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Related Concept Videos

Positive Regulator Molecules02:39

Positive Regulator Molecules

Mitotic cell division results in daughter cells that exactly resemble the parent cell. However, errors in the DNA replication or distribution of genetic material may lead to genetic mutations that may be passed down to every new cell formed from the resulting abnormal cell. Propagation of such mutant cells is restricted through checkpoint mechanisms present at different stages of the cell cycle. These checkpoints involve regulator molecules that either promote or demote cell cycle events.
Positive Regulator Molecules02:39

Positive Regulator Molecules

Mitotic cell division results in daughter cells that exactly resemble the parent cell. However, errors in the DNA replication or distribution of genetic material may lead to genetic mutations that may be passed down to every new cell formed from the resulting abnormal cell. Propagation of such mutant cells is restricted through checkpoint mechanisms present at different stages of the cell cycle. These checkpoints involve regulator molecules that either promote or demote cell cycle events.
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Positive Regulator Molecules

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Digestion begins with a cephalic phase that prepares the digestive system to receive food. When our brain processes visual or olfactory information about food, it triggers impulses in the cranial nerves innervating the salivary glands and stomach to prepare for food.
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Signal Flow Graphs01:18

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Related Experiment Video

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

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Published on: December 7, 2021

Detecting controlling nodes of boolean regulatory networks.

Steffen Schober1, David Kracht, Reinhard Heckel

  • 1Institute of Telecommunications and Applied Information Theory, Ulm University, Ulm, Germany. steffen.schober@uni-ulm.de.

EURASIP Journal on Bioinformatics & Systems Biology
|October 13, 2011
PubMed
Summary

This study introduces spectral techniques to efficiently detect controlling nodes in Boolean regulatory networks. These methods offer improved time and sample complexity compared to exhaustive search, particularly for unate networks.

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Area of Science:

  • Computational Biology
  • Systems Biology
  • Network Science

Background:

  • Boolean models are foundational for understanding gene regulatory networks.
  • Network perturbations highlight the importance of identifying critical controlling nodes.
  • Specific classes of Boolean functions (e.g., unate) possess unique Fourier domain properties.

Purpose of the Study:

  • To investigate the detection of controlling nodes in Boolean networks using spectral techniques.
  • To analyze networks with unbalanced functions and low average sensitivity.
  • To explore the efficacy of spectral methods for 1-low networks, including unate and nested canalyzing functions.

Main Methods:

  • Application of spectral learning algorithms to Boolean networks.
  • Analysis of networks with unbalanced functions and average sensitivity < 2/3k.
  • Focus on the class of 1-low networks (unate, linear threshold, nested canalyzing).
  • Development of analytical bounds for sample complexity.

Main Results:

  • Spectral techniques significantly improve time and sample complexity for detecting controlling nodes over exhaustive search.
  • Analytical upper bounds are derived for the number of samples required.
  • Improved algorithms for large-scale unate networks are presented and validated numerically.

Conclusions:

  • Spectral methods provide a more efficient approach for identifying controlling nodes in Boolean regulatory networks.
  • The findings are particularly relevant for analyzing complex biological systems with specific function classes.
  • This work advances computational tools for systems biology research.