SusChEM: Metabolic Analysis and Optimization of Biofuel Production during light-dark cycles in Synechocystis

PI：Peeble，Christian建议编号：1336236机构：科罗拉多州立大学标题：SusChEM：聚胞体中光暗循环中生物燃料生产的代谢分析和优化本项目的总体目标是开发所需的计算工具，以指导蓝藻中用于生物燃料生产的代谢工程工作。蓝藻经历了一个由昼夜节律驱动的动态细胞环境。这种环境对蓝藻生物燃料生产的影响还知之甚少，本项目旨在通过计算和实验技术更好地了解这种变化的细胞环境。蓝藻因其利用太阳能和固定大气碳的能力而成为一个有吸引力的生产平台。此外，它们表现出快速的倍增时间，并拥有成熟的分子生物学工具箱。与它们的异养同行不同，很少有人专注于开发光合作用有机体中全球新陈代谢的代谢模型，以准确描述在光：暗周期中经历的动态。这些生物体表现出独特的细胞条件和挑战，可以极大地改变所产生的分子的总体滴度。在光合作用平台的光照条件下，氧化还原电位和循环电子流条件更有利于异源燃料的产生。该项目计划通过开发代谢的动态通量平衡分析(FBA)模型来解决这些不足之处，该模型捕捉在光：暗周期下经历的变化动力学。这个动态的FBA模型将利用包括代谢组学、转录组学和蛋白质组学在内的实验方法来生成。这将是第一个为光合作用有机体开发的动态FBA模型。然后，这个模型将被用来产生可检验的假说，以确定将提高生物燃料生产率的关键基因修改。这项工作将有助于全面了解光合作用有机体中的细胞代谢，并可应用于其他感兴趣的光合作用有机体。这些研究产生的菌株将直接导致光合作用先进的生物燃料生产，具有经济和环境可持续的潜力。该菌株将作为未来生物燃料升级战略和生化生产战略研究的平台。该项目将为本科生和研究生提供跨学科培训，并正在作出重大努力，将代表性不足群体的成员包括在内。学生将获得新陈代谢的数学模型、代谢工程、合成生物学和光合作用生物的微生物细胞培养方面的经验。这些经验将使学生处于合成生物学和光合作用有机体生物燃料生产研究的前沿。该项目还将为K-12学生的研究提供指导机会。

PI: Peebles, ChristieProposal Number: 1336236Institution: Colorado State UniversityTitle: SusChEM: Metabolic Analysis and Optimization of Biofuel Production during light-dark cycles in SynechocystisThe overall goal of this project is to develop the computational tools needed to guide metabolic engineering efforts in cyanobacteria for biofuel production. Cyanobacteria experience a dynamic cellular environment that is driven by circadian rhythms. The impacts of this environment on biofuel production in cyanobacteria are poorly understood, and this projects aims to better understand this changing cellular environment through computational and experimental techniques.Cyanobacteria are an attractive production platform due to their ability to utilize solar energy and fix atmospheric carbon. In addition, they exhibit rapid doubling times and have an established molecular biology toolbox. Unlike their heterotrophic counterparts, little work has focused on developing metabolic models of global metabolism in photosynthetic organisms that accurately describe the dynamics experienced during light:dark cycles. These organisms exhibit unique cellular conditions and challenges that can substantially alter overall titers of molecules produced. During light conditions in a photosynthetic platform, redox potential and cyclic electron flow conditions are much more favorable for heterologous fuel production. This project plans to address these deficiencies by developing a dynamic flux balance analysis (FBA) model of metabolism that captures the changing dynamics experienced under light:dark cycles. This dynamic FBA model will be generated utilizing experimental methods that include metabolomics, transcriptomics, and proteomics. This will be the first dynamic FBA model developed for a photosynthetic organism. This model will then be utilized to generate testable hypothesis to identify key genetic modifications that will increase biofuel productivity. This work will contribute to an overall understanding of cellular metabolism in a photosynthetic organism and can be applied to other photosynthetic organisms of interest.The strain created from these studies will lead directly to photosynthetic advanced biofuel production with the potential to be economically and environmentally sustainable. This strain will serve as a platform for future research on biofuel upgrading strategies and on biochemical production strategies. The project will provide interdisciplinary training for undergraduate and graduate students, with significant efforts being made to include members of underrepresented groups. Students will gain experience in mathematical modeling of metabolism, metabolic engineering, synthetic biology, and microbial cell cultivation of photosynthetic organisms. These experiences will place the students at the forefront of research in synthetic biology and biofuel production in photosynthetic organisms. The project will also provide opportunities for mentoring of K-12 students in research.