Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Background and Environment Affect Phenotype

Background and Environment Affect Phenotype

Although the genetic makeup of an organism plays a major role in determining the phenotype, there are also several environmental factors, such as temperature, oxygen availability, presence of mutagens, that can alter an organism’s phenotype.
An example of how genetic background affects phenotype can be seen in horses. The Extension gene in horses is responsible for their coat color. A wild-type gene (EE) produces black pigment in the coat, while a mutant gene (ee) produces red pigment. A...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Functional Groups Are All You Need for Chemically Interpretable Molecular Property Prediction.

Journal of chemical information and modeling·2026

Same author

ThermOptCobra: Thermodynamically optimal construction and analysis of metabolic networks for reliable phenotype predictions.

iScience·2025

Same author

Genome-scale metabolic modelling identifies reactions mediated by SNP-SNP interactions associated with yeast sporulation.

NPJ systems biology and applications·2025

Same author

Improving the accuracy of hyaluronic acid molecular weight estimation by conventional size exclusion chromatography.

Journal of chromatography. B, Analytical technologies in the biomedical and life sciences·2017

Same journal

Oncostatin M orchestrates collective epithelial migration via HIF1A activation.

NPJ systems biology and applications·2026

Same journal

Identification of phillyrin as a PPAR-γ ligand through network pharmacology and molecular modeling.

NPJ systems biology and applications·2026

Same journal

Expanding the role of metabolism in whole plant models.

NPJ systems biology and applications·2026

Same journal

Modeling single nucleus microglia across species identifies immune pathways and therapeutic candidates in Alzheimer's disease.

NPJ systems biology and applications·2026

Same journal

Metabolic set theory: a generalized model of microbial interactions.

NPJ systems biology and applications·2026

Same journal

Gene prioritization across ancestries uncovers distinct molecular pathophysiology and therapeutic landscape in polycystic ovary syndrome.

NPJ systems biology and applications·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jan 8, 2026

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

Modelling reliable metabolic phenotypes by analysing the context-specific transcriptomics data.

Pavan Kumar S^1,2,3, Nirav Pravinbhai Bhatt^4,5,6,7

¹BioSystems Engineering and Control (BiSECt) Lab, Department of Biotechnology, Indian Institute of Technology Madras (IIT Madras), Chennai, Tamil Nadu, India.

NPJ Systems Biology and Applications

|December 13, 2025

Summary

This summary is machine-generated.

Localgini, a novel algorithm, enhances metabolic models by analyzing gene expression variability. This method accurately identifies active metabolic reactions for context-specific models, improving biological insights.

More Related Videos

High-Throughput Metabolic Profiling for Model Refinements of Microalgae

High-Throughput Metabolic Profiling for Model Refinements of Microalgae

Published on: December 4, 2021

Metabolic Labeling of Newly Transcribed RNA for High Resolution Gene Expression Profiling of RNA Synthesis, Processing and Decay in Cell Culture

Metabolic Labeling of Newly Transcribed RNA for High Resolution Gene Expression Profiling of RNA Synthesis, Processing and Decay in Cell Culture

Published on: August 8, 2013

Related Experiment Videos

Last Updated: Jan 8, 2026

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

High-Throughput Metabolic Profiling for Model Refinements of Microalgae

High-Throughput Metabolic Profiling for Model Refinements of Microalgae

Published on: December 4, 2021

Metabolic Labeling of Newly Transcribed RNA for High Resolution Gene Expression Profiling of RNA Synthesis, Processing and Decay in Cell Culture

Metabolic Labeling of Newly Transcribed RNA for High Resolution Gene Expression Profiling of RNA Synthesis, Processing and Decay in Cell Culture

Published on: August 8, 2013

Area of Science:

Computational biology
Systems biology
Metabolic modeling

Background:

Genome-scale metabolic models (GEMs) offer insights into cellular metabolism but often lack context-specific details.
Transcriptomic data integration is key to refining GEMs for disease and cellular states.
Current methods struggle with accurately identifying context-dependent metabolic adaptations.

Purpose of the Study:

To introduce 'Localgini', an algorithm for quantifying gene expression variability using the Gini coefficient.
To develop a method for constructing accurate context-specific models (CSMs) from GEMs and transcriptomic data.
To improve the specificity of GEMs for studying cellular adaptations.

Main Methods:

Developed the Localgini algorithm to measure gene expression heterogeneity across samples.
Applied Localgini to generate CSMs using transcriptomic data from NCI-60 cancer cell lines and human tissues.
Evaluated Localgini against six different model extraction methods (MeMs).

Main Results:

Localgini-based CSMs demonstrated improved representation of housekeeping functions and known metabolic pathways.
Active reaction sets identified by Localgini required minimal support from MeMs.
Localgini reduced variability in CSMs generated across different MeMs with identical expression data.

Conclusions:

Localgini offers an accurate approach for building context-specific metabolic models by integrating gene expression heterogeneity.
The algorithm enhances the biological relevance and specificity of GEMs.
Localgini facilitates more reliable computational studies of cellular metabolism in various contexts.