Understanding competition and cooperation within microbiota is of high fundamental and clinical importance, helping to comprehend species' evolution and biodiversity. We co-encapsulated and cultured two isogenic Escherichia coli strains expressing blue (BFP) and yellow (YFP) fluorescent proteins into numerous emulsion droplets and quantified their growth by employing fluorescence measurements. To characterize and compare the bacterial growth kinetics and behavior in mono and co-culture, we compared the experimental observations with predictions from a simple growth model. Varying the initial ratio (R0) of both cell types injected, we observed a broad landscape from competition to cooperation between both strains in their confined microenvironments depending on start frequency: from a nearly symmetric situation at R0 = 1, up to the domination of one subpopulation when R0 ≫ 1 (or R0 ≪ 1). Due to competition between the strains, their doubling times and final biomass ratios (R1) continuously deviate from the monoculture behavior. The correlation map of the two strains' doubling times reveals that the R0 is one of the critical parameters affecting the competitive interaction between isogenic bacterial strains. Thanks to this strategy, different species of bacteria can be monitored simultaneously in real-time. Further advantages include high statistical output, unaffected bacteria growth, and long-time measurements in a well-mixed environment. We expect that the millifluidic droplet-based reactor can be utilized for practical clinical applications, such as bacterial antibiotic resistance and enzyme reaction kinetics studies.
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World J Microbiol Biotechnol
January 2025
Shandong Provincial Key Laboratory of Water Pollution Control and Resource Reuse, School of Environmental Science and Engineering, Shandong University, 72 Binhai Road, Jimo, Qingdao, 266237, China.
Catabolic plasmids are critical factors in the degradation of recalcitrant xenobiotics, such as dioxins. Understanding the persistence and evolution of native catabolic plasmids is pivotal for controlling their function in microbial remediation. Here, we track the fitness and evolution of Rhodococcus sp.
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December 2024
Motor Action Research Group (GIAM), Institut de Desenvolupament Social i Territorial (INDEST), National Institute of Physical Education of Catalonia (INEFC), University of Barcelona (UB), Av. de l'Estadi 12-22, Anella Olímpica, E-08038 Barcelona, Spain.
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View Article and Find Full Text PDFFoods
January 2025
College of Basic Science, Tianjin Agricultural University, Tianjin 300380, China.
The safety and health of food have been persistent concerns, particularly about meat products. Low-temperature meat products refer to those that are processed at lower temperatures. Meat, rich in proteins and other nutrients, is highly susceptible to microbial contamination, leading to spoilage, particularly when processed at lower temperatures that increase storage and transportation requirements.
View Article and Find Full Text PDFTrends Biotechnol
January 2025
Terra Research and Teaching Centre, Microbial Processes and Interactions (MiPI), Gembloux Agro-Bio Tech, University of Liège, Gembloux, Belgium. Electronic address:
Much attention has focused on understanding microbial interactions leading to stable co-cultures. In this work, substrate pulsing was performed to promote better control of the metabolic niches (MNs) corresponding to each species, leading to the continuous co-cultivation of diverse microbial organisms. We used a cell-machine interface, which allows adjustment of the temporal profile of two MNs according to a rhythm, ensuring the successive growth of two species, in our case, a yeast and a bacterium.
View Article and Find Full Text PDFEntropy (Basel)
December 2024
School of Computer Science, Peking University, Beijing 100871, China.
Multi-agent systems often face challenges such as elevated communication demands, intricate interactions, and difficulties in transferability. To address the issues of complex information interaction and model scalability, we propose an innovative hierarchical graph attention actor-critic reinforcement learning method. This method naturally models the interactions within a multi-agent system as a graph, employing hierarchical graph attention to capture the complex cooperative and competitive relationships among agents, thereby enhancing their adaptability to dynamic environments.
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