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Computation and measurement of cell decision making errors using single cell data.

Iman Habibi1, Raymond Cheong2, Tomasz Lipniacki3

  • 1Center for Wireless Information Processing, Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, NJ, United States of America.

Plos Computational Biology
|April 6, 2017
PubMed
Summary

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This summary is machine-generated.

This study introduces a new computational method to quantify cell decision errors like false alarms and misses, crucial for understanding signaling pathway failures. The method accurately models these errors in both normal and abnormal cells, offering better insights than previous metrics.

Area of Science:

  • Computational biology
  • Systems biology
  • Cellular signaling

Background:

  • Cellular decision-making is prone to errors due to noise and signaling failures.
  • The tumor necrosis factor (TNF) signaling pathway, regulating Nuclear Factor κB (NF-κB), is a key system for studying these errors.

Purpose of the Study:

  • To develop and validate a novel computational method for quantifying cell decision errors.
  • To analyze false alarm and miss probabilities in the TNF-NF-κB pathway under normal and abnormal conditions.

Main Methods:

  • Development of a new computational method to quantify cell decision errors.
  • Analysis of single-cell experimental data from the TNF-NF-κB pathway.
  • Computation of false alarm and miss error probabilities.

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Main Results:

  • Identified and quantified two types of cell decision errors: false alarms and misses.
  • Formulated the dependence of error probabilities on signal transduction noise levels.
  • Demonstrated the method's ability to model and measure decision-making alterations in abnormal cells, such as A20-deficient cells.

Conclusions:

  • The developed method accurately models cell decision-making errors in normal and abnormal conditions, considering noise uncertainty.
  • The new decision error metrics provide a more accurate characterization of signaling failures compared to the pathway capacity metric.