CAREER: Learning mechanistic models with automated experiments

职业：通过自动化实验学习机械模型

• 批准号: 2339026
• 负责人: Paul Jensen
• 金额: $ 137.1万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-02-01 至 2029-01-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2339026&HistoricalAwards=false
• 关键词: CAREER; Learning; mechanistic; models; automated

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.

尽管微生物组革命揭示了数千种新物种，但它也带来了挑战，因为与使用常规方法的微生物学家相比，以更快的速度确定了新物种。不知道这些新物种的作用，因此很难理解微生物组如何对生物系统的行为做出贡献。为了应对这一挑战，该提案开发了自主系统，这些系统将人工智能和自动化的湿法实验结合在一起，以建立这些理解的微生物的机械模型。此外，该提案旨在培训粗糙的机构的学生使用这些自主系统，并使这些学生能够在返回其家庭机构后继续进行这项研究。该提案基于PIS以前的工作开发Bacterai（AI-Drien驱动的机器人实验室），这是一个AI-Drien驱动的机器人实验室，该实验室在一个封闭的设计，执行和解释湿lab的封闭式问题中回答科学问题，而不用人类介入，而不是人类的介入 - 同样没有人进行介入。 Bacterai将通过三种方式增强。首先，自动化实验将与代谢模型结合使用，以发现基因组规模的调节网络，以理解细菌。其次，将建立一个建议系统，以选择实验，以在数千个环境中有效地映射基因型到表型。最后，将扩展到该机器人实验室的访问权限，将扩展到粗糙的机构的学生，以培训利用AI和自动化科学的新一代生物学家。这三个目标将加速微生物学，并阐明我们周围和周围生活的众多知识的微生物。该项目得到了分子和蜂窝生物科学划分的系统和合成生物学集群的支持。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响审查标准，认为值得通过评估来获得支持。