(643b) Methane-Limited Vs Oxygen-Limited Growth of Methylomicrobium Buryatense 5GB1: a Systems Approach
Over the past 30 years methanotrophs have moved from a âblack boxâ organism to being on the cusp of becoming the next biocatalyst in a promising biotechnical world. With their unique methane monooxygenase enzyme, these organisms can capture methane from renewable biogas processes or waste streams of chemical conversion systems. Of the many different methanotrophs, Methylomicrobium buryatense 5GB1 is a promising strain because of its relatively fast growth rate in medium that is resistant to contaminants and due to its potential to generate organic acids, ectoine, and desirable lipids for biodiesel.
In this work we provide a systematic approach in analyzing the carbon use and growth patterns of 5GB1 with an in-house gas mixing system that safely creates a variety of headspace conditions with methane, oxygen, and nitrogen. Of interest are the yields of methane to biomass, to carbon dioxide, and to organic liquid products in batch and continuous cultures. The approaches used overcome challenges with headspace analysis and increased CO2 solubility. In doing so, an overall balance of 95%-105% was consistently achieved.
Via the established carbon balance, the measured pickup and production rates are utilized to examine a recently developed genome metabolic model (GEMs). Our findings suggest that solely constraining methane pickup alone would not accurately portray the measured growth and carbon dioxide production rates as predictions cross multiple phenotypes within the model. By including both methane and oxygen measured inputs as constraints, insights on discrepancies from optimal solutions and predictive performance were found to be caused by the modelâs energy production and consumption capabilities. Energetic maintenance costs and an additional hybrid respiration were examined in efforts to improve prediction values of growth and carbon dioxide production rates to those measured. Through this analysis, both experimental and computational data provide insights on the phenotype of 5GB1 under different experimental conditions, adding to the knowledge base required to design processes for improved methane bioconversion.