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Entropy02:39

Entropy

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Salt particles that have dissolved in water never spontaneously come back together in solution to reform solid particles. Moreover, a gas that has expanded in a vacuum remains dispersed and never spontaneously reassembles. The unidirectional nature of these phenomena is the result of a thermodynamic state function called entropy (S). Entropy is the measure of the extent to which the energy is dispersed throughout a system, or in other words, it is proportional to the degree of disorder of a...
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Entropy01:18

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The first law of thermodynamics is quantitatively formulated via an equation relating the internal energy of a system, the heat exchanged by it, and the work done on it. A quantitative formulation of the second law of thermodynamics leads to defining a state function, the entropy.
When an ideal gas expands isothermally, the disorder in the gas increases. From the molecular perspective, the gas molecules have more volume to move around in.
Consider an infinitesimal step in the expansion, which...
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Standard Entropy Change for a Reaction03:00

Standard Entropy Change for a Reaction

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Entropy is a state function, so the standard entropy change for a chemical reaction (ΔS°rxn) can be calculated from the difference in standard entropy between the products and the reactants.
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Parametric Survival Analysis: Weibull and Exponential Methods01:14

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Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
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Exponential and Sinusoidal Signals01:18

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The exponential function is crucial for characterizing waveforms that rise and decay rapidly. This continuous-time exponential function is defined using exponential terms with constants α and A. When both constants are real, the function is represented as,
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Exponential Functions with Base e01:30

Exponential Functions with Base e

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Exponential functions with base e are essential for modeling continuous processes of growth and decay. The constant e, approximately 2.718, naturally arises in systems where change occurs proportionally to the current value. A positive exponent represents continuous growth, while a negative exponent represents continuous decay. These functions are especially useful for describing situations where change happens smoothly over time rather than in discrete steps.One clear example of exponential...
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Related Experiment Video

Updated: Feb 6, 2026

Bulk and Thin Film Synthesis of Compositionally Variant Entropy-stabilized Oxides
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Exponential equilibration of genetic circuits using entropy methods.

José A Cañizo1, José A Carrillo2, Manuel Pájaro3

  • 1Departamento de Matemática Aplicada, Universidad de Granada, 18071, Granada, Spain.

Journal of Mathematical Biology
|August 19, 2018
PubMed
Summary
This summary is machine-generated.

This study demonstrates that a continuum model for genetic circuits rapidly converges to equilibrium. Mathematical analysis and simulations confirm fast equilibration for single and multiple gene networks.

Keywords:
35B4039B9965M9992Dxx

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Area of Science:

  • Computational Biology
  • Mathematical Biology
  • Systems Biology

Background:

  • Genetic circuits are fundamental biological systems.
  • Continuum models approximate chemical master equations for genetic circuits.
  • Understanding circuit dynamics and convergence is crucial.

Purpose of the Study:

  • Analyze a continuum model for genetic circuits.
  • Prove exponentially fast convergence to equilibrium using entropy methods.
  • Investigate asymptotic equilibration in multi-gene networks.

Main Methods:

  • Analysis of a partial integro-differential equation model.
  • Application of entropy methods for convergence proofs.
  • Numerical simulations for network dynamics.

Main Results:

  • Demonstrated exponentially fast convergence to equilibrium for the continuum model.
  • Established explicit bounds for convergence.
  • Confirmed asymptotic equilibration for multi-gene networks under specific conditions.

Conclusions:

  • The continuum model exhibits rapid and predictable convergence to equilibrium.
  • Entropy methods provide rigorous analytical tools for genetic circuit models.
  • Numerical simulations support theoretical findings for complex genetic networks.