21EBTA: EB-AI Consortium for Bioengineered Cells & Systems (AI-4-EB)

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.

For engineering biology (EB) to deliver its promised impact on healthcare, the environment, and sustainable growth, new methods that speed up the EB DBTL cycle and fully utilise experimental data are needed. This requires AI methods that are adapted to the types of problems, data, and biological scales encountered in EB. Doing so provides a paradigm shift in our ability to model and engineer such systems. Through AI-4-EB, we will focus on the development of AI/ML methods that (1) yield human-interpretable predictive models that can direct the automated design and optimisation of biological systems, and (2) are built to address the particularities of experimental synthetic biology data and workflows, i.e. heterogeneous data, with low signal-to-noise and sampling rate.

In particular, we will develop novel AI methods that allow us to learn across different biological scales, from genotypic information (DNA/RNA sequences) and phenotypic behaviours (P1); to rewiring regulatory and metabolic networks (P2); to controlling intricate interactions between cells for emergent properties (P3), as well as cellular communities (P4). As a core for the transition award, we have chosen four leading-light projects that tackle different aspects of the AI-EB integration, highlighting the opportunities and benefits of an integrated approach.

The leading-light projects will yield: (P1) graph-neural-network learning methods to predict eukaryotic gene expression in context, (P2) abductive-meta-interpretive learning to learn the rewiring rules of transcriptional networks for cellular optimisation, (P3) physics-informed neural networks to uncover robust patterning principles through AI-engineered Turing patterns, and (P4) automated statistical modelling approaches based on Gaussian processes to design robust synthetic communities. These will be augmented by additional projects awarded through sandpits and community building workshops.