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Entropy within the Cell01:22

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A living cell's primary tasks of obtaining, transforming, and using energy to do work may seem simple. However, the second law of thermodynamics explains why these tasks are harder than they appear. None of the energy transfers in the universe are completely efficient. In every energy transfer, some amount of energy is lost in a form that is unusable. In most cases, this form is heat energy. Thermodynamically, heat energy is defined as the energy transferred from one system to another that...
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The second law of thermodynamics can be stated quantitatively using the concept of entropy. Entropy is the measure of disorder of the system.
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Emergent phenomena in living systems:A statistical mechanical perspective.

Indrani Bose1

  • 1Department of Physics, Bose Institute, Kolkata, India.

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Biological systems exhibit emergent phenomena driven by both deterministic and stochastic dynamics. Noise-induced transitions, akin to physical phase transitions, are observed in gene expression and population genetics models.

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Area of Science:

  • Systems biology
  • Statistical mechanics
  • Nonlinear dynamics

Background:

  • Living systems' phenomena arise from biological network dynamics, involving both deterministic and stochastic elements.
  • Stochastic processes like gene expression and cell differentiation introduce molecular-level noise.
  • Nonlinear interactions amplify noise, leading to emergent phenomena and complex system behaviors.

Purpose of the Study:

  • To review emergent phenomena in biological systems that resemble phase transitions in physical systems.
  • To focus on noise-induced transitions in stochastic models of gene expression and population genetics.
  • To illustrate the application of statistical mechanics in bridging theoretical models and experimental biological observations.

Main Methods:

  • Review of theoretical models incorporating stochastic dynamics.
  • Analysis of noise-induced transitions in gene expression and population genetics models.
  • Comparison of biological phenomena with equilibrium and non-equilibrium phase transitions in physics.

Main Results:

  • Stochastic models reveal noise-induced transitions absent in deterministic models, particularly in gene expression and population genetics.
  • Some transitions exhibit critical-point phenomena within the mean-field Ising universality class.
  • Biological examples like biofilms and tissue homeostasis show behaviors analogous to critical phenomena.

Conclusions:

  • Statistical mechanics provides a framework for understanding emergent phenomena in biological systems.
  • Noise plays a crucial role in driving transitions and generating complexity in living systems.
  • The study highlights the power of physics-based models in explaining biological observations.