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Related Concept Videos

Position-effect Variegation02:32

Position-effect Variegation

In 1928, a German botanist Emil Heitz observed the moss nuclei with a DNA binding dye. He observed that while some chromatin regions decondense and spread out in the interphase nucleus, others do not. He termed them euchromatin and heterochromatin, respectively. He proposed that the heterochromatin regions reflect a functionally inactive state of the genome. It was later confirmed that heterochromatin is transcriptionally repressed, and euchromatin is transcriptionally active chromatin.
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Transdifferentiation, also known as lineage reprogramming, was first discovered by Selman and Kafatos in 1974 in silkmoths. They observed that the moths’ cuticle-producing cells transformed into salt-producing cells. Many such cases of natural transdifferentiation occur in organisms. In humans, pancreatic alpha cells can become beta cells. In newts, the loss of the eye’s lens causes the pigmented epithelial cells to transdifferentiate into the lens cells.
Artificial transdifferentiation occurs...

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Related Experiment Video

Updated: Jun 6, 2026

Detection of Trypanosoma brucei Variant Surface Glycoprotein Switching by Magnetic Activated Cell Sorting and Flow Cytometry
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Identification from stochastic cell-to-cell variation: a genetic switch case study.

B Munsky1, M Khammash

  • 1Los Alamos National Laboratory, Center for Nonlinear Studies and Computer, Computational and Statistical Sciences Division, Los Alamos, NM, USA. brain.munsky@gmail.com

IET Systems Biology
|November 16, 2010
PubMed
Summary
This summary is machine-generated.

Cellular variability arises from random gene, RNA, and protein interactions. Measuring variability in both LacI and cI proteins can help identify parameters in stochastic gene regulatory models.

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Area of Science:

  • Systems Biology
  • Computational Biology
  • Molecular Biology

Background:

  • Cellular processes exhibit inherent randomness, leading to significant cell-to-cell variability.
  • Stochastic gene regulatory models aim to capture this variability, but parameter identification remains challenging.
  • Techniques like flow cytometry enable quantitative measurement of cell-to-cell variability.

Purpose of the Study:

  • To investigate the identifiability of parameters for a stochastic genetic toggle switch model.
  • To determine if statistical information from protein expression can constrain model parameters.
  • To propose experiments for parameter identification in gene regulatory networks.

Main Methods:

  • Utilized finite state projection approaches for model analysis.
  • Explored parameter identification using mean expression levels, LacI protein distributions, and LacI-cI multivariate distributions.
  • Simulated data to computationally investigate parameter identifiability.

Main Results:

  • Parameter identification was not achievable using only LacI protein variability.
  • Parameters of the genetic toggle switch model were identifiable when considering cell-to-cell variability in both LacI and cI proteins.
  • Simulated data guided the proposal of specific experimental designs.

Conclusions:

  • Simultaneous measurement of multiple protein variabilities is crucial for accurate stochastic model parameterization.
  • Finite state projection is a viable method for exploring parameter identifiability in gene regulatory models.
  • Proposed experiments offer a pathway for validating model predictions and understanding gene network dynamics.