In numerous contexts today we are faced with making decisions of increasing size and complexity, where many different considerations interlock in complex ways. Consider a staff rostering problem to assign staff to shifts while respecting required shift patterns and staffing levels, physical and staff resources, and staff working preferences. The decision-making process is often further complicated by the need also to optimise an objective, such as to maximise profit or to minimise waste. It is natural to characterise such problems as a set of decision variables, each representing a choice that must be made in order to solve the problem at hand (e.g. which staff member is on duty for the Friday night shift), and a set of constraints describing allowed combinations of variable assignments (e.g. a staff member cannot be assigned to a day shift immediately following a night shift). A solution is an assignment of a value to each variable satisfying all constraints. Many decision-making and optimisation formalisms take this general form. In all of these formalisms the model of the problem is crucial to the efficiency with which it can be solved. A model in this sense is the set of decision variables and constraints chosen to represent a given problem. There are typically many possible models and formulating an effective model is notoriously difficult. Therefore automating modelling is a key challenge. Over the last decade, in the context of Constraint Programming we have taken a novel approach to addressing this challenge. The user writes a problem specification in the abstract constraint specification language 'Essence', capturing the structure of the problem above the level of abstraction at which modelling decisions are made. Our modelling pipeline, on which our proposed research is based, automatically generates a model from this specification. This removes the need for user constraint modelling expertise, and also preserves the structure of the specified problem, allowing the system easily to explore alternative models and to exploit properties such as symmetry. Our pipeline generates constraint models equivalent in quality to those of a competent human constraint programmer, and so represents a significant milestone towards fully automated modelling. Important challenges do, however, remain. The first is to generate models of the quality that human experts are capable. Given the inherent difficulty of these problems, and the importance of the model in mitigating that difficulty, raising the quality of the generated models is crucial. The second is to expand the range of output models beyond the constraint programming formalism. The substantial challenge we address in this proposal is to overcome these two limitations to produce a powerful, general automated modelling and solving system unique in targeting a range of solving formalisms from a single abstract constraint specification. Our existing pipeline is ideal for extension to other formalisms. The impact of this change will be substantial: combinatorial search problems are ubiquitous across the public and private sectors, and academia. We will deliver better solutions to these problems more rapidly, increasing efficiency and reducing cost.

Nowadays diagnosis is largely enabled by the identification of molecular markers associated with the onset of a pathological state. Nevertheless, many diseases escape this paradigm, as the biochemical fingerprint of the aberrant cells do not differ significantly from healthy ones, hindering early diagnosis and reducing the impact of treatments. One prototypical example is Leukaemia, a type of cancer that kills more than 300,000 people in the world every year. The evolution of the disease happens as we get older, but there is now evidence that cells in our body progress towards a malignant phenotype many years before they can be identified with current diagnostic techniques. This proposal will exploit mechanobiology, a field of research that has progressed in the last 10 years, as a novel method to interrogate very early changes in cellular state, bringing it closer to medical use by combining advanced biomaterials, novel microscopy techniques and robotics. Mechanobiology has taught us that cells can feel and react to their mechanical environment. For example, cancer cells are softer than normal cells. However, reorganisation of their niche causes increased tissue stiffness. Here, we will use mechanical stimulation to interrogate cells potential to become cancer cells. Cell response to these external mechanical stimuli will reveal their potential to evolve from health to disease. We will focus on leukaemia, a cancer that originates in the bone marrow, as normal haematopoietic stem cells, which play the essential role of making our blood, start a malignant transformation giving rise to leukemic stem cells. We have demonstrated that healthy cells and pre-malignant/malignant cells respond differently to mechanical stimulation. This project will develop an in vitro model of the bone marrow using soft hydrogels with defined mechanical and biochemical properties that host mesenchymal stem cells and hematopoietic (or leukemic) stem cells, as are found together in the marrow. We will investigate how external mechanical stimulation of the model using nanoscale vibration of controlled frequency and amplitude discriminate between healthy vs diseased systems. To monitor these mechanical changes in the in vitro model we will use Brillouin microscopy in a biological context. This technique is based on the propagation of acoustic waves in the system to characterise mechanical properties and will allow detailed mapping of stiffness of the bone marrow model as a function of time - importantly in a non-invasive way. Moreover, the level of mechanical stimulation will be dependent on the readout provided by Brillouin microscopy that will feed into a control system to alter the level of the mechanical vibrational stimulation imposed on the bone marrow model. We will develop the technology to have a robust on-chip system that includes the bone marrow model and integrates mechanical stimulation. We will use the technology in two clinical applications: (1) to assess whether the technology can predict leukaemia which can be induced as an off-target effect of the treatment (chemotherapy/radiotherapy) of solid tumours and (2) to assess whether the technology can predict malignant transformations in heaematopoeitic stem cells that happes with age, eventally leading to leukaemia.

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.

We often hear 'the system' is broken, but what do we mean by this? How can changing the way we think about, define, research, evidence, monitor, evaluate and resource 'the system' lead to meaningful change for deprived communities? How will this change benefit those who have first-hand experience of trauma, homelessness, poverty, unemployment, displacement, poor mental health or imprisonment? REALITIES takes a human-systems approach noting 'health and social care systems' (HSCS) are constructed mental representations of relationships existing in the world to promote health for people. Our Scottish consortium of 57 people has five established asset hubs in Clackmannanshire, Dundee, Easter Ross, Edinburgh and North Lanarkshire with strong relationships uniting conflicting ways of seeing the world. Through phase 2, we co-produced a systems-level model with deprived communities, policymakers, practitioners and researchers collecting and respecting different types of knowledge and alternative evidence-bases (from arts performances to nature walks; words to statistics) as equally important to understand complexities of unjust and avoidable health differences. Foundational funding evidenced REALITIES is able to transcend the challenge for our currently imagined HSCS. The medical model of disease shaping who and what is considered to be part of 'the health system' has brought benefits to human existence, though key actors within these place-based HSCS systems understand the limitations of this systems-framing for human flourishing. At present, they don't have a way to help reimagine them. REALITIES provides exploration and method for this reimagining. A model representing collective pathways producing creative routes for people to get the healthcare they need at the right time of their journeys by co-researching and co-creating with them the "what, whom, how, and why" - leading to successful connections between individuals with health and social needs and community-based opportunities for health and wellbeing improvement. We are a transdisciplinary collective of individuals with lived and felt experience of inequalities working alongside policymakers; local authorities; charities; artists; environmentalists and researchers from policy; health humanities; arts; psychology; human geography; environmental sociology; dentistry; medicine; statistics; economics; counselling; psychotherapy; management; medical anthropology; design and innovation. We will: understand what work is needed to enable places to reimagine and build 'systems' that create equitable health and wellbeing. explore and explain how links between creativity, relationships and nature create healthier and more resilient communities and environments for people in deprived areas. support creative, participatory processes, enabling communities to construct shared mental models (systems) using different ways of knowing (epistemologies) and perceiving reality (ontologies). combine different ways of knowing, enabling a more complete representation of bio-psycho-social-political factors which create 'health' and ways in which these are experienced by marginalised people. support communities to construct place-based versions of systems encompassing all aspects of health and wellbeing, and make purposeful changes in the nature of their relationships with each other and their environment. explore the usefulness of 'standard' Health Economic evaluation tools to assess Social Return of Investment, working with communities to re-conceptualise and re-define measures of 'value' and 'quality of life' in relation to human experience.