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Related Concept Videos

Gene-Environment Interactions01:20

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Gene expression is a dynamic process that is significantly influenced by environmental factors. This interaction underlies the complex nature of biological development and the phenotypic differences observed among individuals, even among those with identical genetic makeups. Factors such as radiation, temperature, behavior, nutrition, and stress play pivotal roles in determining how genes are expressed. The concept of the reaction range is central to understanding this interaction. It posits...
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Introduction to Enzyme Kinetics01:19

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Enzyme kinetics studies the rates of biochemical reactions. Scientists monitor the reaction rates for a particular enzymatic reaction at various substrate concentrations. Additional trials with inhibitors or other molecules that affect the reaction rate may also be performed.
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Generating Controlled, Dynamic Chemical Landscapes to Study Microbial Behavior
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Kinetics-based inference of environment-dependent microbial interactions and their dynamic variation.

Hyun-Seob Song1,2, Na-Rae Lee3, Aimee K Kessell1

  • 1Department of Biological Systems Engineering, University of Nebraska-Lincoln, Lincoln, Nebraska, USA.

Msystems
|April 29, 2024
PubMed
Summary

We developed a new model to predict how microbial interactions change with their environment. This framework integrates growth kinetics and a generalized Lotka-Volterra model, improving ecological predictions and community engineering.

Keywords:
Lotka-Volterra modelscompetitioncontext dependencecooperationkinetic modelsmicrobial communities

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Area of Science:

  • Microbial Ecology
  • Theoretical Ecology
  • Systems Biology

Background:

  • Microbial communities exhibit dynamic interactions influenced by environmental changes.
  • Predictive ecological modeling requires understanding context-dependent interspecies interactions.
  • Current models lack a fundamental understanding of environment-driven microbial interactions.

Purpose of the Study:

  • To propose a novel theoretical framework for modeling environment-dependent microbial interactions.
  • To integrate growth kinetics and generalized Lotka-Volterra models for predicting interaction dynamics.
  • To provide a method for understanding and engineering microbial community behavior.

Main Methods:

  • Combined growth kinetics with a generalized Lotka-Volterra model.
  • Represented interspecies interactions as an explicit function of environmental variables.
  • Experimentally validated the framework using a synthetic consortium of *Escherichia coli* mutants.

Main Results:

  • Demonstrated prediction of altered interspecies interactions based on dynamic environmental balances.
  • Showcased how substrate availability controls interactions in an *E. coli* consortium.
  • Quantified complex interaction aspects like asymmetry in microbial relationships.

Conclusions:

  • The developed framework enables modeling environment-controlled microbial interactions.
  • This approach improves understanding, prediction, and engineering of microbial communities.
  • The theory is applicable to diverse ecological systems beyond microbes.