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BoolFilter: an R package for estimation and identification of partially-observed Boolean dynamical systems.

Levi D Mcclenny1, Mahdi Imani2, Ulisses M Braga-Neto2

  • 1Electrical and Computer Engineering Department, College Station, Texas, USA. levimcclenny@tamu.edu.

BMC Bioinformatics
|November 28, 2017
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Summary
This summary is machine-generated.

This study introduces BoolFilter, an R package for Partially-Observed Boolean Dynamical Systems (POBDS). It enables gene regulatory network analysis from noisy transcriptomic data, improving bioinformatics research.

Keywords:
Boolean Kalman FilterGene expression analysisGene regulatory networksNetwork inferencePartially-Observed Boolean Dynamical SystemsParticle filter

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Area of Science:

  • Computational Biology
  • Systems Biology
  • Bioinformatics

Background:

  • Gene regulatory networks control essential cellular processes.
  • Boolean networks model gene interactions but rely on directly observable states.
  • Transcriptomic data is indirectly and incompletely measured, posing challenges for Boolean network models.

Purpose of the Study:

  • Introduce the R package BoolFilter for Partially-Observed Boolean Dynamical Systems (POBDS).
  • Provide a computational tool for analyzing gene regulatory networks from noisy transcriptomic data.
  • Address the limitations of existing Boolean network models in handling indirect measurements.

Main Methods:

  • Implementation of the POBDS model in the BoolFilter R package.
  • Utilizes exact and approximated (particle) filters for state and parameter estimation.
  • Leverages network interface from the BoolNet R package for compatibility.

Main Results:

  • BoolFilter enables estimation of Boolean states and network topology from time-series transcriptomic data.
  • The package facilitates simulation of transcriptomic data based on Boolean network models.
  • It handles uncertainty in measurement processes inherent in transcriptomic analysis.

Conclusions:

  • BoolFilter offers a robust toolbox for the bioinformatics community.
  • Provides state-of-the-art algorithms for simulating and identifying gene regulatory systems.
  • Supports various expression technologies including cDNA microarrays, RNA-Seq, and cell imaging.