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Related Concept Videos

DNA Microarrays02:34

DNA Microarrays

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Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...
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Temporal Quantification of MAPK Induced Expression in Single Yeast Cells
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Measuring information flow in cellular networks by the systems biology method through microarray data.

Bor-Sen Chen1, Cheng-Wei Li1

  • 1Laboratory of Control and Systems Biology, Department of Electrical Engineering, National Tsing Hua University Hsinchu, Taiwan.

Frontiers in Plant Science
|June 18, 2015
PubMed
Summary
This summary is machine-generated.

Measuring cellular information flow is challenging. This study introduces a systems biology method using microarray data to indirectly estimate signal transductivity in signaling pathways and information transductivity in gene regulatory networks (GRN).

Keywords:
gene regulatory networkinformation flowinformation transductivitymicroarray datasignal transduction pathwaysignal transductivitysystem theory

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Area of Science:

  • Systems Biology
  • Computational Biology
  • Molecular Biology

Background:

  • Direct measurement of information flow in cellular networks is difficult.
  • Cellular communication relies on complex signal transduction pathways and gene regulatory networks (GRN).
  • Understanding information flow is crucial for deciphering cellular functions and dysfunctions.

Purpose of the Study:

  • To develop and apply a systems biology method for indirectly measuring information flow in cellular networks.
  • To quantify signal transductivity in signaling pathways and information transductivity in GRNs.
  • To analyze differences in information flow between normal and leukemia cancer cells.

Main Methods:

  • Utilized an information flow model and microarray data.
  • Employed a recursive least square parameter estimation algorithm to identify system parameters.
  • Applied systems theory to estimate signal and information transductivities.

Main Results:

  • Successfully estimated signal transductivities from extracellular signals to downstream proteins.
  • Quantified information transductivities within the GRN between transcription factors.
  • Observed substantial changes in signal transductivity from normal to leukemia cells, indicating systematic dysfunction.

Conclusions:

  • The proposed method enables indirect estimation of information flow using high-throughput data like microarrays, next-generation sequencing, or proteomics.
  • Significant alterations in signal transduction pathways are associated with leukemia.
  • This approach provides insights into cellular network dysfunctions in diseases like cancer.