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Related Concept Videos

Cyclic Processes And Isolated Systems01:19

Cyclic Processes And Isolated Systems

A thermodynamic system with zero heat exchange and work is an isolated system. For these systems, the internal energy remains constant.
In the case of a non-isolated system, the change in the internal energy is zero only if the process is cyclic. A thermodynamic process is considered cyclic if the system undergoes a series of changes and returns to its initial state. 
Consider a cyclic process that returns to its initial state, undergoing a four-step process. The heat transfer along each path...
Classification of Systems-I01:26

Classification of Systems-I

Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
Entropy Changes Accompanying Specific Processes01:21

Entropy Changes Accompanying Specific Processes

Entropy, a measure of disorder in a system, changes during phase transitions like freezing or boiling. At the transition temperature Ttrs, where two phases are in equilibrium, the phase transition is a reversible process. The entropy change can be calculated from a substance's enthalpy of transition using the equation ΔStrs = ΔtrsH /Ttrs.When a perfect gas expands isothermally from one volume to another, entropy increases logarithmically with volume. Conversely, isothermal compression results...
Neural Circuits01:25

Neural Circuits

Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
Modeling with Differential Equations01:25

Modeling with Differential Equations

Population dynamics can be described mathematically by considering the population size P(t) as a function of time. The rate of change of the population is then represented by the derivative of P(t). A simple assumption is that the rate of growth is proportional to the size of the population itself. This leads to an exponential growth model, where the population increases rapidly without bound. While this is a useful first approximation, it does not reflect realistic long-term...
State Space Representation01:27

State Space Representation

The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...

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Related Experiment Video

Updated: May 24, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

Dynamics and processing in finite self-similar networks.

Simon DeDeo1, David C Krakauer

  • 1Santa Fe Institute, Santa Fe, NM 87501, USA. simon@santafe.edu

Journal of the Royal Society, Interface
|March 2, 2012
PubMed
Summary

Biological networks exhibit self-similarity. Their topology, whether branching or looped, significantly impacts signal propagation and noise response, with small-world networks showing surprising modularity.

Area of Science:

  • Complex systems biology
  • Network science
  • Systems biology

Background:

  • Biological networks, from molecular to ecological scales, commonly display self-similar geometric properties.
  • These networks exhibit diverse topologies, including branching and loop-like structures, influencing their functional characteristics.

Purpose of the Study:

  • To analyze the relationship between network topology and signal propagation across different network classes.
  • To investigate how network structures respond to noise and influence system integration.

Main Methods:

  • Comparative analysis of network topologies with varying features (branching vs. loop-like).
  • Examination of signal containment and propagation dynamics within these networks.
  • Assessment of network response to varying levels of noise.

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ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data
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ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

Published on: January 16, 2019

Related Experiment Videos

Last Updated: May 24, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data
05:12

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

Published on: January 16, 2019

Main Results:

  • Network topology (branching vs. loop-like) dictates signal propagation capabilities.
  • Different network types exhibit contrasting responses to noise, affecting integration.
  • Small-world networks, despite short diameters, may display slower dynamics and increased modularity.

Conclusions:

  • Network topology is a critical determinant of signaling efficiency and noise resilience in biological systems.
  • Mesoscopic properties of networks, not just infinite-limit behaviors, are crucial for understanding biological phenomena.
  • Findings offer insights into the design principles of biological networks across scales.