Our vision for this Transition Award is to leverage and combine key emerging technologies in Artificial Intelligence (AI) and Engineering Biology (EB) to enable and pioneer a new era of world-leading advances that will directly contribute to the objectives of the National Engineering Biology Programme. Realisation of the benefits of Engineering Biology technologies is predicated on our ability to increase our capability for predictive design and optimisation of engineered biosystems across different biological scales. Such a scaled approach to Engineering Biology would serve to significantly accelerate translation of scientific research and innovation into applications of wide commercial and societal impact. Synthetic Biology has developed rapidly over the past decade. We now have the core tools and capabilities required to modify and engineer living systems. However, our ability to predictably design new biological systems is still limited, due to the complexity, noise, and context dependence inherent to biology. To achieve the full capability of Engineering Biology, we require a change in capacity and scope. This requires lab automation to deliver high-throughput workflows. With this comes the challenge of managing and utilising the data-rich environment of biology that has emerged from recent advances in data collection capabilities, which include high-throughput genomics, transcriptomics, and metabolomics. However, such approaches produce datasets that are too large for direct human interpretation. There is thus a need to develop deep statistical learning and inference methods to uncover patterns and correlations within these data. On the other hand, steady improvements in computing power, combined with recent advances in data and computer sciences have fuelled a new era of Artificial Intelligence (AI)-driven methods and discoveries that are progressively permeating almost all sectors and industries. However, the type of data we can gather from biological systems does not match the requirements for off-the-shelf ML/AI methods and tools that are currently available. This calls for the development of new bespoke AI/ML methods adapted to the specific features of biological measurement data. AI approaches have the potential to both learn from complex data and, when coupled to appropriate systems design and engineering methods, to provide the predictive power required for reliable engineering of biological systems with desired functions. As the field develops, there is thus an opportunity to strategically focus on data-centric approaches and AI-enabled methods that are appropriate to the challenges and themes of the National Engineering Biology Programme. Closing the Design-Build-Test-Learn loop using AI to direct the "learn" and "design" phases will provide a radical intervention that fundamentally changes the way that we design, optimise and build biological systems. Through this AI-4-EB Transition Award we will build a network of inter-connected and inter-disciplinary researchers to both develop and apply next-generation AI technologies to biological problems. This will be achieved through a combination of leading-light inter-disciplinary pilot projects for application-driven research, meetings to build the scientific community, and sandpits supported by seed funding to generate novel ideas and new collaborations around AI approaches for real-world use. We will also develop an RRI strategy to address the complex issues arising at the confluence of these two critical and transformative technologies. Overall, AI-4-EB will provide the necessary step-change for the analysis of large and heterogeneous biological data sets, and for AI-based design and optimisation of biological systems with sufficient predictive power to accelerate Engineering Biology.

Today we use many objects not normally associated with computers or the internet. These include gas meters and lights in our homes, healthcare devices, water distribution systems and cars. Increasingly, such objects are digitally connected and some are transitioning from cellular network connections (M2M) to using the internet: e.g. smart meters and cars - ultimately self-driving cars may revolutionise transport. This trend is driven by numerous forces. The connection of objects and use of their data can cut costs (e.g. allowing remote control of processes) creates new business opportunities (e.g. tailored consumer offerings), and can lead to new services (e.g. keeping older people safe in their homes). This vision of interconnected physical objects is commonly referred to as the Internet of Things. The examples above not only illustrate the vast potential of such technology for economic and societal benefit, they also hint that such a vision comes with serious challenges and threats. For example, information from a smart meter can be used to infer when people are at home, and an autonomous car must make quick decisions of moral dimensions when faced with a child running across on a busy road. This means the Internet of Things needs to evolve in a trustworthy manner that individuals can understand and be comfortable with. It also suggests that the Internet of Things needs to be resilient against active attacks from organised crime, terror organisations or state-sponsored aggressors. Therefore, this project creates a Hub for research, development, and translation for the Internet of Things, focussing on privacy, ethics, trust, reliability, acceptability, and security/safety: PETRAS, (also suggesting rock-solid foundations) for the Internet of Things. The Hub will be designed and run as a 'social and technological platform'. It will bring together UK academic institutions that are recognised international research leaders in this area, with users and partners from various industrial sectors, government agencies, and NGOs such as charities, to get a thorough understanding of these issues in terms of the potentially conflicting interests of private individuals, companies, and political institutions; and to become a world-leading centre for research, development, and innovation in this problem space. Central to the Hub approach is the flexibility during the research programme to create projects that explore issues through impactful co-design with technical and social science experts and stakeholders, and to engage more widely with centres of excellence in the UK and overseas. Research themes will cut across all projects: Privacy and Trust; Safety and Security; Adoption and Acceptability; Standards, Governance, and Policy; and Harnessing Economic Value. Properly understanding the interaction of these themes is vital, and a great social, moral, and economic responsibility of the Hub in influencing tomorrow's Internet of Things. For example, a secure system that does not adequately respect privacy, or where there is the mere hint of such inadequacy, is unlikely to prove acceptable. Demonstrators, like wearable sensors in health care, will be used to explore and evaluate these research themes and their tension. New solutions are expected to come out of the majority of projects and demonstrators, many solutions will be generalisable to problems in other sectors, and all projects will produce valuable insights. A robust governance and management structure will ensure good management of the research portfolio, excellent user engagement and focussed coordination of impact from deliverables. The Hub will further draw on the expertise, networks, and on-going projects of its members to create a cross-disciplinary language for sharing problems and solutions across research domains, industrial sectors, and government departments. This common language will enhance the outreach, development, and training activities of the Hub.