he microbes that live within us influence our health and wellbeing. Research has shown changes in the microbiome are linked with inflammatory diseases, diabetes, obesity, cancer and mental health. They can also determine how effective treatments are and how well we recover from surgery. The microbiome has also become a multi-billion-dollar industry, with drug and diagnostic companies racing to capture the market for personalised medicine. Despite these advances, our understanding of the interactions between the host and the microbiota remain primitive as we do not have the tools to interrogate these systems in a quantitative manner. Here we outline a proposal to build a world- leading multi-disciplinary and multi-centre UK Institute for Microbiome Engineering. This institute will leverage cutting-edge engineering approaches to biology - systems and synthetic biology - to influence and modify the microbiota in well determined ways. This investment will have a huge and significant impact for the NHS over the next 30 years, influencing hospital treatment, home healthcare, disease prevention, mental health and wellbeing.

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.

Research at the intersection of biology and engineering has expanded our understanding of living systems and the many unique and valuable capabilities they possess. Scientists and engineers have now begun to harness this knowledge in new ways to address some of humanity's most pressing challenges. For example, using engineered biosystems we can create innovative healthcare solutions, enable more sustainable forms of agriculture, and support clean manufacturing methods. The emerging field of Engineering Biology aims to harness biology to build technologies for a healthy, sustainable, and equitable future. However, to date the lack of a rigorous biological engineering process has resulted in biosystems that are fragile, unpredictable, and difficult to scale when applied in real-world settings. Early pioneers in fields ranging from Aerospace to Information Technologies faced similar challenges when attempting to create robust and reliable systems. Such difficulties were oftentimes overcome using methods from systems and control engineering, which enabled rigorous approaches to the design, optimisation, and realisation of engineered systems, ultimately leading to dramatic economic growth and the creation of entirely new industries. To achieve an equivalent step-change in the engineering of reliable and robust biological systems, our programme will develop similar control and Artificial Intelligence systems in biotechnology - which we term feedback biocontrollers. These biocontrollers will be designed to operate within cells, between cells, and even to interact with non-biological entities (such as computers), thereby allowing researchers and innovators to efficiently and safely harness engineered biology in its many real-world applications. The robust engineering of biological control systems will be underpinned by the development of four "Engineering Pillars". These cover Theory (mathematical/AI approaches based on systems and control theory to model, design, analyse, and optimise biosystems), Software (computational tools able to translate this theory into conceptual designs), Wetware (experimental methods and biological parts to make designs a biological reality), and Hardware (to comprehensively test, scale-up, and deploy engineered biosystems). Each Pillar feeds directly into an integrated "Design-Build-Test-Learn" cycle rooted in systems and control engineering methods, which will accelerate academic and industrial development of new biotechnologies. Technologies developed in each Engineering Pillar will be integrated to address outstanding problems in three "Grand Challenge'' application domains: Biomedicine, Agriculture, and the Environment. Our team will work with industrial partners to generate world-leading solutions for each of these areas, demonstrating how biocontrollers can revolutionise scale-up and deployment of reliable engineered biotechnologies. The EEBio programme represents a timely investment in the new field of Engineering Biology which is set to play a defining role in the future of our society and the rapidly growing Bioeconomy. Our team of world-leading experts and up-and-coming early career researchers will create tools and technologies that are key to the effective engineering of biological systems - as observed in other, mature engineering fields - but which are not yet realised for Engineering Biology. EEBio brings together recent momentum across our team for rapid impact, while also supporting development of seminal ideas; in the near-term this will help address Grand Challenges we face today, while in the long-term it will provide the foundation for many bio-based solutions that will improve human life, agriculture, and the environment. Our work will accelerate responsible industrial exploitation, open up the field to other research communities (in the life, medical and social sciences), and support public confidence in the safety and reliability of Engineering Biology.