Algorithm of spatial-temporal simulation for environment-strain interactions in strain-strain consortia based on resource competition mechanism
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Published on: April 11, 2018
Chen Yang1, Boyuan Xue1, Qianqian Yuan1
1State Key Laboratory of Chemical Resource Engineering, Beijing Key Laboratory of Bioprocess, and Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing 100029, People's Republic of China.
Abstract:
Interaction simulation for co-culture systems is important for optimizing culture conditions and improving yields. For industrial production, the environment significantly affects the spatial-temporal microbial interactions. However, the current research on polymicrobial interactions mainly focuses on interaction patterns among strains, and neglects the environment influence. Based on the resource competition relationship between two strains, this research set up the modules of cellular physicochemical properties, nutrient uptake and metabolite release, cellular survival, cell swimming and substrate diffusion, and investigated the spatial-temporal strain-environment interactions through module coupling and data mining. Furthermore, in an Escherichia coli-Saccharomyces cerevisiae consortium, the total net reproduction rate decreased as glucose was consumed. E. coli gradually dominated favorable positions due to its higher glucose utilization capacity, reaching 100 % abundance with a competitive strength of 0.86 for glucose. Conversely, S. cerevisiae decreased to 0 % abundance with a competitive strength of 0.14. The simulation results of environment influence on strain competitiveness showed that inoculation ratio and dissolved oxygen strongly influenced strain competitiveness. Specifically, strain competitiveness increased with higher inoculation ratio, whereas E. coli competitiveness increased as dissolved oxygen increased, in contrast to S. cerevisiae. On the other hand, substrate diffusion condition, micronutrients and toxins had minimal influence on strain competitiveness. This method offers a straightforward procedure without featured downscaling and provides novel insights into polymicrobial interaction simulation.
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