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

我们对这个过渡奖的愿景是利用和联合收割机在人工智能（AI）和工程生物学（EB）的关键新兴技术，使和开拓世界领先的进步，将直接有助于国家工程生物学计划的目标的新时代。工程生物学技术的好处的实现取决于我们在不同生物尺度上提高工程生物系统的预测设计和优化能力的能力。这种规模化的工程生物学方法将大大加快科学研究和创新转化为具有广泛商业和社会影响的应用。合成生物学在过去十年中发展迅速。我们现在拥有了改造和设计生命系统所需的核心工具和能力。然而，由于生物学固有的复杂性、噪声和背景依赖性，我们可预测地设计新生物系统的能力仍然有限。为了实现工程生物学的全部能力，我们需要改变能力和范围。这需要实验室自动化来提供高通量的工作流程。随之而来的是管理和利用生物学数据丰富的环境的挑战，这些环境是从数据收集能力的最新进展中出现的，包括高通量基因组学，转录组学和代谢组学。然而，这样的方法产生的数据集对于人类的直接解释来说太大了。因此，需要开发深度统计学习和推理方法，以揭示这些数据中的模式和相关性。另一方面，计算能力的稳步提高，加上数据和计算机科学的最新进展，推动了人工智能（AI）驱动的方法和发现的新时代，这些方法和发现正在逐步渗透到几乎所有部门和行业。然而，我们可以从生物系统中收集的数据类型并不符合目前可用的现成ML/AI方法和工具的要求。这需要开发新的定制AI/ML方法，以适应生物测量数据的特定特征。人工智能方法有可能从复杂的数据中学习，并且当与适当的系统设计和工程方法相结合时，可以提供具有所需功能的生物系统的可靠工程所需的预测能力。因此，随着该领域的发展，有机会在战略上专注于以数据为中心的方法和人工智能方法，这些方法适合于国家工程生物学计划的挑战和主题。通过AI-4-EB过渡奖，我们将建立一个相互联系和跨学科的研究人员网络，以开发和应用下一代人工智能技术来解决生物学问题。通过AI-4-EB过渡奖，我们将从根本上改变我们设计、优化和构建生物系统的方式。这将通过应用驱动研究的领先的跨学科试点项目，建立科学界的会议，以及由种子资金支持的沙坑来实现，以围绕人工智能方法产生新的想法和新的合作，以供现实世界使用。我们还将制定RRI战略，以解决这两项关键和变革性技术融合时出现的复杂问题。总的来说，AI-4-EB将为大型和异质生物数据集的分析提供必要的步骤变化，并为基于AI的生物系统设计和优化提供足够的预测能力，以加速工程生物学。

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.