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Flow Cytometry01:23

Flow Cytometry

The development of flow cytometry techniques began in 1934 with initial attempts by Andrew Moldavan, a bacteriologist who counted the cells in a flowing capillary system. Moldavan pumped cells through a capillary tube focused under a microscope for visualization. The invention of photometry allowed the measurement of differentially-stained cells, and Louis Kamentsky developed the first multiparameter flow cytometer in 1965 to identify and count the cancer cells in cervical tissue specimens.
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A structured population modeling framework for quantifying and predicting gene expression noise in flow cytometry

Kevin B Flores1

  • 1Center for Research in Scientific Computation, Department of Mathematics, North Carolina State University, Raleigh, NC, United States.

Applied Mathematics Letters
|June 25, 2013
PubMed
Summary
This summary is machine-generated.

A new structured population model explains gene expression noise using flow cytometry data. Cellular switching and transcriptional re-initiation are key factors identified for accurate modeling of gene expression noise.

Keywords:
Structured population modelsdistributed parametersgene expression noisegene regulatory networkssynthetic biology

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Area of Science:

  • Systems Biology
  • Molecular Biology
  • Quantitative Biology

Background:

  • Gene expression noise is a critical factor influencing cellular function and response.
  • Understanding the sources of gene expression noise is essential for fields like synthetic biology and developmental biology.
  • Time-dependent flow cytometry provides valuable data for analyzing dynamic cellular processes.

Purpose of the Study:

  • To develop and validate a structured population model with distributed parameters.
  • To identify the key mechanisms contributing to gene expression noise in time-dependent flow cytometry data.
  • To accurately fit experimental gene expression data and capture qualitative noise features.

Main Methods:

  • Formulation of a structured population model with distributed parameters.
  • Validation of the model using cell population-level gene expression data from two synthetic eukaryotic cell experiments.
  • Quantitative fitting of the model to experimental data.

Main Results:

  • The developed model successfully captured qualitative noise features across both experiments.
  • The model accurately fitted the gene expression data from the first experiment.
  • The study identified cellular switching between expression states and transcriptional re-initiation as significant contributors to gene expression noise.

Conclusions:

  • A structured population model with distributed parameters is effective for analyzing gene expression noise.
  • Cellular switching and transcriptional re-initiation are crucial mechanisms for explaining observed gene expression noise.
  • The findings provide a framework for more accurate modeling of gene expression dynamics.