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Related Concept Videos

Regulation of Expression Occurs at Multiple Steps02:24

Regulation of Expression Occurs at Multiple Steps

Gene expression can be regulated at almost every step from gene to protein. Transcription is the step that is most commonly regulated. This involves the binding of proteins to short regulatory sequences on the DNA. This association can either promote or inhibit the transcription of a gene associated with the respective sequence.
Transcription results in the generation of precursor (pre-mRNA) that consists of both exons and introns, which needs further processing before being translated to a...
Regulation of Expression Occurs at Multiple Steps02:24

Regulation of Expression Occurs at Multiple Steps

Gene expression can be regulated at almost every step from gene to protein. Transcription is the step that is most commonly regulated. This involves the binding of proteins to short regulatory sequences on the DNA. This association can either promote or inhibit the transcription of a gene associated with the respective sequence.
Transcription results in the generation of precursor (pre-mRNA) that consists of both exons and introns, which needs further processing before being translated to a...
Regulation of Expression at Multiple Steps01:23

Regulation of Expression at Multiple Steps

The gene expression in cells is regulated at different stages: (i) transcription, (ii) RNA processing, (iii) RNA localization, and (iv) translation. Transcriptional regulation is mediated by regulatory proteins such as transcription factors, activators, or repressors—these control gene expression by initiating or inhibiting the transcription of genes. Once a precursor or pre-mRNA is produced, it undergoes post-transcriptional modification, including 5' capping, splicing, and the addition of a...
Somatic to iPS Cell Reprogramming01:29

Somatic to iPS Cell Reprogramming

Reprogramming alters the gene expression in somatic cells, transforming them into induced pluripotent stem (iPS) cells over several generations. Scientists can reprogram cells by introducing genes for four transcription factors—Oct4, Sox2, Klf4, and c-Myc (OSKM) by viral or non-viral methods. These factors are also known as Yamanaka factors after Shinya Yamanaka, who first generated iPS cells using mouse skin cells. Yamanaka was awarded the Nobel Prize in Physiology or Medicine in 2012 for this...
Neuroplasticity01:01

Neuroplasticity

Neuroplasticity reflects the brain's remarkable capacity to adapt and evolve, responding dynamically to learning, experiences, or injury by reorganizing its neural circuitry. This reorganization involves creating new neural connections and refining old ones through a series of biological processes that contribute to the brain's lifelong development and adaptability.
Neural Regulation01:37

Neural Regulation

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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Updated: Jul 4, 2026

Experience-Dependent Remodeling of Juvenile Brain Olfactory Sensory Neuron Synaptic Connectivity in an Early-Life Critical Period
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PARROT: Phase-Altering Regulatory Rewiring Over Time.

Chen Chen1, Megha Padi2, John Quackenbush1

  • 1Department of Biostatistics, Harvard T.H. Chan School of Public Health, USA.

Biorxiv : the Preprint Server for Biology
|July 3, 2026
PubMed
Summary

We developed PARROT, a new method to detect changes in gene regulatory networks over time. It identifies critical transition points and reveals how gene communities and connections reconfigure during development and disease.

Keywords:
change-point detectioncommunity detectiondynamic networksgene regulatory networksstochastic block model

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Area of Science:

  • Computational Biology
  • Network Science
  • Genomics

Background:

  • Gene regulatory networks (GRNs) dynamically change during development and disease.
  • Understanding these network dynamics is key to deciphering biological transitions.
  • Current methods often fail to account for network structure or provide interpretable community information.

Purpose of the Study:

  • To introduce PARROT (Phase-Altering Regulatory Rewiring Over Time), a novel framework for change-point detection in dynamic networks.
  • To improve the identification of critical temporal transitions in GRNs.
  • To provide interpretable community structure alongside change-point detection.

Main Methods:

  • Utilized Stochastic Block Models (SBMs) for network analysis.
  • Developed a framework to jointly estimate change-point locations and community structure.
  • Applied to unipartite and bipartite networks with Gaussian or Bernoulli edge models.

Main Results:

  • PARROT demonstrated superior performance in simulations, outperforming existing methods in change-point detection and community recovery.
  • Successfully identified known phase boundaries in human cardiac differentiation and mouse lung development data.
  • PARROT pinpoints gene reassignments and changes in inter-module connections across different network states.

Conclusions:

  • PARROT offers a robust and interpretable approach for analyzing dynamic gene regulatory networks.
  • The framework enhances understanding of developmental and disease-related network restructuring.
  • Available as an R package for broader scientific application.