About AI-4-EB

Artificial Intelligence (AI) and Engineering Biology (EB) are transformative technologies that are having a profound impact on our world. The intersection of these two great technologies is an area of rapidly growing interest with huge potential to address global challenges in sustainable bio-based production and healthcare.

Our vision 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 EB Programme.

Realising the benefits of EB technologies is predicated on increasing our capability for predictive design and
optimisation of engineered biosystems across different EB scales. This will significantly accelerate translation of research and innovation into applications of wide commercial and societal impact.

A new UK-wide consortium at the intersection of AI and EB has been launched by Imperial’s Centre for Synthetic Biology, working with UCL and the University of Manchester.  Led by Prof Guy-Bart Stan and the AI-4-EB management committee, this UKRI consortium is a collaboration with seven industrial partners and ten academic institutions, and the consortium brings together a diverse cohort of researchers in AI, Synthetic Biology, Biomathematics, Social Sciences, and innovative companies to enable capability, community and capacity development at the confluence of AI and EB.

The extensive network of stakeholders, research teams and industry partners allows in the AI-4-EB consortium allows the AI-4-EB to harness the combined power of both AI and EB to pioneer a new wave of biotechnical development that will accelerate our ability to address important global challenges in sustainable production and health. This will ultimately, pave the way towards the creation of a cluster of new world-leading activities for AI-guided engineering of biological systems.

Structure of the AI-4-EB Consortium

1. Supporting leading-light projects with a focus on AI development for biological systems and integration of advanced automation technologies
2. Building a new community that integrates the AI and EB disciplines, by understanding the positions, directions, and challenges in each area to identify new opportunities and the emergent key challenges associated with them
3. Through flexible seed-funding and sandpit events, the AI-4-EB supports new work that addresses unmet
   needs, i.e. new scientific, applied, or RRI questions, or key challenges identified at the workshops,
   such as heterogeneous data integration, and AI-driven context-aware predictive modelling
4. Developing an RRI strategy to address the complex issues arising at the confluence of these two critical transformative technologies