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Related Concept Videos

Stability01:28

Stability

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The time response of a linear time-invariant (LTI) system can be divided into transient and steady-state responses. The transient response represents the system's initial reaction to a change in input and diminishes to zero over time. In contrast, the steady-state response is the behavior that persists after the transient effects have faded.
The stability of an LTI system is determined by the roots of its characteristic equation, known as poles. A system is stable if it produces a bounded...
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Biofilms01:29

Biofilms

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Biofilms are complex communities of microorganisms encased in a self-produced extracellular polysaccharide matrix attached to surfaces. These microbial consortia can include single or multiple species, providing enhanced survival benefits by forming organized, multilayered structures.The formation of biofilms occurs through four key stages: attachment, colonization, development, and dispersal.During attachment, free-swimming planktonic cells adhere to a surface, often facilitated by...
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Microtubules are hollow cylindrical filaments having a diameter of approximately 25 nm and a length that varies from 200 nm to 25 μm. GTP-bound tubulin subunits form αβ-heterodimers for microtubule assembly. These core building blocks interact longitudinally, polymerizing into protofilaments. The protofilaments then interact with one another through lateral bonding forces to form stable cylindrical microtubules. These cylindrical filaments are dynamic as they undergo repeated...
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Microorganisms are classified as acidophiles, neutrophiles, or alkaliphiles based on their pH growth preferences, reflecting their adaptations to specific environments. Maintaining a stable intracellular pH is critical for macromolecular stability and enzymatic activity, which can be challenged by external pH variations.Neutrophiles, such as Escherichia coli, grow optimally between pH 5.5 and 8.0. These microorganisms inhabit neutral or slightly acidic environments and employ mechanisms like...
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Operon Model01:23

Operon Model

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The operon model represents a fundamental mechanism of gene regulation in prokaryotes, enabling coordinated expression of genes involved in related metabolic or functional pathways. Operons consist of structural genes, a promoter, and an operator, with transcription regulated by repressors, activators, and small effector molecules.Structure and Function of OperonsAn operon is a cluster of structural genes transcribed together under the control of a single promoter. The promoter region...
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Microbial Nutrition01:28

Microbial Nutrition

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Organisms exhibit remarkable metabolic diversity, categorized based on how they acquire energy and carbon. These strategies enable survival in various ecological niches and are essential for maintaining energy flow and nutrient cycling within ecosystems.Energy and Carbon SourcesOrganisms are classified as phototrophs or chemotrophs based on energy acquisition. Phototrophs use light as their energy source, while chemotrophs rely on oxidizing chemical compounds. Further differentiation arises...
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Related Experiment Video

Updated: Aug 5, 2025

An In Vitro Batch-culture Model to Estimate the Effects of Interventional Regimens on Human Fecal Microbiota
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Alternative stable states, nonlinear behavior, and predictability of microbiome dynamics.

Hiroaki Fujita1, Masayuki Ushio2,3, Kenta Suzuki4

  • 1Center for Ecological Research, Kyoto University, Otsu, Shiga, 520-2133, Japan. fujita.h@ecology.kyoto-u.ac.jp.

Microbiome
|March 28, 2023
PubMed
Summary

Predicting microbiome shifts is now possible. Using statistical physics and nonlinear mechanics, researchers can forecast abrupt changes in microbial communities, crucial for health and industry.

Keywords:
Alternative stable statesBiodiversityBiological communitiesChaosCommunity collapseCommunity stabilityDysbiosisEmpirical dynamic modelingMicrobiome dynamicsNon-linear dynamics

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Area of Science:

  • Microbial ecology
  • Statistical physics
  • Non-linear dynamics

Background:

  • Microbiome dynamics are key indicators and drivers in human health, agriculture, and industry.
  • Predicting abrupt microbiome shifts, like dysbiosis, is challenging due to complex community structures.

Discussion:

  • This study integrates theoretical frameworks with empirical data to anticipate drastic microbial community changes.
  • Time-series data from 48 experimental microbiomes over 110 days were analyzed using statistical physics and non-linear mechanics.

Key Insights:

  • Abrupt microbiome changes can be characterized as shifts between alternative stable states or dynamics around complex attractors.
  • Microbiome collapses were successfully predicted using 'energy landscape' analysis from statistical physics and a stability index from non-linear mechanics.

Outlook:

  • Extending classic ecological concepts allows forecasting of abrupt events in species-rich microbial systems.
  • This predictive capability is vital for managing microbiome stability in various applications.