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Related Concept Videos

Mass Spectrometry: Complex Analysis01:21

Mass Spectrometry: Complex Analysis

Mass spectrometry is an important technique for the identification of pure compounds. However, it has some limitations for the analysis of complex mixtures, often due to excessive fragmentation making the spectrum too complicated to decipher. Mass spectrometry can be combined with suitable separation methods in sequence, forming hyphenated methods, which are useful in the analysis of complex mixtures.
GC–MS is a powerful hyphenated method commonly used in forensics and environmental...
Thermal Sigmatropic Reactions: Overview01:16

Thermal Sigmatropic Reactions: Overview

Sigmatropic rearrangements are a class of pericyclic reactions in which a σ bond migrates from one part of a π system to another. These are intramolecular rearrangements where the total number of σ and π bonds remain unchanged.
Sigmatropic shifts are classified based on an order term [i, j ], where i and j indicate the number of atoms across which each end of the σ bond migrates. Below are examples of a [3,3] sigmatropic shift in 1,5-hexadiene, referred to as...
Mass Spectrometry: Overview01:19

Mass Spectrometry: Overview

Mass spectrometry is an analytical technique used to determine the molecular mass and molecular formula of a compound. The basic principle of mass spectrometry is to generate ions from the analyte molecule and measure these ion abundances against their molecular mass. One common type of ionization, known as electron ionization or EI, bombards the analyte molecules in the gas phase with high-energy electron beams. The electron beams displace an electron from the molecule and leave behind a...

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Related Experiment Video

Updated: May 21, 2026

Simultaneous Affinity Enrichment of Two Post-Translational Modifications for Quantification and Site Localization
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Simultaneous Affinity Enrichment of Two Post-Translational Modifications for Quantification and Site Localization

Published on: February 27, 2020

LambdaPy and LambdaR: Thermodynamics-Based Biogeochemical Reaction Modeling Packages for Integrating High-Resolution

Manokaran Veeramani1, Sanjog Kharel1, Hugh C McCullough1

  • 1University of Nebraska-Lincoln, Lincoln, NE, USA.

Methods in Molecular Biology (Clifton, N.J.)
|May 19, 2026
PubMed
Summary
This summary is machine-generated.

Substrate-explicit thermodynamic modeling (SXTM) simplifies biogeochemical modeling by automatically creating reaction models from organic matter chemical formulas. This method, demonstrated with river data, aids understanding of microbial roles in biogeochemical cycles.

Keywords:
Biogeochemical modelingFTICR-MSOrganic matterSubstrate-explicit modelingThermodynamics

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Atmospheric-pressure Molecular Imaging of Biological Tissues and Biofilms by LAESI Mass Spectrometry
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Atmospheric-pressure Molecular Imaging of Biological Tissues and Biofilms by LAESI Mass Spectrometry

Published on: September 3, 2010

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Last Updated: May 21, 2026

Simultaneous Affinity Enrichment of Two Post-Translational Modifications for Quantification and Site Localization
12:11

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Published on: February 27, 2020

Atmospheric-pressure Molecular Imaging of Biological Tissues and Biofilms by LAESI Mass Spectrometry
09:22

Atmospheric-pressure Molecular Imaging of Biological Tissues and Biofilms by LAESI Mass Spectrometry

Published on: September 3, 2010

Area of Science:

  • Environmental microbiology
  • Biogeochemistry
  • Computational modeling

Background:

  • Microorganisms drive essential biogeochemical cycles.
  • Microbial reactions depend on biotic and abiotic factors like microbe interactions and chemical traits.
  • Modeling complex organic matter (OM) pools is challenging.

Purpose of the Study:

  • To provide guidance on substrate-explicit thermodynamic modeling (SXTM) for biogeochemical reactions.
  • To demonstrate SXTM's utility in formulating models from ultra-high-resolution mass spectrometry data.
  • To introduce LambdaPy and LambdaR software for implementing SXTM.

Main Methods:

  • SXTM automatically formulates stoichiometric and kinetic biogeochemical models from organic matter chemical formulas.
  • The approach requires only two parameters: maximum growth rate (μmax) and harvest volume (Vh).
  • SXTM has been extended beyond aerobic respiration to include other electron acceptors.

Main Results:

  • SXTM enables efficient modeling of complex OM pools, even with thousands of identified compounds.
  • A tutorial using river corridor OM data from the WHONDRS consortium is provided.
  • Software packages LambdaPy (Python) and LambdaR (R) are available for SXTM implementation.

Conclusions:

  • SXTM significantly facilitates the modeling of microbially driven biogeochemical cycling.
  • This approach enhances understanding of the interactions between microbes, enzymes, and OM.
  • Integration with microbial- and enzyme-explicit modeling will further advance research.