The Scottish Manufacturing Institute aims to research technology for manufacture, addressing the requirements of European, UK and regional industries. It taps into the broad expanse of research at Heriot-Watt University to deliver innovative manufacturing technology solutions. The SMI delivers high quality research and education in innovative manufacturing technology for high value, lower volume, highly customised, and high IP content products that enable European and UK Manufacturers to compete in an environment of increased global competition, environmental concern, sustainability and regulation, where access to knowledge, skills and IP determine where manufacturing is located. Our mission is to deliver high impact research in innovative manufacturing technologies based on the multidisciplinary technology resource across Heriot-Watt University, the Edinburgh Research Partnership, the Scottish Universities Physics Alliance and beyond. The Institute is organised into three themes:- Digital Tools;- Photonics; and - MicrosystemsThe vision of the Digital Tools Theme is to provide tomorrow's engineers with tools that will help them to easily capture, locate, exploit and manipulate 3D information for mechanical products of all kinds using distributed, networked resources. Photonics has strong resonance with the needs of developed economies to compete in the 21st Century global market for manufacturing, providing: routes to low cost automated manufacture; and the key processes underpinning high added value products. We have a shared conviction that photonics technologies are an essential component of any credible strategy for knowledge-based industrial production. The Photonics Theme vision is for the SMI to be internationally recognised as the leading UK focus for industrially-relevant photonics R&D, delivering a mix of academic and commercial outputs in hardware, process technology and production applications.The principal strategy of the Microsystems Theme is to research into new integration and packaging solutions of MEMS that are low cost, mass manufacturable and easily adoptable by the industry. The vision is to become a European Centre of Excellence in MEMS integration and packaging over the next 5 years. We thus aspire to service UK manufacturing industry with innovative technology for high value, lower volume, highly customised, and high IP content products; and to help UK industry expand globally in an internationally competitive market.

Synthetic Biology (SynBio) is an emerging engineering discipline with an ambitious goal: empowering scientists with the ability to programme new functions into cells, just like we would do with computers. Despite a thriving community and notable successes, however, writing "functioning algorithms" for cells remains extremely time-consuming. This is a roadblock towards the engineering of mammalian cells, an area uniquely positioned to develop potentially groundbreaking therapeutic applications. This translates into high development costs that, in turn, are limiting the pace at which Synthetic Biology progresses towards applications. Model-Based System Engineering (MBSE) is the answer the engineering community found to similar problems and is widely used to streamline manufacturing. In this framework, mathematical models are used to screen candidate designs via simulations and bring to testing only the most promising solutions. Despite being an engineering discipline, SynBio lacks a MBSE framework. This is largely due to three connected issues: (a) the scarcity of accurate mathematical models of parts (e.g. promoters) in the first place. Such a shortage (b) makes it difficult to "reverse engineer" the connection between the DNA sequence and the kinetics of the transcribed mRNA (e.g. promoter sequence and leakiness of expresion). This means that (c) the inverse "re-design" problem, i.e. finding the optimal DNA sequence of a part, cannot be solved, let alone automatically. With this fellowship, I aim at filling this gap and develop a "Model-Based Biosystem Engineering" (MBBE) framework to automate the Design-Build-Test-Learn (DBTL) cycle in Synthetic Biology. Given their role in cell and gene therapy, with my team, we will focus on synthetic promoters for mammalian cells. Prompted by the recent successes and challenges of CAR T cells -immune cells engineered to kill cancer cells, we will use the framework to engineer a hypoxia-inducible promoter that optimises a set of criteria we will determine and prioritise with our collaborator Prof. Chen at UCLA. We will first focus on the development of the MBBE framework; to this aim we will tackle the three issues mentioned above by: (a) developing a high-throughput microfluidic device that allows to infer, with minimum experimental efforts (via Optimal Experimental Design), reliable mathematical models of hundreds of variants of a promoter, (b) using these results to automatically learn/predict gene expression dynamics from promoter sequence via machine learning and (c) combining this prediction scheme with computational optimisation to identify and refine promoter sequences so that they satisfy given specifications and maximise pre-determined objectives. To develop a hypoxia-inducible promoter, we will start from an initial pool of 600 sequences -designed to cover a fraction of the design space as big as possible, and we will iterate twice over our automatic DBTL loop to finally obtain promoter(s) that can be used to overcome the current limitations of CAR T cells. Besides automating the DBTL cycle, the approach I propose has three main benefits: it allows to obtain, and publicly share, reliable models (1) faster -as we will use Optimal Experimental Design methods to minimise experimental efforts, (2) cost-effectively -as microfluidics drastically reduces the use of reagents and automation renders human intervention unnecessary; (3) in a reproducible way -as all the data and the steps in the inference are tracked and immediately made publicly available.

The functional intricacy of the central nervous system (CNS) arises from the complex anatomical and dynamic interactions between different types of neurones involved in specific networks. Hence, the encoding of information in neural circuits occurs as a result of interactions between individual neurones as well as through the interplay within both microcircuits (made of few neurones) and large scale networks involving thousands to millions of cells. One of the great challenges of neuroscience nowadays is to understand how these neural networks are formed and how they operate. Such challenge can be resolved only through simultaneous recording from thousands of neurones that become active during specific neuronal tasks. One of the experimental approaches to fulfil this goal is to use multielectrode arrays (MEAs) that consist of several channels (electrodes) that can each record (and/or stimulate) from few adjacent neurones within a particular area of the CNS. MEAs can be used in vitro to record from dissociated neuronal cultures or from brain slices or isolated retinas. These MEAs consist of assemblies of electrodes embedded in planar substrates. Typical commercial MEAs consist of 60-128 electrodes with a spacing of 100-200 um. Considering that a generic neurone in the mammalian CNS has a diameter of about 10 um, it is obvious that such MEAs cannot convey information on the activity of all neurones involved in a specific network, but rather just from a sample of these cells. To overcome this activity under-sampling, in this project, we will use the Active Pixel Sensor (APS) MEA, a novel type of MEA platform developed in a NEST-EU Project by our collaborator Luca Berdondini (Italian Institute of Technology, Genova). This MEA consists of 4,096 electrodes with near cellular resolution (21x21 um, 42 um centre-to-centre separation, covering an active area of 2.5 mm x 2.5 mm), where recording is possible from all channels at the same time. We will use the APS MEA to record spontaneous waves of activity that are present in the neonatal vertebrate retina. These waves occur during a short period of development during perinatal weeks and they are known to play an important role in guiding the precise wiring of neural connections in the visual system, both at the retinal and extra-retinal levels. The APS-MEA, thanks to its unmet size and resolution, will enable us to reach new insights into the precise dynamics of these waves as never achieved before. Recordings from such large scale networks at near cellular resolution generate extremely rich datasets with the drawback that these datasets are very large and difficult to handle, thus necessitating the development of new powerful analytical tools enabling to decode in a fast, efficient and user-friendly way how cellular elements interact in the network. The development of such computational tools is the central goal of this project, while the experimental work on the retina defines a challenging and unique scientific context. The tools we plan to develop will yield parameters that will help us reach better understanding of network function, from the temporal firing patterns of individual neurones to how activity precisely propagates within the network. We will also develop novel tools for easier visualisation of the dynamical behaviour of the activity within the network. These tools will be developed in a language that could be easily utilized by other investigators using the same recording system or other platforms of their choice. Finally, to ensure that these tools are accessible to the wide neurophysiology community, they will be deployed on CARMEN (Code Analysis, Repository and Modelling for e-Neuroscience), a new internet-based neurophysiology sharing resource designed for facilitating worldwide communication between collaborating neurophysiologists.

Controllable Large Displacement Continuum Surfaces, LCDS, hold the potential for application across a diverse range of applications including the highly dexterous manipulation of parts in manufacturing environments, soft/flexible exoskeleton systems in healthcare, and jointless surface control in the aerospace, automotive, energy and food processing industries. Another application with immediate benefit across multiple industries would be to replace conventional mould surfaces used in the development of bespoke carbon fibre components with a single, reconfigurable surface capable of forming on-demand to desired mould profiles from digital files. Currently low volume production line moulds are produced through expensive (hand carved, milling, turning, and more recently 3D printing) methods that can account for upwards of 20% of a component's manufacturing cost. The use of LCDS systems to form on-demand mould shapes for low volume parts would result in massive savings to the production of such components. The problem is that to date LDCS operate in 'open loop' with little or no sensor feedback capability to maintain desired curvature under changing conditions, or consideration as to how external forces might best be accounted for. Additionally, placement of actuation elements on the surface to achieve complex profiles is largely accomplished through user intuition and experience, limiting efficiency at the design stage. This results in 'trial and error' methods to LDCS design and control that increase production costs and reduce surface performance under operation. To move beyond 'trial by error' design and control of LDCS undergoing large elastic deformations, accurate, yet computationally efficient, methodologies to model and simulate in both the kinematic and dynamic domains are required. This project will advance the use of LDCS into the next realm by providing the tools necessary to enable robust procedures for their design and control based not on 'trial and error', but physical model information within an accurate and efficient structure. This will not only make direct, meaningful contributions to the use of LDCS in carbon fibre production. But open further applications of LDCS to areas such as the highly dexterous manipulation of parts in manufacturing environments, soft/flexible exoskeleton systems in healthcare, and deformable surface control in the aerospace, automotive, energy and food processing industries.

The vision for this Programme is to deliver the step changes in Robotics and Autonomous Systems (RAS) capability that are necessary to overcome crucial challenges facing the nuclear industry in the coming decades. The RAS challenges faced in the nuclear industry are extremely demanding and complex. Many nuclear installations, particularly the legacy facilities, present highly unstructured and uncertain environments. Additionally, these "high consequence" environments may contain radiological, chemical, thermal and other hazards. To minimise risks of contamination and radiological shine paths, many nuclear facilities have very small access ports (150 mm - 250 mm diameter), which prevent large robotic systems being deployed. Smaller robots have inherent limitations with power, sensing, communications and processing power, which remain unsolved. Thick concrete walls mean that communication bandwidths may be severely limited, necessitating increased levels of autonomy. Grasping and manipulation challenges, and the associated computer vision and perception challenges are profound; a huge variety of legacy waste materials must be sorted, segregated, and often also disrupted (cut or sheared). Some materials, such as plastic sheeting, contaminated suits/gloves/respirators, ropes, chains can be deformed and often present as chaotic self-occluding piles. Even known rigid objects (e.g. fuel rod casings) may present as partially visible or fragmented. Trivial tasks are complicated by the fact that the material properties of the waste, the dose rates and the layout of the facility within which the waste is stored may all be uncertain. It is therefore vital that any robotic solution be capable of robustly responding to uncertainties. The problems are compounded further by contamination risks, which typically mean that once deployed, human interaction with the robot will be limited at best, autonomy and fault tolerance are therefore important. The need for RAS in the nuclear industry is spread across the entire fuel cycle: reactor operations; new build reactors; decommissioning and waste storage and this Programme will address generic problems across all these areas. It is anticipated that the research will have a significant impact on many other areas of robotics: space, sub-sea, mining, bomb-disposal and health care, for example and cross sector initiatives will be pursued to ensure that there is a two-way transfer of knowledge and technology between these sectors, which have many challenges in common with the nuclear industry. The work will build on the robotics and nuclear engineering expertise available within the three academic organisations, who are each involved in cutting-edge, internationally leading research in relevant areas. This expertise will be complemented by the industrial and technology transfer experience and expertise of the National Nuclear Laboratory who have a proven track record of successfully delivering innovation in to the nuclear industry. The partners in the Programme will work jointly to develop new RAS related technologies (hardware and software), with delivery of nuclear focused demonstrators that will illustrate the successful outcomes of the Programme. Thus we will provide the nuclear supply chain and end-users with the confidence to apply RAS in the nuclear sector. To develop RAS technology that is suitable for the nuclear industry, it is essential that the partners work closely with the nuclear supply chain. To achieve this, the Programme will be based in west Cumbria, the centre of much of the UK's nuclear industry. Working with researchers at the home campuses of the academic institutions, the Programme will create a clear pipeline that propels early stage research from TRL 1 through to industrially relevant technology at TRL 3/4. Utilising the established mechanisms already available in west Cumbria, this technology can then be taken through to TRL 9 and commercial deployment.