Introduction
Our growing ability to engineer biological systems is poised to radically disrupt how we make chemicals, grow food, treat disease, and ultimately support a more sustainable future. Modern engineering biology techniques can be used to reprogram cells, but major challenges remain in handling the complexity, scale and diversity of the data sets underpinning these efforts. New approaches are required to use biological data effectively for engineering. This interest group aims to act as a point of focus to develop and explore data-centric approaches that can unlock the practical engineering of life and overcome these current hurdles to harnessing engineered biology. The group will bring together computer scientists, mathematicians, engineers, biologists, chemists, and social scientists to address interdisciplinary research challenges in this area, build a diverse and inclusive community, and establish a national vision for how data science and AI can support engineering biology.
Explaining the science
Engineering Biology is the application of engineering principles to the design and construction of new biological systems such as microbes, plants and tissues. These systems can be employed for many applications in therapy, diagnostics, new materials, and sustainable manufacture of chemicals that are indispensable in our everyday lives. In 2021, Engineering Biology was listed by the Government as one of the seven key technologies of UK strength and development. This interest group aims at nucleating the world-leading UK knowhow in AI and Engineering Biology into a unified programme that supports the future growth of the field.
Aims
Advancing our ability to engineer biology is a strategic priority for the UK. Data-centric approaches are playing an increasingly important role in enabling the development of highly predictive models to accelerate biological design and engineering. However, many of the existing research programs in this area are disjoint, lacking a clear long-term vision and a community to support future growth and development. This interest group will position the Alan Turing Institute front and centre of this discipline and leverage its national role to support its future growth and development. The Institute will provide a physical location and access to deep expertise in data science and AI to develop the necessary theory and computational tools for engineering biology towards positive real-world impact. The core aims are:
- Community – Build a diverse and inclusive community that bridges the biological, social, mathematical, and computational sciences to find practical, data-centric solutions for the effective engineering of biology.
- Theory and tools – Develop new theory, computational tools, and biological methods to overcome existing challenges for data-centric approaches to engineering biology. We will focus on issues surrounding standardisation, the cost of data acquisition, how to integrate heterogeneous data types, the multi-scale nature of biological systems, interpretable methods, and trustworthiness/ethical considerations.
- Training – Offer training opportunities for community members to learn from each other and better understand the unique difficulties of using diverse, noisy, and often incomplete biological data for the practical engineering of living systems.
- Connections for impact – Foster new connections between researchers, funders, government, and industry to guide strategic policy decisions that ensure the leading UK position in data science and engineering biology is maintained, as well as our future technological readiness in this emerging intersection of fields.
Please visit our website for more information.
How to get involved
Talking points
- How can we leverage AI to enable engineering biology?
- Are new AI methods needed for engineering biology?
- What training and skills gaps must be addressed for the UK to fully benefit from synergies between AI and Engineering Biology?
Organisers
Dr Diego Oyarzun
Reader in Computational Biology, University of Edinburgh