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Related Concept Videos

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Microorganisms play a pivotal role in maintaining ecosystem balance by recycling essential elements such as carbon, nitrogen, and phosphorus, as well as supporting processes like bioremediation, wastewater treatment, and biofuel production.Microbes in Elemental CyclesIn the carbon cycle, microorganisms decompose organic matter, releasing carbon dioxide via aerobic respiration. This carbon dioxide is subsequently used by photosynthetic organisms to synthesize organic compounds, closing the...
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State-Space-Based Framework for Predicting Microbial Interaction Variability in Wastewater Treatment Plants.

Zhong Yu1,2, Yue Huang3, Zhihao Gan1,2

  • 1School of Environmental Science and Engineering, Sun Yat-sen University, Guangzhou 510006, PR China.

Environmental Science & Technology
|August 9, 2022
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Summary

Researchers developed a new framework to control microbial communities in wastewater treatment plants. This method identifies stable microbial subnetworks for predictable manipulation, improving environmental and health applications.

Keywords:
control theorydynamical microbial systemmanifold geometric propertynonsequential microbiome profileswastewater treatment plants

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Area of Science:

  • Microbiology
  • Environmental Engineering
  • Systems Biology

Background:

  • Microbial communities are crucial for environmental integrity, biosystem performance, and human health.
  • Manipulating microbial communities is challenging due to complex, nonlinear interspecific interactions.
  • Existing methods struggle with the dynamic and variable nature of microbial networks.

Purpose of the Study:

  • To develop a novel manifold-based framework for analyzing microbial interaction variability.
  • To design a control strategy for manipulating microbes within nonlinear community networks.
  • To validate the framework using real-world microbiome data from wastewater treatment plants.

Main Methods:

  • Utilized manifold geometric properties to investigate microbial interaction patterns.
  • Developed a control strategy based on identified stable microbial subnetworks.
  • Employed simulations to demonstrate the effectiveness of the control strategy.

Main Results:

  • Identified deterministic rival and cooperative relationships within activated sludge and anammox communities.
  • Revealed stable microbial subnetworks within the broader nonlinear community network.
  • Demonstrated through simulation that selected microbes can direct community dynamics predictably.

Conclusions:

  • The manifold-based framework offers an efficient solution for selecting control inputs in microbial networks.
  • This approach enables reliable manipulation of microbial communities in dynamic environments.
  • The findings open new avenues for applications in wastewater treatment and other biological fields.