Clinical diagnosis is seldom definitive. Clinical data are noisy and sparse, and often support multiple diagnoses and potential therapies. To decide how best to treat a patient requires identifying the many possible outcomes for an individual and their corresponding probabilities. In this project we will apply the mathematics of uncertainty quantification, developed for automotive, geological and meteorological predictions, combined with biophysical models of individual patient physiology and pathophysiology to predict patient outcomes and their corresponding probabilities. This will demonstrate how patient specific computational models can be used to make prospective predictions to guide procedures and inform uncertain clinical decisions. The use of uncertainty quantification and predictive patient specific models will be applied to patients with atrial fibrillation. Atrial fibrillation (AF) is the most common cardiac arrhythmia in the UK. In patients who do not respond to drug treatment, the pathological regions of the atria are removed or isolated through catheter ablation. However, up to 40% of patients with advanced (persistent) AF require further ablations to treat atrial tachycardia (pathological but regular activation) that develops after they have had an initial ablation to treat their AF. To reduce the number of additional procedures, this project will predict the probability that a patient will develop atrial tachycardia and the path that the atrial tachycardia will take, based on measurements recorded at the time of the initial persistent AF ablation procedure. If successful this approach would guide preventative ablations during the initial procedure to reduce the need for repeat procedures.

In the diagnosis and treatment of disease, clinicians base their decisions on understanding of the many factors that contribute to medical conditions, together with the particular circumstances of each patient. This is a "modelling" process, in which the patient's data are matched with an existing conceptual framework to guide selection of a treatment strategy based on experience. Now, after a long gestation, the world of in silico medicine is bringing sophisticated mathematics and computer simulation to this fundamental aspect of healthcare, adding to - and perhaps ultimately replacing - less structured approaches to disease representation. The in silico specialisation is now maturing into a separate engineering discipline, and is establishing sophisticated mathematical frameworks, both to describe the structures and interactions of the human body itself, and to solve the complex equations that represent the evolution of any particular biological process. So far the discipline has established excellent applications, but it has been slower to succeed in the more complex area of soft tissue behaviour, particularly across wide ranges of length scales (subcellular to organ). This EPSRC SoftMech initiative proposes to accelerate the development of multiscale soft-tissue modelling by constructing a generic mathematical multiscale framework. This will be a truly innovative step, as it will provide a common language with which all relevant materials, interactions and evolutions can be portrayed, and it will be designed from a standardised viewpoint to integrate with the totality of the work of the in silico community as a whole. In particular, it will integrate with the EPSRC MultiSim multiscale musculoskeletal simulation framework being developed by SoftMech partner Insigneo, and it will be validated in the two highest-mortality clinical areas of cardiac disease and cancer. The mathematics we will develop will have a vocabulary that is both rich and extensible, meaning that we will equip it for the majority of the known representations required but design it with an open architecture allowing others to contribute additional formulations as the need arises. It will already include novel constructions developed during the SoftMech project itself, and we will provide many detailed examples of usage drawn from our twin validation domains. The project will be seriously collaborative as we establish a strong network of interested parties across the UK. The key elements of the planned scientific advances relate to the feedback loop of the structural adaptations that cells make in response to mechanical and chemical stimuli. A major challenge is the current lack of models that operate across multiple length scales, and it is here that we will focus our developmental activities. Over recent years we have developed mathematical descriptions of the relevant mechanical properties of soft tissues (arteries, myocardium, cancer cells), and we have access to new experimental and statistical techniques (such as atomic force microscopy, MRI, DT-MRI and model selection), meaning that the resulting tools will bring much-need facilities and will be applicable across problems, including wound healing and cancer cell proliferation. The many detailed outputs of the work include, most importantly, the new mathematical framework, which will immediately enable all researchers to participate in fresh modelling activities. Beyond this our new methods of representation will simplify and extend the range of targets that can be modelled and, significantly, we will be devoting major effort to developing complex usage examples across cancer and cardiac domains. The tools will be ready for incorporation in commercial products, and our industrial partners plan extensions to their current systems. The practical results of improved modelling will be a better understanding of how our bodies work, leading to new therapies for cancer and cardiac disease.

Heart disease is the leading cause of disability and death in the UK and worldwide, resulting in enormous health care costs. Risk prediction on an individual patient basis is imperfect. Advanced medical development has already saved many lives, particularly in systolic heart failure. However, there is currently no treatment option for diastolic heart failure (with preserved ejection fraction) due to its complexity of multiple mechanisms and co-modality. Structural heart diseases, such as myocardial infarction (MI- commonly known as heart attack) and mitral regurgitation (MR, a leakage of blood through the mitral valve to left atrium in systole), where biomechanical factors are crucial, are often precursors to heart failure. MI can eventually lead to dilated heart failure despite immediate treatments post-MI. MR can induce pulmonary hypertension and oedema and subsequently, right heart overload and heart failure. The grand challenge is for these situations the heart simply cannot be modelled as an isolated left ventricle (as in most of the current studies); flow-structure interaction (FSI), heart-valve interaction, multiscale soft tissue mechanics, and tissue growth and remodelling (G&R) all play important roles in the progression of the structural diseases. This project is set up to meet this challenge by delivering a multiscale computational framework to include Whole-Heart FSI with G&R. Making use of the novel mathematical tools (constitutive laws, G&R, upscaling and statistical inference) developed by SofTMech, I will build a realistic four-chamber heart model that include heart-valve, chamber-chamber, heart-blood, and heart-circulation interactions, which will be powerful enough to model MI, MR and their pathological consequences. This work will be in close collaboration with my clinical, industrial and academic collaborators. The model will quantify which factors lead to adverse G&R and what variations are to be expected as the disease progresses. We will also identify significant biomechanical markers (e.g. constitutive parameters, energy indices, stress/strain evolution). The predictive values of these biomechanical parameters will be assessed against other established predictors of adverse remodellings, such as duration of ischaemia, final coronary flow grade after a primary percutaneous coronary intervention, and microvascular obstruction revealed by MRI. Thus, this project will generate new testable hypotheses and will be a significant step up towards more consistent decision-support for clinicians, since increasingly the pace and complexity of medical advances outstrip the ability of individual clinicians to cope with. Due to the statistical emulation and uncertainty quantification built into the project, the model predictions will be fast and quantified with error bounds on the outcome of alternative treatments. Consequently, we will also address the critical aspect of convincing clinicians that information obtained from simulations will be correct and relevant to their daily practice. The proposed research is right within the Healthcare Technologies "Optimising Treatment" and "Developing Future Therapies" priority areas, as well as targeting "New Connections from Mathematical Sciences", and "Statistics and Applied Probability" of Mathematical Sciences.

Our genetic material is constantly subjected to damage. Much of this damage comes not from external sources such as carcinogens or radiation, but from the intrinsic biological processes that DNA has to perform to allow our cells to function. One key biological function of DNA involves its replication, so that each of the two daughter cells produced during cell division receives a complete and accurate copy of all the genetic information. Even in normal cells, this copying process is not 100% perfect, and the resulting errors can lead to mutations that alter cell function, potentially leading to cell death or other changes that can lead to cancer. Recent findings have shown that some cancers are associated with defects in the polymerase enzymes that copy DNA. DNA polymerases are normally highly accurate, but defects in these polymerases can increase the error rate when the genome is copied, and this can accelerate the development of some types of cancer in tissues where there is a high rate of cell multiplication. In this grant we will study these defective polymerases in detail, to understand which changes found in human cells are likely to be pathogenic. We will study how the polymerase malfunctions, the consequences for the process of chromosome replication and the types of copying mistakes which result. Understanding the properties of the cancer-associated DNA polymerase should help to explain how these defects lead to tumour formation. It is also possible that drugs might be developed that target the replication of tumour cells that express these variant polymerases.

With each heart beat a wave of electrical activation sweeps across the heart stimulating the muscles to contract. In the healthy heart the wave is initiated from many locations across the wall and rapidly activates the whole heart leading to a synchronous, efficient and effective pumping of blood around the body. In patients suffering dyssynchronous heart failure the activation wave starts on the right hand side of the heart and slowly progresses to the left hand side of the heart. This asynchronous activation pattern causes an asynchronous, inefficient and ineffective pumping of blood. To treat these patients a pacing device is implanted with leads attached to the left and right hand side of the heart. By activating the left and right side of the heart from these two leads the patient's activation pattern can be resynchronised leading to a synchronous and effective contraction. This treatment is referred to as cardiac resynchronisation therapy or CRT. CRT is an effective treatment in most patients but 30-50% of patients fail to improve or respond to treatment. Due to the invasive nature and cost of the procedure it is undesirable to treat patients who will not respond. Identifying the patients who cannot respond is currently obfuscated by the inability to guarantee optimal treatment in all cases. Hence it is not possible to differentiate from patients that did not respond as they did not receive the optimal treatment from those that were unable to benefit from CRT under any conditions. At present guidelines suggest a "one size fits all" approach to the location of the leads on the patient's heart despite significant evidence that the location of the leads plays a critical role in determining outcome. This indicates that some patients may respond to CRT but only if they receive optimal lead placement. The aim of this project is to determine the best location to place the pacing lead on the left side of the heart in each individual patient receiving CRT, based on the physiology and pathology of the specific patient's heart. To achieve this aim we propose to use advanced high fidelity and resolution imaging techniques to characterise the shape of the patient's heart, the potential pacing locations, and the location of any dead non-conducting tissue in the heart. We will combine this anatomical information with measurements of electrical activation time to create a biophysical model of the electrical properties of the individual patient's heart. Using the model we will be able to simulate the activation patterns in the patient's heart for each potential pacing location. In a training data set we will compare the activation patterns at each pacing location with measured pump function, in response to pacing, to identify the activation pattern that best predicts the optimal pacing location. A prospective clinical study will then be performed where patient specific models will be created for each patient prior to procedure and the optimal pacing site identified. The predictive capacity of the model will then be evaluated when the device is implanted by testing if the model has correctly predicted the optimal pacing location. The project represents a significant advance for patient specific models - moving from a technique for analysing patient data to a tool for guiding patient treatment. Improving outcomes for CRT patients will reduce morbidity and hospitalisation rates, decrease the financial burden of non-responding patients on the NHS and improve our ability to identify what characteristics determine if a patient will respond to treatment.