure can have a devastating impact on patients' lives. Transplantation offers much better long-term survival prospects compared to dialysis, but there is an acute shortage of donors. Compared to deceased kidney donation, living-donor kidney donation (LKD) has even better long-term patient and transplant outcomes. However, medical incompatibility, for example, may prevent a living donor from donating a kidney to a loved one who is in need. Kidney Exchange Programmes (KEPs) help to increase LKD by allowing recipients who require a kidney transplant, and who have a willing but medically incompatible donor, to "swap" their donor with that of another recipient, leading to a cycle of transplants. Altruistic donors may trigger chains of transplants that can also benefit multiple recipients. The UK Living Kidney Sharing Scheme (UKLKSS), which is operated by NHS Blood and Transplant (NHSBT), is the largest KEP in Europe. Algorithms developed by Manlove and his colleagues have been used by NHSBT to find optimal solutions for UKLKSS matching runs every quarter since 2008. There are several ways in which the UKLKSS can be expanded and strengthened in the future, to facilitate better matches and more transplants, as follows: 1. Cycles and chains are currently restricted in length for logistical reasons. Allowing longer cycles and chains than at present will lead to more kidney transplants. 2. International collaboration between the UK and other countries will lead to more transplantation opportunities, particularly for highly sensitised (hard to match) recipients. 3. In the presence of longer cycles and chains, and international collaboration, the existing interpretation of an "optimal" solution will no longer be valid. Conducting simulations will allow NHSBT to determine exactly what they wish to optimise in the light of long-term effects on simulated data. Delivering these enhancements will involve tackling the following complex research challenges: (RC1): design algorithms for larger pools and longer cycles / chains. As the underlying computational problem of finding an optimal set of kidney exchanges is intractable, advanced techniques are required to find a solution efficiently. (RC2): design algorithms for international kidney exchange. When multiple countries are participating in an international KEP, key considerations of fairness and stability become important. (RC3): design algorithms to cope with changes to optimality criteria. A small change to an optimality objective can necessitate significant changes to the algorithm to find an optimal solution. (RC4): create a dynamic dataset generator, producing instances that reflect real-world characteristics. This will give realistic estimates of the effects of different optimality criteria for NHSBT. The proposed project will meet all these challenges via a new collaboration between Glasgow and Durham. This will provide a synergy between the expertise of Manlove in matching problems and kidney exchange, and that of Paulusma in game-theoretic aspects of matching problems and international kidney exchange. The main resources requested are Postdoctoral Research Associates at Glasgow and Durham, and a Research Software Engineer at Glasgow. The project partner NHSBT will be a key member of the project team. The project will also benefit from the expertise of the following visiting researchers: Maxence Delorme (Tilburg University, operational research), Péter Biró and Márton Benedek (KRTK Budapest, algorithmic game theory). The work programme comprises three interconnected work packages, as follows: WP1: design of new algorithms for national KEPs, using advanced integer programming techniques. WP2: design of new algorithms for international KEPs, using techniques from cooperative game theory. WP3: software implementation and experimental evaluation, which will include building new software for the UKLKSS, realising the impact of this project.

Missing data are a common problem in many application areas. The presence of missing values complicates analyses, and if not dealt with properly can result in incorrect conclusions being drawn from the data. It is often helpful to assume there is a process that produces the missing values, typically called a missing data mechanism. A particularly problematic scenario is when this mechanism is in part determined by some other unknown variables, such as the missing values themselves. This is known as a missing not at random (MNAR) mechanism. If missing values arise due to a MNAR mechanism then conclusions drawn from the data will typically be biased. Also, importantly, it is not possible to know whether this problem occurs or not in the data. This is the challenging problem area that this proposal seeks to address, namely developing procedures that can best test whether or MNAR occurs in the data. The proposal will consider scenarios where it is possible to estimate some of the missing values through a follow up sample. The main purpose of this is to learn about the missing data mechanism and specifically test whether the MNAR assumption is valid or not. Further, the recovered data will also help to correct for the effect the missing data have on conclusions. The proposal makes use of optimal design techniques to decide which missing values to follow up. Essentially certain missing values might yield more information about the type of missing data mechanism than others; in addition some values might be more likely than others to be recovered. In this way we would ensure maximum information from the recovered data is obtained. This will allow data analysts to determine whether the presence of MNAR is likely and take appropriate action. We will collaborate with our project partners, the Office for National Statistics and NHS Blood and Transplant in the development of these methods. Our project partners will provide relevant data for us to consider realistic scenarios and we will discuss interim results with them to ensure our methods are most useful for practitioners. We will also present the work as part of a missing data course at the African Institute of Mathematical Sciences (AIMS) to maximise the global benefit of the work. The methods developed in this proposal will be disseminated through papers and presentations. In addition, we will create a free to use R package that will implement the methods to allow easy uptake by users. We will provide training in using this R package as part of a two-day workshop where we will describe our methods to users. A dedicated website will be updated throughout the project to describe developments and facilitate engagement with interested parties.

Matching Problems are discrete optimization problems involving a set of applicants who seek to be collectively matched to a set of objects. Applicants may have preferences over a subset of the objects, and vice versa. Preferences may be ordinal, i.e., expressible in terms of a first, second, third choice etc., or cardinal, i.e., there is a real-valued utility associated with assigning an applicant to an object. Typical goals are to maximize the size of the matching, i.e., number of matched applicant-object pairs, and/or to optimize social welfare according to the given preferences. This project will focus on three specific Matching Problems with direct practical applications: Kidney Exchange, Junior Doctor Allocation and Teacher Placement. The Kidney Exchange problem involves kidney patients who have a willing but incompatible donor "swapping" their donor with that of another patient in a similar position. The objective is to find an optimal set of swaps among patients (the applicants) and donors (the objects), taking into account the utility of a potential donor kidney to a patient. Since 2007, NHS Blood & Transplant have run the National Living Donor Kidney Sharing Schemes, which seeks out optimal sets of swaps involving kidney transplant patients and donors on their database every quarter. As every matched patient may lead to an additional life saved, optimality is an important goal. In Junior Doctor Allocation, intending junior doctors (the applicants) are to be assigned to hospital posts (the objects), on the basis of ordinal preferences of doctors over hospitals and vice versa. The UK Foundation Programme Office annually runs a centralized scheme to form an optimal allocation of doctors to hospitals, taking these preferences into account. Another example of a Matching Problem with ordinal preferences is Teacher Placement in which intending teachers (the applicants) are to be assigned to geographic regions (the objects) on the basis of teachers' ordinal preferences over regions that they are prepared to work in. In the UK, Teach First places graduates in schools serving low-income communities across England and Wales. In both applications, optimizing preferences is seen as important from both the standpoints of applicants' careers and workforce supply. Success in this respect improves participants' satisfaction and ultimately the well-being of society. In each of the three applications, current techniques used to construct optimal matchings are not scalable to larger problem sizes or more complex planning restrictions and optimality criteria. These issues will arise, for example, 1) for Kidney Exchange through the planned transnational collaboration between European countries; 2) for Junior Doctor Allocation through couples applying jointly to be matched to geographically close hospitals; 3) for Teacher Placement through the need to load-balance the allocation of teachers to schools according to regional targets. In this project we will develop novel algorithms to tackle the new challenges exemplified above. Since the underlying optimization problems are computationally hard, sophisticated optimization techniques must be used. Also, since problem instances can be large (e.g., Junior Doctor Allocation in the UK involves around 7,000 applicants annually), the algorithms must be scalable and efficient, running in seconds or minutes rather than hours or days, for both small instances and also for large instances where the number of participants is in the thousands. This project will bring together two internationally-leading research groups in a new collaboration, combining the expertise of the FATA research group at the School of Computing Science, University of Glasgow, in solving Matching Problems, with that of the the ERGO research group at School of Mathematics, University of Edinburgh, in solving integer programming problems, in order to tackle the above large and complex Matching Problems.