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.

When glaciers retreat, their forefields present a unique opportunity to investigate the initial phases of soil formation and microbial succession. As the ice retreats leaving space for microbial and plant colonisation, some studies show evidence of an increase in a variety of microbial proxies, such as nitrogen fixation, microbial enzymatic activity and diversity, in relation to years of exposure until certain soil stability is reached. Surprisingly, very little is known regarding the genetic and functional diversity of microbes in Arctic habitats. The composition and the metabolic potential of the entire microbial population can be explored by isolating and characterising their genetic material recovered directly from the environment using a metagenomic approach. Each sample of a soil habitat analysed represents a snapshot of the complex mixture of different microbial types and some types will be much more abundant than others. For instance, we predict in this project that genes associated with phototrophic C and N fixation and aerobic C metabolism will be predominant at the initial stages of succession in soil after glacial retreat, while deeper soil samples will provide conditions for anaerobic C and N metabolism to develop, include the production and consumption methane, which is a very powerful greenhouse gas. The metagenomic approach can be further linked to rates of metabolism and geochemical characteristics of soils, many of those factors have strong feedbacks with each other. There have been few integrated studies which link microbial diversity to ecosystem function and the biogeochemical cycling of key elements (C, N, Fe). This proposal aims to employ such integrated approach to generate new and uniquely datasets of genetic and functional diversity of representative terrestrial Arctic habitats. The project will instigate a step jump in our understanding of metabolic pathways of terrestrial Arctic habitats to improve biogeochemical models and quantification of the full metabolic package during successional events in soils after glacial retreat. The forefields of 2 glaciers (one in Svalbard and one in Greenland, which represent one small polar system and one major ice sheet, respectively) will be chosen for this project because they provide a range of forefield habitats of different sizes, locations, vegetation and availability of water surrounding the system. Samples for the metagenomic analyses will be taken from representative soils representing different ages of exposure after glacial retreat. We aim to generate several orders of magnitude more primary sequence data than existing metagenome pipelines were originally designed to deal with. This sampling strategy will give us a high-resolution picture of the microbial genetic and metabolic diversity associated with key elements (e.g., C, N, Fe) of glacial forefield habitats, also allowing us to PREDICT changes in metabolic pathways and biogeochemical cycles in response to glacial retreat. The project will instigate a step jump in our understanding of the biodiversity of glacial Arctic terrestrial habitats and provide a database that may be used to interpret data recovered during future. This will ultimately give us valuable insights in relation to the potential for life in other icy planets and moons and during the so called Snowball Earth. Data generated in this proposal can be incorporated into models of carbon, nitrogen, iron and sulphur cycling.

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.

Modelling and simulation play important roles in designing everything from planes to cars to bridges. However, advances in connectivity and computing now enable models to be linked directly to a specific object or system, creating a "digital twin". Digital twins represent a computational surrogate for a particular object and are updated through time as more information becomes available. However, digital twins are not limited to manufactured objects alone. This project aims to develop digital twins of patients, where a model will track a patient through time. We focus on making digital twins of patients' hearts using detailed imaging data sets over the period of a clinical trial. This is the first step towards models that are updated in real-time, track the patient throughout their life and directly feed back into informing patient care. The digital twin approach builds on patient-specific computer models of the heart that are currently being evaluated to guide procedures in the UK at King's College London and in the US. These models are designed to optimise treatments for a specific patient's pathophysiology but only simulate a small number of heartbeats. Digital twins, which track a patient through time, will forecast disease progression and response to therapy. This represents the next step in simulation guided therapy, where the optimal treatment and, importantly, when to deliver it, will be predicted. This project will address the technical challenges in calibrating computer models of large numbers of patients, how to efficiently update these models through time as more data becomes available, how to analyse images of the heart recorded over the duration of a clinical trial and how to predict complex changes in shape and function of the heart. The approaches will be applied to study three patient groups in three studies. First, we will test if multi-scale cardiac biomechanics models can identify common causes of pump dysfunction in heart failure patients. Second, we will test if digital twins can predict which patients who have recovered from heart failure can stop their heart failure mediation. Thirdly, we will test if digital twin forecasts can be used to predict recovery and pre-empt the need for advanced heart failure therapy in newly diagnosed heart failure patients. This will provide the first demonstration of cardiac biomechanics digital twins using real clinical data to answer important clinical questions.

Although an increasing number of people survive heart attacks, the scar left in their heart muscle leaves them at an increased risk of developing lethal cardiac 'arrhythmias' (abnormal beating of the heart) following the initial attack. Little is known about the underlying processes linking the presence of scars to increased death from cardiac arrhythmias. Specifically, it is not well understood whether the scar is involved in the actual generation of the arrhythmia, or whether it just helps to stabilise an arrhythmic episode generated by another mechanism, unrelated to the scar itself. As a result, diagnosis and therapy planning is non-optimal for these patients, and the rate of sudden death due to arrhythmic events is still high within this population. Current clinical tools can provide useful information regarding scars within patients who have suffered prior heart attacks. Clinical magnetic resonance (MR) imaging gives an important non-invasive means of analysing the location and shape of scars in patients. In addition, analysis of clinical electrocardiogram (ECG) recordings during arrhythmia can suggest not only the type of arrhythmia, but also the role the scar may play in such episodes. In particular, careful analysis of the shape of the ECG trace in the first few arrhythmic beats has suggested that, in many cases, the scar itself is highly likely to be the actual source of the ectopic activity responsible for generating the arrhythmia. Basic science investigations have shown that the structure of the tissue in and around the scar is highly diverse, and that the functional electrical properties are also changed from that of the normal, healthy cardiac tissue. As such, how the scar may act to generate lethal arrhythmia is thought to involve highly complex processes, which are not yet well understood. Our goal is to use computer modelling alongside high-resolution animal and clinical images to gain an in-depth understanding of the underlying processes involved in the generation of lethal arrhythmias directly from within cardiac scars. By using high-resolution animal images of scars, we will generate exceptionally-detailed computational models to investigate how the interaction between structural and functional diversity within a scar may encourage the generation of arrhythmia. This will allow us to understand how the fine-scaled properties of the scar and surrounding tissue make it susceptible to arrhythmias, identifying key 'hot spot' regions which represent the most dangerous potential sources of arrhythmic activity. We will then use this knowledge in comparison with patient MR and arrhythmia incidence data to make an important step towards translating these findings into the clinic, helping provide a mechanistic explanation of the underlying observed relationships uncovered in the clinical data. Overall, the findings from this research will pave the way for improved of risk stratification in patients with cardiac scars, and the development of novel clinically-useful therapies targeting the scar as a source of arrhythmia generation. The potential beneficiaries from this research will be extensive due to the high incidence of heart attacks annually in the UK (124,000), and the significant risk posed by arrhythmia to individuals following a heart attack. Consequently, this work also has the potential to reduce the health and economic costs of associated death and illness.