Multiphase Metabolic Modeling of Biochemical Producing Bacterial Communities in Bubble Column Reactors

鼓泡塔反应器中生化生产细菌群落的多相代谢模型

• 批准号: 2048757
• 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: 2021
• 资助国家: 美国
• 起止时间: 2021-05-15 至 2022-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2048757&HistoricalAwards=false
• 关键词: Multiphase; Metabolic; Modeling; Biochemical; Producing

Development of cost-effective technologies for sustainable production of commodity fuels and chemicals remains a paramount challenge for the United States. Bacteria-based chemical production represents an alternative to petrochemical processes due to the microbes’ evolved ability to utilize diverse feedstocks and synthesize a broad array of biochemicals. However, the effectiveness of microbial systems has only been demonstrated at large scale for a limited number of applications, such as conversion of corn-derived sugar to the fuel additive ethanol. To succeed in the marketplace, microbial systems must offer more flexibility with respect to the feedstock they consume as well as the biochemicals they produce. Industrial waste gases rich in CO and CO2 represent an abundant and cheap source of carbon for upgrading to more valuable chemicals. Over the past decade, gas fermentation with CO2-consuming (acetogenic) bacteria has been shown to be a promising technology for large-scale conversion of such waste gases to ethanol. However, gas conversion to more valuable chemical products is considerably more challenging. The goal of this project is to develop the gas fermentation process simulation and optimization technology necessary to advance microbial systems for large-scale production of platform chemicals, the building blocks of a wide range of chemical products. The proposed research will significantly advance a novel microbial paradigm for conversion of cheap waste gases into value-added products by focusing on critical process systems engineering challenges. The research team will recruit women and URM students into the project through the NSF-sponsored Northeast Alliance in Minority and Graduate Education and the Professoriate program at UMass.Large-scale gas fermentation requires specialized bubble column reactor technologies and sophisticated operating strategies to maintain desirable multiphase hydrodynamics, achieve high gas-liquid mass transfer rates, minimize the inhibitory effects of dissolved gases and synthesized byproducts on cellular growth, achieve high cell densities, and ensure high volumetric productivity over a range of feed conditions. While bubble column reactors have been extensively investigated for CO fermentation, studies have been limited to acetogen monocultures and existing process engineering challenges have received very little attention. We have been developing a bubble column modeling approach based on combining genome-scale stoichiometric models of microbial metabolism with multiphase transport equations governing column hydrodynamics. In this project, these sophisticated models consisting of linear programs (LPs) and partial differential equations (PDEs) will be utilized to develop dynamic process modeling and optimization strategies for coculture bubble column reactors. Our target is the conversion of CO-rich gas streams containing CO2 and/or H2 to the platform chemicals butyrate and 2,3-butanediol. The proposed research has three specific aims: (1) Perform monoculture and coculture continuous-flow stirred tank reactor experiments with the acetogen Clostridium autoethanogenum and computationally identify promising gut bacteria by quantifying their metabolic activity; (2) Develop dynamic models of bubble column bioreactors for in silico investigation of coculture stability and performance for different gut bacteria paired with C. autoethanogenum; and (3) Perform rigorous optimization of the most promising coculture systems to determine dynamic startup and steady-state operating policies to maximize volumetric productivity. Collectively, the three aims will yield innovations in bioreactor process engineering through the systematic development of optimization approaches for LP-PDE models and in gas fermentation for renewable chemical production through the integrated development of dynamic modeling and optimization technologies.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

为可持续生产商品燃料和化学品开发具有成本效益的技术仍然是美国面临的最大挑战。基于细菌的化学生产是石油化工过程的一种替代方案，因为微生物具有利用各种原料和合成各种生物化学物质的进化能力。然而，微生物系统的有效性仅在有限数量的应用中得到了大规模的证明，例如将玉米衍生的糖转化为燃料添加剂乙醇。为了在市场上取得成功，微生物系统必须在它们消费的原料以及它们生产的生物化学品方面提供更多的灵活性。富含CO和CO2的工业废气是一种丰富而廉价的碳来源，可用于升级为更有价值的化学品。在过去的十年里，利用二氧化碳消耗细菌(产乙酸菌)进行气体发酵被证明是一种很有前途的将此类废气大规模转化为乙醇的技术。然而，将天然气转化为更有价值的化学产品的难度要大得多。该项目的目标是开发必要的气体发酵过程模拟和优化技术，以推动微生物系统大规模生产平台化学品，平台化学品是各种化工产品的基石。这项拟议的研究将通过关注关键的过程系统工程挑战，显著推进将廉价废气转化为增值产品的新微生物范例。研究团队将通过NSF赞助的东北少数民族和研究生教育联盟以及麻省理工大学的教授项目招募女性和URM学生参与该项目。大规模气体发酵需要专门的鼓泡塔反应器技术和复杂的操作策略，以保持理想的多相流体力学，实现高气液传质速率，最大限度地减少溶解气体和合成副产品对细胞生长的抑制作用，实现高细胞密度，并确保在一系列进料条件下的高体积生产率。虽然鼓泡塔反应器在CO发酵方面已经得到了广泛的研究，但研究仅限于乙酸根单一培养物，现有的工艺工程挑战很少受到关注。我们一直在开发一种泡沫塔建模方法，该方法将微生物代谢的基因组规模化学计量模型与管柱流体动力学的多相传输方程相结合。在这个项目中，这些由线性规划(LP)和偏微分方程(PDE)组成的复杂模型将被用来开发共培养鼓泡塔反应器的动态过程建模和优化策略。我们的目标是将含有CO2和/或H2的富含CO的气流转化为平台化学品丁酸酯和2，3-丁二醇。拟议的研究有三个具体目标：(1)以产乙酸梭菌进行单一培养和共培养连续搅拌釜式反应器实验，并通过量化它们的代谢活性来计算确定有潜力的肠道细菌；(2)开发鼓泡塔生物反应器的动态模型，用于电子计算机研究不同肠道细菌与自产乙酸梭菌共培养的稳定性和性能；以及(3)对最有希望的共培养系统进行严格优化，以确定动态启动和稳态操作策略，以最大化体积生产率。总的来说，这三个目标将通过系统地开发LP-PDE模型的优化方法，以及通过集成开发动态建模和优化技术，在可再生化学品生产的气体发酵方面产生生物反应器过程工程方面的创新。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。