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

0846964 PriceThis CAREER研究旨在将系统生物学和生物燃料生成的研究与系统生物学跨学科课程的创建相结合。本课程将针对高级本科生和研究生，并将与整个校园的努力，以创建一个新的生物能源专业硕士课程和生物信息学正在进行的计划相结合。 这项研究背后的动机有两个方面：一个是工程学，一个是科学。工程目标是重新设计拜氏梭菌的代谢网络，以提高丁醇的产量，这对于其作为化学原料的用途及其作为第二代生物燃料的高潜力非常重要。科学目标是开拓新的方法，在特征较少的生物体中使用系统生物学，如C。Beijerinckii的基因组规模的代谢网络，建立足够详细的基因型与表型关系的模型，以增强和控制特定感兴趣的特性，这是许多测序生物面临的一个非常普遍的挑战。具体的研究目标是：1）重建和实验验证第一个可计算的基因组规模的代谢网络的Beijerinckii;测试模型预测的验证实验将包括定量野生型和所选敲除菌株在各种底物上的生长速率、摄取速率和分泌速率; 2）进行代谢组学实验，并使用数据来增强代谢网络重建，并通过各种发酵过程监测代谢的变化; 3）构建基于Matlab的重建模块，以帮助其他团队更快地生成其他代谢模型，尽可能自动化该过程，并为仍然需要手动管理的最后步骤的那些方面提供组织工具;该工具也将作为本课程的教学辅助工具（见下文）; 4）使用计算模型指导C菌株的实验构建。用于提高丁醇生产的Beijerinckii。具体的教育目标是：1）通过开发系统生物学课程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追求相关的系统生物学课程以及通过研究指导来指导研究生; 3）指导来自美国的第一个本科生国际遗传工程机器（iGEM）团队。为系统和合成生物学的大量本科生提供广泛的研究经验;以及4）开发一个关于模型引导的细胞工程的短期课程，从中国和韩国开始，以及在美国进行国际教学。这个建议的项目代表了一个奋进，以足够详细的基因型与表型的关系，以允许合理的设计，以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，并将其与利用该数据的信息内容所需的定量建模相结合。 开发系统生物学方法以从高通量数据源生成预测网络模型是利用基因组学的力量实现工程目标的一项非常重要的活动。更广泛的影响：生产纤维素丁醇的有效方法的开发解决了与美国非常高影响领域相关的问题，包括减少我们对外国石油的依赖和产生碳中性燃料循环以造福环境。拟议的工作还将与更全面的实验计划相结合，包括工业伙伴关系，以将研究结果转化为实际应用。 相关的教育目标是培养新一代的科学家和工程师谁拥有定量分析和生物学的深刻理解，因为需要推动21世纪世纪生物学和新兴的生物能源产业的关键。

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.