Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...
Biostatistics: Overview01:20

Biostatistics: Overview

Biostatistics plays a crucial role in understanding and analyzing data in healthcare and biology. Biostatisticians conduct experiments, gather evidence, and draw meaningful conclusions using statistical methods and techniques. Different variables form the foundation of biostatistical analysis, allowing researchers to understand and interpret data effectively. These variables are classified into different types, each serving a specific purpose in statistical analysis.
Discrete variables are...
Aggregates Classification01:29

Aggregates Classification

Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and Cox...
Statistical Software for Data Analysis and Clinical Trials01:12

Statistical Software for Data Analysis and Clinical Trials

Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...

You might also read

Related Articles

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

Sort by
Same author

Environmental enrichment modulates the mouse immune system.

Lab animal·2026
Same author

Human placental extract mitigates MTX nephrotoxicity.

Lab animal·2026
Same author

Mechanism behind remimazolam neuroprotective effect.

Lab animal·2026
Same author

Postweaning exercise improves sleep deprivation effects.

Lab animal·2026
Same author

NMDA-dependent mechanism of depression.

Lab animal·2026
Same author

Evolocumab in Patients With High-Risk Diabetes: Results From the VESALIUS-CV Trial.

Diabetes care·2026

Related Experiment Video

Updated: Jul 8, 2026

In Vivo Modeling of the Morbid Human Genome using Danio rerio
12:31

In Vivo Modeling of the Morbid Human Genome using Danio rerio

Published on: August 24, 2013

20.8K

TROPPO: tissue-specific reconstruction and phenotype prediction using omics data.

Alexandre Oliveira1, Jorge Ferreira1, Vítor Vieira1

  • 1Centre of Biological Engineering, University of Minho, Braga 4710-057, Portugal.

Bioinformatics Advances
|June 23, 2025
PubMed
Summary

TROPPO is a new open-source Python library that simplifies the creation of accurate, context-specific metabolic models. It addresses challenges in integrating omics data and overcomes limitations of proprietary software for systems biology research.

More Related Videos

Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering
09:43

Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering

Published on: November 22, 2019

6.4K
Skeletal Phenotype Analysis of a Conditional Stat3 Deletion Mouse Model
08:42

Skeletal Phenotype Analysis of a Conditional Stat3 Deletion Mouse Model

Published on: July 3, 2020

4.6K

Related Experiment Videos

Last Updated: Jul 8, 2026

In Vivo Modeling of the Morbid Human Genome using Danio rerio
12:31

In Vivo Modeling of the Morbid Human Genome using Danio rerio

Published on: August 24, 2013

20.8K
Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering
09:43

Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering

Published on: November 22, 2019

6.4K
Skeletal Phenotype Analysis of a Conditional Stat3 Deletion Mouse Model
08:42

Skeletal Phenotype Analysis of a Conditional Stat3 Deletion Mouse Model

Published on: July 3, 2020

4.6K

Area of Science:

  • Systems Biology
  • Metabolic Modeling
  • Bioinformatics

Background:

  • High-throughput technologies have advanced predictive tools like genome-scale metabolic models.
  • Integrating omics data for accurate, context-specific metabolic models remains challenging.
  • Many existing tools are proprietary, limiting accessibility and widespread use.

Purpose of the Study:

  • To introduce TROPPO, an open-source Python library designed to facilitate the creation of context-specific metabolic models.
  • To overcome challenges associated with integrating omics data and proprietary software limitations.
  • To provide accessible tools for systems biology research.

Main Methods:

  • TROPPO supports various context-specific reconstruction algorithms.
  • Includes validation methods for assessing the accuracy and reliability of generated models.
  • Incorporates gap-filling algorithms to ensure metabolic model consistency.

Main Results:

  • TROPPO offers an open-source solution for building context-specific metabolic models.
  • The library integrates seamlessly with existing constraint-based modeling tools.
  • Provides a flexible and accessible platform for systems biology applications.

Conclusions:

  • TROPPO democratizes the creation of metabolic models by being open-source and Python-based.
  • Enhances the ability to generate accurate, tissue-specific models from omics data.
  • Facilitates reproducible and collaborative research in systems biology.