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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...

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Related Experiment Video

Updated: Aug 24, 2025

In Vivo Modeling of the Morbid Human Genome using Danio rerio

In Vivo Modeling of the Morbid Human Genome using Danio rerio

Published on: August 24, 2013

Modeling Clinical Phenotype Variability: Consideration of Genomic Variations, Computational Methods, and Quantitative

Jane P F Bai¹, Li-Rong Yu²

¹Office of Clinical Pharmacology, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, MD 20903, USA.

Journal of Pharmaceutical Sciences

|October 24, 2022

Summary

This summary is machine-generated.

Quantitative systems pharmacology utilizes computational models to simulate virtual patient populations (VPops) for predicting drug response variability. Incorporating genomic and proteomic data enhances these models for more accurate clinical predictions.

Keywords:

Computation Genomic variation Quantitative proteomics Quantitative systems pharmacology Virtual patient population

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Last Updated: Aug 24, 2025

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Area of Science:

Biomedical modeling
Computational pharmacology
Systems biology

Background:

Biomedical and computer technology advances enable mechanistic modeling of disease and drug response variability.
Quantifying response variability is crucial for informing drug development programs.
Computational approaches for virtual patient populations (VPops) are established in quantitative systems pharmacology.

Purpose of the Study:

To explore the integration of genomic and quantitative proteomics data into VPop models.
To enhance the predictive capability of VPops for simulating virtual patient trials.
To account for clinically observed phenotypic variations in a predictive manner.

Main Methods:

Leveraging advances in biomedical and computer technologies for mechanistic modeling.
Utilizing computational approaches developed by quantitative systems pharmacology scientists.
Incorporating data from genomic variations and quantitative proteomics technologies.

Main Results:

The study outlines a framework for creating enhanced VPops.
The proposed approach aims to improve the prediction of drug response variability.
Integration of multi-omics data is suggested for more robust modeling.

Conclusions:

Incorporating genomic and proteomic variations into VPops can significantly improve the modeling and simulation of drug response.
Enhanced VPops hold the potential to accelerate drug development and personalize patient care.
This approach offers a predictive method to understand and manage phenotypic variability in clinical settings.