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Related Concept Videos

What is Metabolism?00:52

What is Metabolism?

Overview
Factors Affecting Drug Biotransformation: Biological01:19

Factors Affecting Drug Biotransformation: Biological

Biological factors significantly impact drug metabolism, influencing drug clearance, efficacy, and potential toxicity.
Species differences: Variations in enzyme systems across species can cause disparities in drug metabolism. For instance, humans may metabolize certain drugs faster than rodents, altering therapeutic effects.
Strain differences: Genetic variations within a species can result in differing enzyme activity, impacting drug response and toxicity. For example, some mouse strains may...
Regulation of Metabolism01:19

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Cellular needs and conditions vary from cell to cell and change within individual cells over time. For example, the required enzymes and energetic demands of stomach cells are different from those of fat storage cells, skin cells, blood cells, and nerve cells. Furthermore, a digestive cell works much harder to process and break down nutrients during the time that closely follows a meal compared with many hours after a meal. As these cellular demands and conditions vary, so do the amounts and...
Overview of Metabolism01:40

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Living cells constantly carry out various chemical reactions which are necessary for their proper functioning. These reactions are interlinked to one another via multiple pathways. The collection of these chemical reactions is known as metabolism.
Plant Metabolism
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Pharmacokinetics in Pediatric Patients: Drug Metabolism01:24

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In pediatric care, understanding the nuances of hepatic drug metabolism is crucial, as it significantly differs from that of adults. This divergence is primarily due to the developmental stage of drug-metabolizing enzymes, which affects how medications are processed in the body. In neonates, for instance, the activity of Phase I enzymes—critical for the initial breakdown of drugs—is markedly reduced, functioning at just 20–40% of the levels seen in adults. This reduction poses a challenge in...
Introduction to Metabolism01:30

Introduction to Metabolism

Metabolism encompasses all biochemical reactions in a living organism, facilitating both the breakdown and synthesis of biomolecules. These metabolic processes are categorized into catabolic and anabolic pathways, which operate in a coordinated manner to ensure energy balance and cellular function.Catabolic Pathways and Energy ReleaseCatabolic pathways involve the breakdown of complex macromolecules such as carbohydrates, lipids, and proteins into smaller structures like monosaccharides, fatty...

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Related Experiment Video

Updated: May 16, 2026

Metabolic Analysis of Drosophila melanogaster Larval and Adult Brains
07:06

Metabolic Analysis of Drosophila melanogaster Larval and Adult Brains

Published on: August 7, 2018

Temporal expression-based analysis of metabolism.

Sara B Collins1, Ed Reznik, Daniel Segrè

  • 1Program in Bioinformatics, Boston University, Boston, Massachusetts, United States of America.

Plos Computational Biology
|December 5, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces Temporal Expression-based Analysis of Metabolism (TEAM) to model dynamic metabolic flux in bacteria. TEAM uses gene expression data to predict metabolic changes, improving our understanding of cellular responses.

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Last Updated: May 16, 2026

Metabolic Analysis of Drosophila melanogaster Larval and Adult Brains
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Area of Science:

  • Systems Biology
  • Metabolic Engineering
  • Computational Biology

Background:

  • Cellular metabolism dynamically reroutes in response to environmental changes.
  • Quantitative and predictive models are crucial for understanding metabolic transitions.
  • Integrating gene expression data into stoichiometric models advances systems biology.

Purpose of the Study:

  • To develop and apply a novel method, Temporal Expression-based Analysis of Metabolism (TEAM), for modeling metabolic dynamics.
  • To analyze the metabolic shifts in Shewanella oneidensis under specific aerobic, lactate-limited conditions.
  • To enhance predictive power by incorporating a gene expression compendium.

Main Methods:

  • Utilized time-series gene expression data to predict temporal metabolic flux distributions.
  • Integrated a large reference compendium of gene expression data to refine predictions.
  • Developed a sensitivity analysis method for the free threshold parameter θ.
  • Constrained the parameter θ by comparing model behavior to experimental data.

Main Results:

  • TEAM successfully predicted temporal metabolic flux distributions for Shewanella oneidensis.
  • Supplementing with a gene expression compendium increased predictive accuracy.
  • Sensitivity analysis revealed distinct metabolic behavior zones based on the parameter θ.
  • Identified that small initial errors in dynamic metabolic flux modeling can propagate significantly over time.

Conclusions:

  • Temporal Expression-based Analysis of Metabolism (TEAM) provides a robust framework for modeling dynamic metabolic flux.
  • The study highlights the importance of parameter sensitivity analysis in dynamic metabolic modeling.
  • Addressing history-dependent sensitivities is critical for future advancements in dynamic metabolic modeling techniques.