CAREER: Learning mechanistic models with automated experiments

虽然微生物组革命揭示了数千个新物种，但它也提出了一个挑战，因为新物种的识别速度比微生物学家使用传统方法所能表征的速度更快。如果不知道这些新物种的作用，就很难理解微生物组如何影响生物系统的行为。为了应对这一挑战，该提案开发了自主系统，该系统将联合收割机人工智能和自动化湿实验相结合，以构建这些未充分研究的微生物的机械模型。此外，该提案还旨在培训农村服务机构的学生使用这些自主系统，并使这些学生能够在返回家乡机构后继续这项研究。该提案建立在PI之前开发BacterAI的工作基础上，BacterAI是一种人工智能驱动的机器人实验室，可以在设计，执行和解释湿实验室实验的闭环中回答科学问题-所有这些都没有人为干预。BacterAI将在三个方面得到增强。首先，自动化实验将与代谢模型相结合，以揭示未充分研究的细菌的基因组规模的调控网络。第二，将建立一个推荐系统，以选择实验，在数千个环境中有效地映射基因型到表型。最后，这个机器人实验室将扩大到农村服务机构的学生，以培养利用人工智能和自动化科学的新一代生物学家。所有这三个目标将加速微生物学的发展，并阐明生活在我们身上和周围的众多未被充分研究的微生物。该项目由分子和细胞生物科学部的系统和合成生物学集群支持。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

While the microbiome revolution revealed thousands of new species, it also presents a challenge as new species are identified at a faster pace than can be characterized by microbiologists using conventional methods. Without knowing what these new species do, understanding how a microbiome contributes to the behavior of biological systems is difficult. To address this challenge, this proposal develops autonomous systems that combine artificial intelligence and automated wet experiments to build mechanistic models of these understudied microbes. In addition, the proposal seeks to train students at rural-serving institutions to use these autonomous systems and to enable these students to continue this research upon return to their home institution.The proposal builds on the PIs previous work in developing BacterAI, an AI-driven robotic laboratory that answers scientific questions in a closed loop of designing, executing, and interpreting wet-lab experiments – all without human intervention. BacterAI will be enhanced in three ways. First, automated experiments will be combined with metabolic models to uncover the genome-scale regulatory networks for understudied bacteria. Second, a recommender system will be built to select experiments for efficient mapping genotype-to-phenotype across thousands of environments. Finally, access to this robotic laboratory will be extended to students at rural-serving institutions to train a new generation of biologists that leverage AI and automated science. All three of these goals will accelerate microbiology and illuminate the numerous understudied microbes that live on and around us. This project is supported by the Systems and Synthetic Biology Cluster of the Division of Molecular and Cellular Biosciences.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.