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.

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生产，还可以帮助减少碳排放。气体发酵技术的商业化开发正由LanzaTech等新兴公司领导，但必须解决许多基础研究问题，以进一步推动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，描述工业相关的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的许多潜在的应用。该GOALI项目的目标是开发时空代谢建模的通用工具，并通过应用于鼓泡塔反应器中的气体发酵来评估方法。PI计划在鼓泡塔反应器中将富含CO的废物流以及合成气（合成气-主要由H2/CO/CO2组成）转化为液体燃料和化学品。 他提出的建模方法包括将物种代谢的基因组规模重建与管理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。由此产生的模型由细胞内代谢的线性程序嵌入在部分不同的方程内的空间和动态变化的细胞外环境。麻省大学将开发有效的模型制定和强大的数值求解技术，使用气体发酵和生物膜生长问题，在硅片测试床。合成气发酵模型将与气体发酵和鼓泡塔反应器技术的工业领导者LanzaTech合作开发。这些模型将制定结合最近开发的基因组规模的合成气发酵细菌杨氏梭菌的代谢重建与对流输送方程的进料气体成分和主要的代谢副产物，乙醇和乙酸。在马萨诸塞大学进行初步测试后，将使用从LanzaTech实验室/中试设施收集的数据对合成气发酵模型进行验证。使用这些数据，时空代谢模型将被细化为必要的捕捉工业鼓泡塔reactors.Broader影响的关键特征：拟议的研究将推进基础研究和影响工业实践。虽然已经发表了一些孤立的论文时空代谢建模，我们的研究将产生一个相当更普遍的治疗这个重要的问题。我们期望以合成气发酵为重点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基金支持的马萨诸塞大学研究生将在LanzaTech完成为期四个月的实习？的Skokie，IL研究机构参与数据收集，并进行模型改进和验证。学生将由两名项目研究人员共同指导，Henson教授（马萨诸塞大学，PI）领导方法开发工作，Griffin博士（LanzaTech，co-PI）监督鼓泡塔模型开发工作。而在LanzaTech，学生将在一个高度多学科和团队导向的环境中与广泛的科学家和工程师合作。马萨诸塞大学和LanzaTech努力的紧密结合将通过频繁的电子邮件交流，每两周一次的视频会议和一年两次的项目会议来实现。至少有两名本科生将通过获得资助的博士学位参与研究。学生扮演部分顾问角色。这些学生将与其他学生通过马萨诸塞州生物燃料研究所（TIMBR）资助互动，并参加正在进行的TIMBR活动。