CAREER: Systems biology and engineering of Clostridium beijerinckii for enhance butanol production

这项职业研究旨在将系统生物学和生物燃料的研究与系统生物学跨学科课程的创建结合起来。本课程将面向高年级本科生和研究生，并将与全校范围内的努力相结合，以创建一个新的生物能源专业硕士课程和一个正在进行的生物信息学课程。这项研究背后的动机是双重的：一个是工程，一个是科学。工程目标是重新设计北耶林氏梭菌的代谢网络，以提高丁醇的生产，丁醇对于其作为化学原料的使用和作为第二代生物燃料的巨大潜力非常重要。科学目标是开拓新方法，将系统生物学应用于特征较少的生物，如C. beijerinckii，以建立模型，将基因型与表型关系充分详细地联系起来，以增强和控制特定感兴趣的特性-这是许多越来越多的测序生物面临的一个非常普遍的挑战。具体研究目标为：1)重建并实验验证首个可计算的贝氏梭菌基因组尺度代谢网络；测试模型预测的验证实验将包括对野生型和选定敲除菌株在各种底物上的生长速率、摄取速率和分泌速率进行量化；2)进行代谢组学实验，利用数据加强代谢网络重建，监测各发酵过程中代谢的变化；3)构建基于matlab的重构模块，帮助其他团队更快地生成其他代谢模型，在可能的情况下实现过程自动化，并为那些仍需要手工管理的方面提供组织工具；这个工具也将作为将要开发的课程的教学辅助工具（见下文）；4)利用计算模型指导贝氏弧菌提高丁醇产量的实验构建。具体的教育目标是：1)通过系统生物学课程的开发和积极的研究指导来指导本科生；2)通过系统生物学研究生课程和与生物能源研究相关的系统生物学相关课程，以及通过研究指导指导研究生；3)指导伊利诺伊大学第一个国际基因工程机器（iGEM）本科生团队，为大量系统和合成生物学本科生提供广泛的研究经验；4)开发一个短期的模型引导蜂窝工程课程，在国际上教授，从中国和韩国开始，以及在美国。智力优势：这个提议的项目代表了对基因型与表型关系进行足够详细建模的努力，以允许合理的设计来指导合成生物学。它还利用了一种新兴的技术和数据源——代谢组学，并将其与定量建模集成在一起，定量建模是利用这些数据的信息内容所必需的。从高通量数据源生成预测网络模型的系统生物学方法的发展是利用基因组学的力量来完成工程目标的一项非常重要的活动。更广泛的影响：开发生产纤维素丁醇的有效方法，解决了与美国影响很大的领域相关的问题，包括减少我们对外国石油的依赖，并产生有利于环境的碳中性燃料循环。根据需要，拟议的工作还将与更全面的实验计划（包括工业伙伴关系）相结合，以将研究成果转化为实际应用。相关的教育目标对于培养对定量分析和生物学有深刻理解的新一代科学家和工程师至关重要，因为这是推动21世纪生物学和新兴生物能源产业所需要的。

0846964PriceThis CAREER research aims to integrate research in systems biology and biofuels generation with the creation of an interdisciplinary course in systems biology. This course will be addressed to both advanced undergraduates and to graduate students, and will be integrated with campus-wide efforts to create a new Professional Master's Program in BioEnergy and an ongoing program in Bioinformatics. The motivations behind the research are two-fold: one engineering and one scientific. The engineering goal is to re-engineer the metabolic network of Clostridium beijerinckii for enhanced production of butanol, important for its use as a chemical feedstock and for its high potential as a second generation biofuel. The scientific objective is to pioneer novel approaches to using systems biology in lesser-characterized organisms, such as C. beijerinckii, to establish models linking the genotype to phenotype relationship in sufficient detail to enhance and control a property of specific interest - a very common challenge for many of the growing number of sequenced organisms.The specific research aims are: 1) Reconstruct and experimentally validate the first computable genome-scale metabolic network of Clostridium beijerinckii; validation experiments to test model predictions will include quantifying growth rates, uptake rates, and secretion rates on various substrates both of the wildtype and of selected knockout strains; 2) Perform metabolomics experiments and use the data to enhance the metabolic network reconstruction and to monitor changes in metabolism through various fermentation processes; 3) Build a Matlab-based reconstruction module to aid other groups to more rapidly generate other metabolic models, automating the process where possible, and providing organization tools for those aspects still requiring a final step of manual curation; this tool will also serve as a teaching aid in the course that will be developed (see below); and 4) Use the computational model to guide the experimental construction of strains of C. beijerinckii for enhanced butanol production. The specific educational aims are: 1) Mentor undergraduate students through the development of a systems biology course and active research mentoring; 2) Mentor graduate students through a graduate course on systems biology and a related course on systems biology specific to bioenergy pursuits, as well as through research mentoring; 3) Guide the first undergraduate International Genetically Engineering Machines (iGEM) team from U. of Illinois to provide expansive research experience for large number of undergraduates in systems and synthetic biology; and 4) Develop a short-course on model-guided cellular engineering to be taught internationally, beginning in China and Korea, as well as within the USA.Intellectual merit: This proposed project represents an endeavor to model the genotype to phenotype relationship in enough detail to allow for rational design to guide synthetic biology. It also utilizes an emerging technology and data source, metabolomics, and integrates it with quantitative modeling that is needed to harness the information content of this data. The development of systems biology approaches to generate predictive network models from high-throughput data sources is a highly significant activity towards harnessing the power of genomics to accomplish engineering goals. Broader Impacts: The development of efficient means to generate cellulosic butanol addresses issues relevant to very high impact areas for the United States, including decreasing our dependence on foreign oil and generating a carbon-neutral fuel cycle to benefit the environment. The proposed work will also be integrated with more comprehensive experimental programs, including industrial partnerships, as needed to translate findings to practical application. The associated educational aims are critical to training a new generation of scientists and engineers who have a deep understanding of both quantitative analysis and biology, as is needed to drive 21st Century Biology and the emerging bioenergy industry.