GOALI: Development of Spatiotemporal Metabolic Models for Syngas Fermentation in Industrial Bubble Column Reactors

GOALI：工业鼓泡塔反应器中合成气发酵时空代谢模型的开发

• 批准号: 1511346
• 负责人: Michael Henson
• 金额: $ 30万
• 依托单位: University of Massachusetts Amherst
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-06-01 至 2019-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1511346&HistoricalAwards=false
• 关键词: GOALI; Development; Spatiotemporal; Metabolic; Models

Henson - 1511346One of the most promising routes to renewable liquid fuels and chemicals is the fermentation of waste carbon by specialized microbes. This can not only enable advanced biofuel and renewable chemical production but could also help reduce carbon emissions. Commercial development of gas fermentation technology is being led by emerging companies such as LanzaTech, but many fundamental research problems must be addressed to further advance the technology towards economic competitiveness. A particularly important challenge is to develop integrated metabolic and transport models that describe gas fermentation in industrially relevant bubble column reactors. The development of such spatiotemporal metabolic models is an emerging research problem with numerous potential applications in environmental science, biotechnology, bioenergy and human health. The objectives of this GOALI project are to develop general tools for spatiotemporal metabolic modeling and to evaluate the methods through application to gas fermentation in bubble column reactors.The PIs plan to convert CO-rich waste streams as well as synthesis gas (syngas - mainly comprised of H2/CO/CO2) to liquid fuels and chemicals in bubble column reactors. His proposed modeling approach involves combining genome-scale reconstructions of species metabolism with transport equations that govern the relevant convective and/or diffusional processes within the spatially varying system. The resulting models consist of linear programs for intracellular metabolism embedded within partial different equations for spatial and dynamic variations within the extracellular environment. UMass will develop efficient model formulation and robust numerical solution techniques using gas fermentation and biofilm growth problems as in silico testbeds. The syngas fermentation models will be developed in collaboration with LanzaTech, an industrial leader in gas fermentation and bubble column reactor technology. These models will be formulated by combining a recently developed genome-scale metabolic reconstruction of the syngas fermenting bacterium Clostridium ljungdahlii with convective transport equations for the feed gas components and the major metabolic byproducts, ethanol and acetate. Following initial testing at UMass, the syngas fermentation models will be validated with data collected from a LanzaTech laboratory/pilot facility. Using these data, the spatiotemporal metabolic models will be refined as necessary to capture the key features of industrial bubble column reactors.Broader Impacts: The proposed research will both advance fundamental research and impact industrial practice. While a few isolated papers have been published on spatiotemporal metabolic modeling, our research will produce a considerably more general treatment of this important problem. We expect the application work focused on syngas fermentation to produce new computational tools to simulate, design and optimize industrial bubble column reactors. The UMass graduate student supported by NSF funds will complete a four month internship at LanzaTech?s Skokie, IL research facility to participate in data collection and to perform model refinement and validation. The student will be co-advised by the two project investigators, with Prof. Henson (UMass, PI) leading the methods development work and Dr. Griffin (LanzaTech, co-PI) overseeing the bubble column model development work. While at LanzaTech, the student will work with a broad array of scientists and engineers in a highly multidisciplinary and team oriented environment. Tight integration of the UMass and LanzaTech efforts will be achieved through frequent email exchanges, biweekly videoconferences and biannual project meetings. At least two undergraduate students will participate in the research by having the funded Ph.D. student serve a partial advising role. These students will interact with other students funded through the Institute of Massachusetts Biofuels Research (TIMBR) and participate in ongoing TIMBR activities.

Henson -1511346可再生液体燃料和化学物质的最有希望的途径之一是用专门的微生物发酵废碳。这不仅可以实现高级生物燃料和可再生化学生产，而且还可以帮助减少碳排放。天然气发酵技术的商业开发是由Lanzatech等新兴公司领导的，但是必须解决许多基本的研究问题，以进一步促进经济竞争力的技术。一个特别重要的挑战是开发综合的代谢和运输模型，这些模型描述了与工业相关的气泡柱反应器中的气体发酵。这种时空代谢模型的发展是一个新兴的研究问题，其在环境科学，生物技术，生物能源和人类健康中的许多潜在应用。该目标项目的目标是开发用于时空代谢建模的通用工具，并通过在气泡色谱柱反应堆中应用于气体发酵来评估这些方法。PIS计划将共富集的废物流以及合成气体（Syngas（Syngas）（主要由H2/CO/CO2）转换为液体燃料燃料燃料燃料燃料和化学物质列中的合成气体。 他提出的建模方法涉及将物种代谢的基因组规模重建与控制空间变化系统中相关对流和/或扩散过程的传输方程。最终的模型由嵌入细胞内代谢的线性程序组成，这些程序嵌入了部分不同方程式中，用于细胞外环境内的空间和动态变化。 UMass将使用气体发酵和生物膜生长问题来开发有效的模型公式和鲁棒的数值溶液技术，如硅测试床中。 Syngas发酵模型将与Lanzatech合作开发，Lanzatech是气体发酵和泡沫柱反应堆技术的工业领导者。这些模型将通过将最近开发的基因组规模的代谢重建结合起来，该基因组量表的代谢重建发酵ljungdahlii梭状芽胞杆菌与进料气体成分的对流传输方程以及主要代谢副产物，乙醇和乙酸酯。在UMass进行初步测试之后，Syngas发酵模型将通过Lanzatech实验室/试点设施收集的数据进行验证。使用这些数据，将在必要时进行时空代谢模型，以捕获工业泡沫柱反应器的关键特征。Broader的影响：拟议的研究将提高基础研究和影响工业实践。虽然已经发表了一些有关时空代谢建模的孤立论文，但我们的研究将对这一重要问题产生更多的一般性处理。我们预计，应用程序的工作重点是合同体发酵，可以生成新的计算工具，以模拟，设计和优化工业气泡柱反应器。 NSF基金支持的UMass研究生将在伊利诺伊州Lanzatech的Skokie完成四个月的实习，以参与数据收集并进行模型的改进和验证。两名项目调查员将与学生共同努力，Henson教授（UMASS，PI）领导了方法开发工作，而Griffin博士（Lanzatech，Co-Pi）负责监督Bubble柱模型开发工作。在Lanzatech期间，学生将在高度多学科和面向团队的环境中与各种各样的科学家和工程师合作。 UMass和Lanzatech努力的紧密整合将通过频繁的电子邮件交流，每两周进行视频会议和双年度项目会议来实现。至少有两名本科生将通过获得资助的博士学位来参加这项研究。学生担任部分建议角色。这些学生将与通过马萨诸塞州生物燃料研究所（TIMBR）资助的其他学生进行互动，并参加持续的Timbr活动。