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 生产可再生液体燃料和化学品最有前途的途径之一是通过专门的微生物发酵废碳。这不仅可以实现先进的生物燃料和可再生化学品的生产，还可以帮助减少碳排放。 LanzaTech 等新兴公司正在引领气体发酵技术的商业化发展，但必须解决许多基础研究问题，以进一步提高该技术的经济竞争力。一个特别重要的挑战是开发集成的代谢和运输模型来描述工业相关鼓泡塔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，在环境科学、生物技术、生物能源和人类健康方面具有许多潜在的应用。该 GOALI 项目的目标是开发时空代谢建模的通用工具，并通过在鼓泡塔反应器中的气体发酵中应用来评估这些方法。PI 计划转化富含二氧化碳的废物流以及合成气（syngas - 主要包括H2/CO/CO2）在泡罩塔反应器中转化为液体燃料和化学品。 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内相关对流和/或扩散过程的传输方程相结合。所得模型由嵌入细胞外环境空间和动态变化的部分不同方程中的细胞内代谢线性程序组成。麻省大学将利用气体发酵和生物膜生长问题（如计算机测试台）开发高效的模型制定和强大的数值求解技术。合成气发酵模型将与气体发酵和鼓泡塔反应器技术领域的行业领导者 LanzaTech 合作开发。这些模型将通过将最近开发的合成气发酵细菌 Clostridium ljungdahlii 的基因组规模代谢重建与原料气成分和主要代谢副产物乙醇和乙酸盐的对流传输方程相结合来制定。在麻省大学进行初步测试后，合成气发酵模型将使用从 LanzaTech 实验室/试点设施收集的数据进行验证。利用这些数据，时空代谢模型将根据需要进行完善，以捕获工业鼓泡塔反应器的关键特征。更广泛的影响：拟议的研究将既推进基础研究，又影响工业实践。虽然已经发表了一些关于时空代谢模型的孤立论文，但我们的研究将为这一重要问题提供更为普遍的解决方案。我们期望专注于合成气发酵的应用工作能够产生新的计算工具来模拟、设计和优化工业鼓泡塔反应器。由 NSF 资金支持的麻州大学研究生将在 LanzaTech 位于伊利诺伊州斯科基的研究机构完成为期四个月的实习，参与数据收集并执行模型细化和验证。该学生将得到两名项目研究人员的共同建议，其中 Henson 教授（麻省大学，PI）领导方法开发工作，Griffin 博士（LanzaTech，联合 PI）负责监督气泡塔模型开发工作。在 LanzaTech 期间，学生将在高度多学科和以团队为导向的环境中与众多科学家和工程师一起工作。麻省大学和 LanzaTech 的紧密整合将通过频繁的电子邮件交流、每两周一次的视频会议和每两年一次的项目会议来实现。至少两名本科生将通过获得资助的博士学位参与该研究。学生发挥部分建议作用。这些学生将与马萨诸塞州生物燃料研究所 (TIMBR) 资助的其他学生互动，并参与正在进行的 TIMBR 活动。