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Related Concept Videos

Stability01:28

Stability

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...
Pole and System Stability01:24

Pole and System Stability

The transfer function is a fundamental concept representing the ratio of two polynomials. The numerator and denominator encapsulate the system's dynamics. The zeros and poles of this transfer function are critical in determining the system's behavior and stability.
Simple poles are unique roots of the denominator polynomial. Each simple pole corresponds to a distinct solution to the system's characteristic equation, typically resulting in exponential decay terms in the system's response.
Control System Problem01:21

Control System Problem

In an open-loop system, such as a basic thermostat, the poles of the transfer function influence the system's response but do not determine its stability. However, when feedback is introduced to form a closed-loop system, such as an advanced thermostat that adjusts heating based on room temperature, stability is governed by the new poles of the closed-loop transfer function.
When forming a closed-loop system, issues can arise if the poles cross into the unstable region, leading to potential...
BIBO stability of continuous and discrete -time systems01:24

BIBO stability of continuous and discrete -time systems

System stability is a fundamental concept in signal processing, often assessed using convolution. For a system to be considered bounded-input bounded-output (BIBO) stable, any bounded input signal must produce a bounded output signal. A bounded input signal is one where the modulus does not exceed a certain constant at any point in time.
To determine the BIBO stability, the convolution integral is utilized when a bounded continuous-time input is applied to a Linear Time-Invariant (LTI) system.
Stability of Equilibrium Configuration: Problem Solving01:13

Stability of Equilibrium Configuration: Problem Solving

The stability of equilibrium configurations is an important concept in physics, engineering, and other related fields. In simple terms, it refers to the tendency of an object or system to return to its equilibrium position after being disturbed. The stability of an equilibrium configuration can be analyzed by considering the potential energy function of the system and examining its behavior near the equilibrium point.
Problem-solving in the context of the stability of equilibrium configuration...
Multimachine Stability01:25

Multimachine Stability

Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:

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Age-dependent Dynamics of Locomotion in Caenorhabditis elegans: A Lyapunov Exponent Analysis
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Algorithms and complexity analyses for control of singleton attractors in Boolean networks.

Morihiro Hayashida1, Takeyuki Tamura, Tatsuya Akutsu

  • 1Bioinformatics Center, Laboratory of Biological Information Networks, Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto, Japan.

EURASIP Journal on Bioinformatics & Systems Biology
|September 17, 2008
PubMed
Summary

We developed algorithms to control singleton attractors in Boolean networks (BNs), which model genetic networks. Gene ordering significantly impacts computational time, with internal nodes prioritized leading to faster results.

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Area of Science:

  • Systems Biology
  • Computational Biology
  • Network Science

Background:

  • Boolean networks (BNs) are mathematical models used to represent complex genetic regulatory networks.
  • Controlling specific states, such as singleton attractors, is crucial for understanding and manipulating network behavior.

Purpose of the Study:

  • To propose and analyze novel algorithms for the effective control of singleton attractors in Boolean networks.
  • To investigate the impact of gene ordering on the computational efficiency of these control algorithms.

Main Methods:

  • Development of several algorithms designed for controlling singleton attractors in Boolean networks.
  • Theoretical estimation and experimental validation of the average-case time complexities for the proposed algorithms.
  • Comparative analysis of gene ordering strategies, focusing on the placement of internal versus external nodes.

Main Results:

  • The proposed algorithms demonstrate effectiveness in controlling singleton attractors in Boolean networks.
  • Gene ordering is identified as a critical factor influencing computational time; prioritizing internal nodes significantly reduces execution time.
  • A heuristic algorithm is presented, offering a trade-off between solution optimality and computational efficiency.

Conclusions:

  • The study highlights the importance of strategic gene ordering in Boolean network control algorithms.
  • Prioritizing internal nodes in the ordering process leads to more computationally efficient control strategies.
  • The developed algorithms and heuristic approach provide valuable tools for analyzing and controlling genetic networks.