The ability to genetically engineer insects of medical and agricultural importance has opened the possibility of deliberately introducing genetic traits into insect populations as a way to alter their ability to either reproduce, to cause crop damage or to vector pathogens that cause disease. However, one thing is identifying the genetic trait that one would like to introduce into a modified insect; it is another thing completely to get that introduced trait to spread into a population. The reason this is difficult is that the added genetic trait usually does not improve the evolutionary fitness of those insects that harbour it, meaning that its representation in the population is unlikely to increase generation upon generation. In fact in some cases the genetic trait is designed to have a strong negative fitness effect on the population. In either of these scenarios this means that huge numbers, usually tens of millions and far in excess of the numbers in the local target population, need to be released in order to have an appreciable effect on the population. This is expensive and logistically challenging. Moreover, the effect lasts only as long as one can continue to release such numbers. Recent innovations in genetic control, such as 'gene drive', get round this problem by ensuring that there is a biased inheritance of the modification each generation, meaning that its frequency in the population can increase relatively rapidly. These types of approaches hold much promise because they are self-sustaining - only a few insects need to be released to have a long term effect - and they are species-specific because the traits are passed on by mating between insects of the same species. Many of these gene drive designs use genome editing tools such as CRISPR as their 'molecular motor' that works to bias the inheritance of the gene drive element among the sperm or eggs that an insect makes and contributes to the next generation. Making small changes to the duration and/or timing of the CRISPR element in the gene drive can drastically affect its performance in how likely it is to be inherited - limiting its expression only to the germline cells where it needs to be active can cause huge improvements in the fitness of insects carrying the drive element and therefore can increase its likelihood of penetrating a target population. Similarly, many gene drives contain also a genetic 'cargo', designed to produce some intended effect in insects carrying it - for example, activation of innate immune system against a pathogen or the production of proteins that interfere with parasite replication - and expression of these effects in insects, or tissues therein, not infected by the pathogen can be very costly. In both cases then, an ability to fine tune expression within the insect, in both time and space, can have a large effect in improving the efficacy. What we are proposing here is to: 1) dissect the process of sperm and egg formation in the ovary and testis, to the single cell level, and extract information on the DNA sequence of the genetic switches in the genome that control expression in the relevant cells necessary to ensure biased inheritance of the gene drive. We will then test these new switches to see if they improve the gene drive performance; 2) We will provide an additional level of exquisite specificity to the expression of the gene drive and/or its cargo by ensuring that each is only active in response to signals - such as RNA from the pathogen - that faithfully signal that expression should occur in that cell type. These RNA-based 'riboswitches' are very novel and proof of their ability to work in this system would have far reaching importance, not just in insect control but in improving the utility and specificity of genome editing in a range of applications including healthcare applications such as in vivo genome editing and CRISPR-based diagnostic assays.

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.

Synthetic Biology is the underpinning discipline for advances in the UK bioeconomy, a sector currently worth ~£200Bn GVA globally. It is a technology base that is revolutionising methods of working in the biotechnology sector and has been the subject of important Government Roadmaps and supported by significant UKRI investments through the Synthetic Biology for Growth programme. This is now leading to a vibrant translational landscape with many start-ups taking advantage of the rapidly evolving technology landscape and traditional industries seeking to embed new working practices. We have sought evidence from key industry leaders within the emerging technology space and received a clear and consistent response that there is a significant deficit of suitably trained PhDs that can bridge the gap between biological understanding and data science. Our vision is a CDT with an integrative training programme that covers experimentation, coding, data science and entrepreneurship applied to the design, realisation and optimisation of novel biological systems for diverse applications: BioDesign Engineers. It directly addresses the priority area 'Engineering for the Bioeconomy' and has the potential to underpin growth across many sectors of the bioeconomy including pharmaceutical, healthcare, chemical, energy, and food. This CDT will bring together three world-leading academic institutions, Imperial College London (Imperial), University of Manchester (UoM) and University College London (UCL) with a wide portfolio of industrial partners to create an integrated approach to training the next generation of visionary BioDesign Engineers. Our CDT will focus on providing an optimal training environment together with a rigorous interdisciplinary program of cohort-based training and research, so that students are equipped to address complex questions at the cutting edge of the field. It will provide the highly-skilled workforce required by this emerging industry and establish a network of future UK Bioindustry leaders. The joint location of the CDT in London and Manchester will provide a strong dynamic link between the SE England biotech cluster and the Northern Powerhouse. Our vision, which brings together a BioDesign perspective with Engineering expertise, can only be delivered by an outstanding and proven grouping of internationally renowned researchers. We have a supervisor pool of 66 world class researchers that span the associated disciplines and have a demonstrated commitment to interdisciplinary research and training. Furthermore, students will work directly with the London and Manchester DNA Foundries, embedding the next generation bioscience technologies and automation in their training and working practices. Cohort training will be delivered through a common first year MRes at Imperial College London, with students following a 3-month taught programme and a 9-month research project at one of the 3 participating institutions. Cohort and industry stakeholder engagement will be ensured through bespoke training and CDT activities that will take place every 6 months during the entire 4-year span of the programme and include multi-year group hackathons, training in responsible research and innovation, PhD research symposia, industry research days, and entrepreneurial skills training. Through this ambitious cohort-based training, we will deliver PhD-level BioDesign Engineers that can bridge the gap between rigorous engineering, efficient model-based design, in-depth cellular and biomolecular knowledge, high throughput automation and data science for the realisation and exploitation of engineered biological systems. This unique cohort-based training platform will create the next generation of visionaries and leaders needed to accelerate growth of the UK bioeconomy.

Chemical biology is spearheading the development & translation of novel molecular tools and technologies to study biology and develop biomedical understanding. Dovetailing these platforms with industry 4.0/5.0 breakthroughs in automation & robotics, artificial intelligence & machine learning, the CDT will unlock the Lab of the Future paradigm. This will redefine the state of the art with respect to making, measuring, modelling & manipulating molecular interactions in biological systems, leading to novel R&D workflows, promoting efficient design-test cycles and driving sustainability. These molecular technologies will (i) enable biological & medical research, (ii) revolutionise understanding of disease & (iii) create novel diagnostics, drugs & therapies, focusing increasingly on individual patient outcomes. They will also impact the agri-tech sector which faces huge demand to increase productivity by unlocking strategies to e.g. track agrochemicals in plants/soil, understand modes of action & drive precision farming. Similarly, advances in personal care industrial processes are critically dependent on development of molecular technologies to gain insight into structured product design. The application of novel molecular tools/technologies, Lab of the Future strategies & their commercialisation through the instrumentation science sector is thus critical to the UK economy, supporting >4,500 healthcare, personal care, agri-science & biotech companies. This will transform (i) therapeutic, agrochemical & personal care product discovery (ii) med-tech/biotech/healthcare instrumentation R&D pipelines & (iii) stimulate creation of SMEs. Working closely with civic partners including Hammersmith & Fulham Council and the NHS, the CDT's talent & research pipeline will act as a growth engine for one of the most rapidly expanding Life Science ecosystems in Europe, the White City Innovation District. Given the importance of Chemical Biology to UK plc there is great demand but short supply of Chemical Biology PhD graduates able to match the pace of innovation across the physical/life science interface, at a time when industry & health sectors need these skills to accelerate productivity. The CDT in Chemical Biology: Empowering UK BioTech innovation with its unique 5 year programme: 1 year MRes + 3 year PhD + 1 year ELEVATE Fellowship directly addresses this skills gap by training a new generation of career-ready graduates, able to embrace the Lab of the Future concept and unlock its potential by fusing innovative molecular tools & tech with industry 4.0 & 5.0 advances to study molecular interactions & develop applications in the life science, agriscience & personal care sectors. CDT students will benefit from a research and training programme created with >100 industry/external stakeholders designed to meet future employer's needs. Our cohort-based programme with EDI at its heart, will allow students to contextualise their work within wider CDT activities & find novel solutions to their research, supported by one of the world's largest Chemical Biology communities: the Institute of Chemical Biology (>165) research groups. Students will be trained in multidisciplinary blue skies/translational research, lean innovation, scale fast/fail fast approaches, creating scientists able to understand molecular technologies, sustainable product design, early-stage commercialisation, & industry's pace of change. To support this, our training includes Future Lab & HackEDU courses (prototyping training), a drug screening programme, Biz-Catalyst (entrepreneurial training), InnovaLab (SME accelerator), a Data Science course, Human Centred Design, Science Communication (with BBC) & Bioethics/RRI/Sustainability/Policy courses. Following PhD completion, students can enter the ELEVATE fellowship programme, bridging the gap between PhD & industry/academia, offering training, personalised workplace opportunities & enable students to kickstart new companies.