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Related Concept Videos

Calculation of Electric Flux01:25

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Consider the electric field of an oppositely charged, parallel-plate system and an imaginary box between those plates. Let the bottom face of the box be ABCD, and the top face be FGHK. The electric field between the plates is uniform and points from the positive plate toward the negative plate. The calculation of this field's flux through the box's various faces shows that the net flux through the box is zero. Why does the flux cancel out here?
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The concept of flux describes how much of something goes through a given area. More formally, it is the dot product of a vector field within an area. For a better understanding, consider an open rectangular surface with a small area that is placed in a uniform electric field. The larger the area, the more field lines go through it and, hence, the greater the flux; similarly, the stronger the electric field (represented by a greater density of lines), the greater the flux. On the other hand, if...
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Updated: Oct 13, 2025

Identifying Per- and Polyfluorinated Chemical Species with a Combined Targeted and Non-Targeted-Screening High-Resolution Mass Spectrometry Workflow
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EFMlrs: a Python package for elementary flux mode enumeration via lexicographic reverse search.

Bianca A Buchner1,2, Jürgen Zanghellini3

  • 1Department of Biotechnology, University of Natural Resources and Life Sciences, Vienna, Austria.

BMC Bioinformatics
|November 11, 2021
PubMed
Summary
This summary is machine-generated.

EFM analysis of large metabolic networks is now feasible using EFMlrs, a Python package that leverages the parallel mplrs algorithm for high-performance computing. This tool enables comprehensive metabolic network characterization previously limited by computational challenges.

Keywords:
CobrapyElementary modesLexicographic reverse searchMetabolic modellingMplrsSystems biology

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Area of Science:

  • Metabolic Network Analysis
  • Computational Biology
  • Systems Biology

Background:

  • Elementary flux mode (EFM) analysis is crucial for metabolic network characterization but computationally intensive.
  • Existing EFM algorithms lack scalability, limiting analysis to medium-scale networks on single servers.
  • The parallel mplrs algorithm offers potential for EFM analysis on high-performance computing (HPC) environments.

Purpose of the Study:

  • To evaluate the applicability of the mplrs algorithm for elementary flux mode enumeration.
  • To develop a user-friendly tool for EFM analysis on large-scale metabolic networks.

Main Methods:

  • Developed EFMlrs, a Python package integrating COBRApy for metabolic model processing.
  • EFMlrs compresses stoichiometric matrices and generates inputs for both mplrs and efmtool.
  • The package supports additional reaction boundaries and integrates into existing computational workflows.

Main Results:

  • EFMlrs provides access to the enumeration capabilities of the parallel mplrs algorithm.
  • The developed package supports established EFM enumeration tools alongside the new method.
  • EFMlrs facilitates the analysis of significantly larger metabolic models than previously possible.

Conclusions:

  • The mplrs algorithm's properties make it well-suited for HPC, enabling efficient EFM analysis.
  • EFMlrs offers new possibilities for unbiased analysis of large-scale metabolic models.
  • EFMlrs is an open-source, easily installable program with a dedicated workflow.