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FlowMax: A Computational Tool for Maximum Likelihood Deconvolution of CFSE Time Courses.

Maxim Nikolaievich Shokhirev1, Alexander Hoffmann

  • 1Signaling Systems Laboratory, Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, California, United States of America ; San Diego Center for Systems Biology, La Jolla, California, United States of America ; Graduate Program in Bioinformatics and Systems Biology, University of California San Diego, La Jolla, California, United States of America.

Plos One
|July 5, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces FlowMax, a computational tool to objectively analyze noisy cell division data from dye-dilution experiments. It improves interpretation of immune cell dynamics and reveals new insights into B cell proliferation.

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Area of Science:

  • Immunology
  • Computational Biology
  • Systems Biology

Background:

  • Immune responses involve complex, multi-cellular dynamics.
  • Lymphocyte population dynamics are driven by individual cell processes like activation and division.
  • Interpreting dye-dilution experiments is challenging due to cell-to-cell variability and technical noise.

Purpose of the Study:

  • To develop a computational methodology for objective interpretation of noisy dye-dilution data.
  • To create a tool that accounts for technical and biological variability in cell population modeling.
  • To analyze B cell responses in NFκB knockout mice.

Main Methods:

  • Developed a computational methodology to parameterize cell population models using noisy dye-dilution data.
  • Estimated fit sensitivity and redundancy by sampling the solution landscape and clustering.
  • Integrated a cell fluorescence model to account for technical and biological variability.
  • Incorporated the methodology into an integrated phenotyping tool, FlowMax.

Main Results:

  • FlowMax enables objective interpretation of model fits by estimating sensitivity and redundancy.
  • The methodology improves fit accuracy by accounting for both technical and biological variability.
  • Analyzed B cells from NFκB knockout mice, confirming known findings and revealing a novel phenotype for nfkb1/p105/50 in limiting B cell proliferation.
  • Achieved results with reduced effort and cost compared to previous methods.

Conclusions:

  • The developed computational methodology and FlowMax tool provide objective and quantitative conclusions for dye-dilution studies.
  • FlowMax enhances the analysis of immune cell dynamics, particularly B cell proliferation.
  • The tool is suitable for high-throughput analysis in clinical and pharmacological screening.