Collaborative Research: GCR: Accelerated Discovery of Synthetic Biological Materials

合作研究：GCR：加速发现合成生物材料

• 批准号: 2219191
• 负责人: Kelly Werder
• 金额: $ 16.13万
• 依托单位: University of South Florida
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2027-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2219191&HistoricalAwards=false
• 关键词: Collaborative; Research; GCR; Accelerated; Discovery

This project aims to develop a pathway to transform the economy from petroleum derived plastics to bioplastics made of synthetic biological materials (SBM) manufactured by bacteria and derived from sustainable sources. A convergent team comprised of experts from synthetic biology, biotechnology, machine learning, social sciences, materials science, and mechanics will work on developing commercially viable, high-performance plastic fibers made of SBMs with the goal of building a pipeline that will both produce sustainable high-performance SBMs of the future and help train a diverse workforce for the future.The research activities focus on addressing key challenges by developing: (1) machine-learning tools to explore potential DNA sequences that can be programmed into bacteria for SBM biosynthesis, reducing the size the design and search space; (2) a transformative discovery pipeline for rapid identification of high-performance SBMs by integrating machine learning tools with first-principle simulations and experimentation that have varying cost and fidelity; and (3) an original economics and sociological framework linked to techno-economic, life-cycle, and supply-chain models to formulate strategies to steer SBMs to market adoption.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.

该项目旨在开发一种途径，将经济从石油衍生塑料转变为由细菌制造的合成生物材料（SBM）制成的生物塑料，并来自可持续来源。一个由合成生物学、生物技术、机器学习、社会科学、材料科学和力学专家组成的融合团队将致力于开发商业上可行的，由SBMs制成的高性能塑料纤维，目标是建立一条既能生产可持续的高性能未来的性能SBM，并帮助培养未来的多元化劳动力。研究活动的重点是通过开发解决关键挑战：（1）机器学习工具，用于探索可以编程到细菌中用于SBM生物合成的潜在DNA序列，从而减小设计和搜索空间的大小;（2）通过将机器学习工具与具有不同成本和保真度的第一原理模拟和实验相结合，用于快速识别高性能SBM的变革性发现管道;以及（3）与技术经济，生命周期，并供应─该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的评估来支持。影响审查标准。