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Proteomics

Proteomics

A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term proteomics...

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Related Experiment Video

Updated: Jun 8, 2026

Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification

Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification

Published on: November 15, 2017

Modeling experimental design for proteomics.

Jan Eriksson¹, David Fenyö

¹Department of Chemistry, Swedish University of Agricultural Sciences, Uppsala, Sweden. Jan.Eriksson@kemi.slu.se

Methods in Molecular Biology (Clifton, N.J.)

|September 14, 2010

Summary

This summary is machine-generated.

Proteomics experiments require careful design due to proteome complexity. Computer simulations of experimental models can optimize these designs for better results.

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A Streamlined Approach for Mass Spectrometry-Based Proteomics Using Selected Tissue Regions

A Streamlined Approach for Mass Spectrometry-Based Proteomics Using Selected Tissue Regions

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Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification

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A Streamlined Approach for Mass Spectrometry-Based Proteomics Using Selected Tissue Regions

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Area of Science:

Proteomics
Bioinformatics
Experimental Design

Background:

Proteome complexity necessitates rigorous experimental planning.
Effective investigation of proteomes is crucial in biological research.

Purpose of the Study:

To present a framework for modeling proteomics experiments.
To demonstrate the utility of computer simulations in refining experimental designs.

Main Methods:

Developing computational models for proteomics experiments.
Utilizing computer simulations to test and optimize various design parameters.

Main Results:

Identified key factors influencing experimental outcomes through simulation.
Provided a method for enhancing the efficiency and reliability of proteomics studies.

Conclusions:

Modeling and simulation are powerful tools for improving proteomics experimental design.
Optimized designs lead to more robust and interpretable proteomic data.