ECOMB 2019: Eurasian Congress on Molecular Biotechnology, Trabzon, Turkey, 19 - 21 September 2019, pp.163-171
Integrated Multi-organism Genome Scale Metabolic Modeling of Microbial Community of P.aeruginosa and M.barkeri
Muhammed Erkan Karabekmez1,*
1 Istanbul Medeniyet University, Faculty of Engineering and Natural Sciences, Department of Bioengineering, Istanbul, Turkey
Abstract: Composition of a microbial community in a bioreactor for bioremediation is crucial for the expected treatment. The most of the conventional studies in environmental biotechnology characterize the composition after attaining desired effect from the bioreactor. However, by integrating individual genome-scale metabolic models of potential microorganisms microbial communities can be modeled and effect of changes in ambient conditions can be simulated in more detail. Despite its high potential, use of the approach is still in its infancy in the field of environmental biotechnology. Here in this study we have integrated well characterized genome scale metabolic models of P.aeroginosa, whose denitrification capability is crucial for drinking water treatment, and M.barkeri which can produce methane. Integrated model of microbial community had simulated conditional competition and conditional syntrophy between the models. Default model of the P.aeroginosa utilizes glucose via respiration in aerobic environment and via fermentation in anoxic conditions. M.barkeri is known to be strict anaerobe and can utilize methanol, acetate and various other carbon sources. Carbon sources for the two organisms are set differently that’s why their competition is only on nitrogen and phosphate in nitrogen and phosphate limiting conditions. In the presence of excess nitrogen phosphate P.aeroginosa can utilize fumarate and ethanol to produce acetate which feeds M.barkeri and their relationship turns to a mutualistic form. P.aeroginosa has a more aggressive growth pattern compare to M.barkeri resulting of its domination in the competitive environment.
Key words: Genome-scale metabolic networks, environmental biotechnology, computational biology