Research

Harnessing photoautotroph-methanotroph interactions for biogas conversion to fuels and chemicals using binary consortia

Funded by: Office of Biological and Environmental Research, Genomic Science Program,

Award Number DE-SC0019181.

Principal Investigator: Jin Wang, Auburn University;

Co-Principal Investigator: Q. Peter He, Auburn University;

Co-Principal Investigator: Marina Kalyuzhnaya, San Diego State University;

Co-Principal Investigator: Alexander S Beliaev, Pacific Northwest National Laboratory;

Biogas, produced through anaerobic digestion of organic waste streams, has immense potential to be used as a renewable feedstock to produce high-density fuels and commodity chemicals. However, the utilization of biogas (~60% CH4 and ~40% CO2) represents a significant challenge due to the presence of contaminants such as H2S, ammonia, and volatile organic carbon compounds. To tap into this immense potential, effective biotechnologies that co-utilize both CO2 and CH4 are needed.

In nature, microbial communities have developed a highly efficient way to recover the energy and capture carbon from biogas through metabolic coupling of methane oxidation to oxygenic photosynthesis. From an engineering perspective, this coupling offers several major advantages for the design of robust microbial catalysts for biogas conversion. First, exchange of in situ produced O2 and CO2 dramatically reduces mass transfer resistance of the two gas substrates; Second, in situ O2 consumption removes inhibition on photoautotroph and eliminates risk of explosion; Third, interdependent yet compartmentalized configuration of the coculture offers flexibility and more options for metabolic engineering.

Using the principles that drive the natural consortia, we have assembled and investigated several different photoautotroph-methanotroph cocultures that exhibit stable growth under a broad range of cultivation conditions. In this project, using Arthrosipira platensisMethylomicrobium buryatense 5GB1 as the model coculture, we aim to develop experimental and computational tools to gain qualitative and quantitative understandings on the interactions and dynamics of the coculture at both systems and molecular levels, and to validate our findings through experiments and mutant development.

The research activities will emphasize a multidisciplinary systems approach, which integrates hypothesis-driven experiments with data-driven transcriptomic analyses, and uses genome-scale metabolic modeling to integrate the findings obtained on the potential interactions within the coculture. The fundamental understanding on the interactions and dynamics of the coculture will lay the foundation for the design and optimization of synthetic binary consortia for production of fuels and chemicals from biogas.

Students who are working or worked on this project:

Dr. Matthew Hilliard (current postdoc); Kiumars Badr (current PhD student); William Whelan (current PhD student); Alisabeth Bradford (current PhD student), Dr. Nathan Roberts (graduated), Dr. Kyle Stone (graduated).

Publications:

  1. Badr K., Whelan W., He Q.P. & Wang J. (2020), Fast and Easy Quantitative Characterization of Methanotroph-Photoautotroph Cocultures, under review;
  2. Roberts N., Hilliard M., He Q.P. & Wang J. (2020), A microalgae-methanotroph coculture platform to convert wastewater into microbial biomass for fuels and chemical production, Frontiers in Energy Research, Vol. 8, Article 563352.
  3. He Q.P. & Wang J. (2020), Application of systems engineering principles and techniques in biological big data analytics: a review, Processes, 8(8), 951;
  4. Stone K., Hilliard M., Badr K., Bradford A., He Q.P., & Wang J. (2020), Comparative study of oxygen-limited and methane-limited growth phenotypes of Methylomicrobium buryatense 5GB1, Biochemical Engineering Journal, 161, 15, 1-13;
  5. Stone K., He Q.P., & Wang J. (2019), Two Experimental Protocols for Accurate Measurement of Gas Component Uptake and Production Rates in Bioconversion Processes, Nature – Scientific Report, 9 (1), 5899. https://www.nature.com/articles/s41598-019-42469-3
  6. Bahr K., Hilliard M., Roberts N., He Q.P. and Wang J. (2019), Photoautotroph-Methanotroph Coculture – A Flexible Platform for Efficient Biological CO2-CH4 Co-utilization, IFAC-PapersOnLine, 52 (1), 916-921;

Patent:

  1. He Q.P., Wang J., Hilliard M.V., Culture systems and methods of using same, US Patent Application # 16,934,766, filed on July 21, 2020

Presentation:

  1. Badr K., Whelan W., He Q.P. & Wang J. (2020), Tools for Easy, Fast and Accurate Quantitative Characterization of the Methanotroph-Photoautotroph Coculture, AIChE Annual Meeting, accepted;
  2. Roberts N., Hilliard M., He Q.P. & Wang J. (2020), A Microalgae-Methanotroph Coculture Platform for Fuels and Chemical Production from Wastewater, AIChE Annual Meeting, accepted;
  3. Badr K., He Q.P. & Wang J. (2020), Exploring the Interspecies Interactions within a Methanotroph-Cyanobacteria Coculture through Genome-Scale Metabolic Modeling, 2020 AIChE Annual Meeting, accepted;
  4. Badr K., Hilliard M., He Q.P. & Wang J. (2020), A dynamic genome-scale metabolic network model for a novel methanotroph-cyanobacteria coculture, 42nd Symposium on Biomaterials, Fuels and Chemicals, accepted and selected for oral presentation;
  5. Roberts N., Hilliard M., Badr K., Whelan W., He Q.P. & Wang J. (2020), A novel biological platform for integrated biogas upgrading and nutrient recovery to valorize anaerobic digester, 42nd Symposium on Biomaterials, Fuels and Chemicals, accepted for poster presentation;
  6. Badr K., Whelan W., Roberts N., He Q.P. & Wang J. (2020), Quantitative Characterization of Methanotroph-Photoautotroph Cocultures, 42nd Symposium on Biomaterials, Fuels and Chemicals, accepted and selected for oral presentation;
  7. Badr K., Hilliard M., Whelan W., He Q.P., Kalyuzhnaya M., Beliaev A.  & Wang J. (2019), Understanding the Dynamics of a Methanotroph-Cyanobacterium Coculture through Kinetic Modeling and Experimental Verification, 2nd International Conference on Microbiome Engineering, Dec. 2-4, Boston, MA
  8. Stone K., He Q.P. & Wang J. (2019), Two Experimental Protocols for Accurate Measurement of Gas Component Uptake and Production Rates in Bioconversion Processes, 2019 AIChE Annual Meeting, Nov. 11-15, Orlando, FL
  9. Roberts N., He Q.P. & Wang J. (2019), Nutrient Recovery from Municipal Wastewater Using a Methanotroph-Microalgae Co-Culture, 2019 AIChE Annual Meeting, Nov. 11-15, Orlando, FL
  10. Badr K., He Q.P. & Wang J. (2019), A Dynamic Genome-Scale Metabolic Network Model for a Novel Methanotroph-Cyanobacteria Coculture, 2019 AIChE Annual Meeting, Nov. 11-15, Orlando, FL
  11. Badr K., Hilliard M., He Q.P. & Wang J. (2019), Kinetic Modeling of a Novel Methanotroph-Cyanobacterium Coculture for Biogas Conversion, Annual Data Science Forum – Machine Learning in Science and Engineering Symposium, June 10-12, 2019, Atlanta, GA;
  12. Bahr K., Roberts N., He Q.P. & Wang J. (2018), Understanding the Stability and Robustness of a Methanotroph-Cyanobacterium Coculture through Kinetic Modeling and Experimental Verification, 2018 AIChE Annual Conference, Oct. 28 – Nov. 2, Pittsburgh, PA.
  13. Roberts N., He Q.P. & Wang J., Using Methanotroph-Microalgae Coculture for Wastewater Treatment, 2018 AIChE Annual Conference, Oct. 28 – Nov. 2, Pittsburgh, PA.
  14. Stone K., He Q.P. & Wang J., Methane-limited vs oxygen-limited growth of Methylomicrobium Buryatense 5GB1:a systems pproach, 2018 AIChE Annual Conference, Oct. 28 – Nov. 2, Pittsburgh, PA;
 

Systematically engineer Clostridium for efficient ester production

Funded by: United States Department of Agriculture, National Institute of Food and Agriculture, AFRI Foundational Program, 2018-67021-27715

Principal Investigator: Yi Wang, Auburn University

Co-Principal Investigator: Jin Wang, Auburn University

The finite nature of fossil fuel, as well as the associated environmental problems, provides an impetus for alternative bio-based fuels and chemicals from renewable resources. Butyl butyrate is a valuable fuel source possessing excellent properties as gasoline, diesel or aviation kerosene components, and an industrially significant biochemical as food flavors, fragrance scents and feedstock for various industries. Traditional butyl butyrate production from petroleum is highly energy-consuming and not environmental friendly. Therefore, production of butyl butyrate through biological-catalyzed routes has become increasingly attractive because it is renewable, efficient and environmentally benign.

Clostridium tyrobutyricum is well known as a hyper-butyrate producer, and high-level butanol production can be achieved through metabolic engineering approaches (with co-production of high-level butyryl-CoA or butyrate). Therefore, C. tyrobutyricum can be considered as an excellent platform for butyl butyrate production through rational metabolic engineering. The long-term goal of this project is to achieve renewable ester, particularly butyl butyrate production from low-value carbon sources using metabolically stable Clostridium tyrobutyricum that will be generated through systematic genome engineering.

Wang group’s contribution to this project is to develop a genome-scale metabolic model (GEM) for C. tyrobutyricum. As production of butyl butyrate requires more reducing power which might impair the redox balance of the metabolic pathways, a high quality GEM will guide the development of metabolic engineering strategies for butyl butyrate production.

Students who are working or worked on this project:

Kiumars Badr (current PhD student).

Presentation:

  1. Badr K., Zhang J., Wang Y., & Wang J. (2019), Genome Scale Model Analysis of Clostridium Tyrobutyricum for Butyl Butyrate Production, 2019 AIChE Annual Meeting, Nov. 11-15, Orlando, FL
 

A System Identification Based Framework for Metabolic Network Analysis

Description:

Genome-scale metabolic network models represent the link between the genotype and phenotype of the organism, where they are usually reconstructed based on the genome sequence annotation and relevant biochemical and physiological information. These models provide a holistic view of the organism’s metabolism and constraint-based metabolic flux analysis methods have been used extensively to study genome-wide cellular metabolic networks. However, due to the scale and complexity involved in genome-scale models, how to validate the model and how to extract mechanistic insights from the model have been challenging.  To address this challenge, we have developed a system identification based framework to extract qualitative biological information (such as how different pathways interact with each other) from quantitative numerical results by performing carefully designed in silico experiments. This framework has been applied to compare genome-scale models, to guide the improvement of genome scale models, as well as to enhance phenotype phaseplane analysis.

Funding:

NSF 1264861

Students working on this project:

Graduate: Andy Damiani
Undergraduate: Jeffrey Liu


Kinetic Modeling of Co-Culture Systems with a Novel Bioreactor and Pseudo-Continuous Fermentation

 Description:

In view of rising concerns over energy sustainability and global warming, lignocellulosic biofuels have been identified as promising long-term renewable energy sources. Complete substrate utilization is one of the prerequisites to render lignocellulosic biofuel processes economically competitive. For lignocellulosic ethanol, fermentation of hexoses (i.e., glucose, mannose, and galactose) using Saccharomyces cerevisiae is well established on a large scale. However, the conversion of pentoses (i.e., xylose and arabinose) to ethanol is still one of the major barriers of industrializing lignocellulosic ethanol processes. Compared to the use of a single recombinant strain, co-culture of specific microbes that exploits their native capabilities to metabolize different sugar components of lignocellulosic hydrolysates is a promising alternative. Recently, research on using different co-culture systems for lignocellulosic ethanol fermentation has drawn significant interest, mainly due to their flexibility, tunability, and increased resistance to environmental stress. However, up to now, very limited research has been done to systematically investigate the dynamic properties and interaction of co-culture strains due to the lack of effective co-culture equipment and dynamic modeling tools. In this project, we develop a novel membrane separated bioreactor, together with pseudo-continuous fermentation to conduct systematic research on a co-culture system (Saccharomyces cerevisiae  and Scheffersomyces stipitis).

Funding:

USDA-AFRI 10252981 and Sun Grant

Students working on this project:

Graduate: Min Hea Kim, Andy Damiani, Kyle Stone
Undergraduate: Tomi Adekoya


Dynamic Modeling of Metabolic Network Systems

Description:

Constraint-based approaches to model cellular metabolism have made significant progress in the last few decades, and hundreds of methods have been developed. Relying on the stoichiometry of the reaction network and an optimization procedure, these methods describe the steady-state of the metabolic network without requiring kinetics information. However, these methods cannot describe the cell’s dynamic behavior, and have difficulties in incorporating the information of cellular regulation. Although flux balance analysis (FBA, the most commonly applied constraint-based approach) has been extended to dynamic versions, limitations exist and further improvement is needed. In this area, we aim to develop a computation framework, named flux balance analysis guided dynamic programming (FBA-DP), to model the dynamics of cellular metabolism. In addition, we have developed new equipment and experimental procedures to achieve precise control of operation condition as well as high-frequency data acquisition required to develop the computational framework.

 Funding:

NSF 1264861, NSF-IGERT

Students working on this project:

Graduate: Andy Damiani, Kyle Stone


Statistics Pattern Analysis-Next Generation Process Monitoring Framework

Description:

Manufacturing processes are transitioning from labor-intensive operation towards automation intensive operation. This trend has significantly reduced human intervention to the process and created more “black boxes” where little direct knowledge is available regarding what is going on with the process. At the same time, due to the advances in sensor, control and information technologies, industry of all sizes and all types are sitting on mountains of data. Therefore, it is crucial to extract meaningful information from large and complex data sets in order to assess process performance and improve process efficiency and product quality. At the same time, modern manufacturing processes present some challenges that cannot be readily addressed by existing methods, such as nonlinear dynamics, non-Gaussianity, frequent process changes driven by manufacturing on-demand, as well as overwhelming amount of data-preprocessing required by batch processes. In this area, we develop a next generation process monitoring framework – Statistic Pattern Analysis to help address these challenges. By monitoring the selected statistics of process variables, instead of process variables themselves, the new framework effectively addresses process non-Gaussianity, nonlinearity as well as eliminating/reducing labor intensive data pre-processing.

 Funding:

Alabama Innovation Fund & Anderson Foundation and DOE GAANN

Students working on this project:

Graduate: Xiu Wang


Understanding Cellular Metabolism of Methanotrophs

Description:

Natural gas has emerged as a potential major feedstock and a more sustainable resource for the production of fuels and valuable chemical products due to two reasons: (1) the large abundance found in the U.S. and the world, and (2) potential benefit of reducing greenhouse gas (GHG) emissions. Compared to chemical conversion of methane to liquid fuels, usually through the Fischer-Tropsch process, biological methane conversion through methanotrophs offers many advantages: low capital cost, high selectivity at ambient temperature and pressure. However, many challenges exist for a commercially viable biological conversion route due to the low production and low yield. In order to develop a highly efficient bio-conversion route, a better biocatalyst is necessary. In this project, we apply the experimental and computational tools developed by our group to gain better understanding on the cellular metabolism of a methanotroph, which will provide a foundation for developing an improved biocatalyst.

Funding:

DOE GAANN

Students working on this project:

Graduate: Kyle Stone, Andy Damiani
Undergraduate: Krishane Suresh

 


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