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Related Concept Videos

Proteomics01:33

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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¹³C NMR: Distortionless Enhancement by Polarization Transfer (DEPT)

When proton-coupled carbon-13 spectra are simplified by a broadband proton decoupling technique, structural information about the coupled protons is lost. Distortionless enhancement by polarization transfer (DEPT) is a technique that provides information on the number of hydrogens attached to each carbon in a molecule. While the DEPT experiment utilizes complex pulse sequences, the pulse delay and flip angle are specifically manipulated. The resulting signals have different phases depending on...

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Related Experiment Video

Updated: May 26, 2026

Mapping Dysfunctional Protein-Protein Interactions in Disease
09:39

Mapping Dysfunctional Protein-Protein Interactions in Disease

Published on: October 24, 2025

A time-series DDP for functional proteomics profiles.

Luis E Nieto-Barajas1, Peter Müller, Yuan Ji

  • 1Department of Statistics, ITAM, Rio Hondo 1, Progreso Tizapan, 01080 Mexico City, Mexico. lnieto@itam.mx

Biometrics
|January 7, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces a novel mixed effects model to analyze time-course protein expression data from reverse phase protein arrays (RPPA). The model captures temporal and pathway dependencies, revealing pathway-specific functional profiles and individual marker expression patterns over time.

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Area of Science:

  • Proteomics
  • Systems Biology
  • Statistical Modeling

Background:

  • Reverse phase protein array (RPPA) technology enables high-throughput protein expression measurement.
  • Understanding protein dynamics over time and their pathway-specific regulation is crucial in biological research.
  • Existing statistical models may not fully capture the complex dependencies in time-course RPPA data.

Purpose of the Study:

  • To develop a statistical model that accounts for temporal correlations and pathway dependencies in time-course RPPA data.
  • To analyze protein expression profiles over time for individual markers and their pathway-specific functions.
  • To apply this model to RPPA data for a comprehensive understanding of protein regulation.

Main Methods:

  • Utilized reverse phase protein array (RPPA) for time-course protein expression measurements.
  • Proposed a mixed effects model incorporating temporal and protein-specific components.
  • Developed random probability measures (RPM) with Dirichlet process models and multivariate beta distributions to handle temporal dependence.
  • Employed a conditionally autoregressive model to address pathway dependence among proteins.

Main Results:

  • The developed model successfully accommodates the complex dependencies inherent in time-course RPPA data.
  • Revealed pathway-dependent functional profiles for a set of coregulating proteins.
  • Identified marginal protein expression profiles over time for individual markers.

Conclusions:

  • The proposed mixed effects model provides a robust framework for analyzing time-course RPPA data.
  • This approach enhances the understanding of protein function and regulation within biological pathways.
  • The findings highlight the importance of considering both temporal and pathway dependencies in proteomic studies.