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

Stringent Response in E. coli01:23

Stringent Response in E. coli

215
Bacterial growth is closely tied to nutrient availability, with cells proliferating exponentially under favorable conditions and entering a stationary phase when resources become scarce. This transition is mediated by a regulatory mechanism known as the stringent response, which allows bacteria to adapt to nutrient deprivation by modulating gene expression and metabolic activity.During nutrient scarcity, intracellular amino acid levels decline. It results in the accumulation of uncharged tRNAs...
215
Operon Model01:23

Operon Model

908
The operon model represents a fundamental mechanism of gene regulation in prokaryotes, enabling coordinated expression of genes involved in related metabolic or functional pathways. Operons consist of structural genes, a promoter, and an operator, with transcription regulated by repressors, activators, and small effector molecules.Structure and Function of OperonsAn operon is a cluster of structural genes transcribed together under the control of a single promoter. The promoter region...
908
Amino Acid Catabolism01:18

Amino Acid Catabolism

750
Microorganisms rely on proteins as an essential carbon and energy source, particularly in environments with limited polysaccharides or lipids. However, proteins are too large to cross the plasma membrane unaided, necessitating enzymatic degradation. Microbes secrete extracellular proteases and peptidases that hydrolyze proteins into peptides, which can then be transported across the membrane. Once inside the cell, intracellular proteases degrade these peptides into free amino acids, which...
750
Inorganic Nitrogen Assimilation01:22

Inorganic Nitrogen Assimilation

372
Nitrogen is an essential element in biological systems, forming a crucial component of proteins, nucleic acids, and other cellular constituents. Many bacteria and archaea acquire nitrogen in the form of nitrate (NO₃⁻) or ammonia (NH₃), which are then assimilated into biomolecules through specific enzymatic pathways.Assimilatory Nitrate ReductionWhen nitrate enters the cell, it undergoes a two-step reduction process known as assimilatory nitrate reduction. Initially, the enzyme...
372
Biosynthesis in Bacteria01:24

Biosynthesis in Bacteria

454
Biosynthesis in bacteria is a fundamental anabolic process that generates essential macromolecules, including proteins, nucleic acids, lipids, and polysaccharides. These macromolecules are critical for cellular growth, replication, and function. The process is tightly regulated and energetically linked to catabolic pathways to ensure optimal resource utilization.Biosynthetic pathways begin with precursor metabolites such as pyruvate, acetyl-CoA, and glucose-6-phosphate derived from glycolysis,...
454
Cells Coordinate Growth and Proliferation02:36

Cells Coordinate Growth and Proliferation

4.9K
Cell size is a significant factor impacting cellular design, function, and fitness. There exists some internal coordination by which cells double their masses before division, thus, achieving homeostasis. Coordination between cell growth and proliferation depends on the checkpoints in between cell cycle phases. Loss of coordination or failure in the checkpoint mechanism can drive the cell to uncontrolled growth and loss of cellular function. Like dividing cells that coordinate cellular growth,...
4.9K

You might also read

Related Articles

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

Sort by
Same author

Comparative genomic analysis of <i>Bacteroides fragilis</i> from intestinal and extra-intestinal sites.

Microbiology spectrum·2026
Same author

Overcoming the limits of traditional rate calculations from sparse concentration data: a probabilistic framework for bioprocess modeling.

Bioprocess and biosystems engineering·2026
Same author

Predictive Algal Systems Biology: Integrating Omics, Genome-Scale Metabolic Models, and Machine Learning.

Bioengineering (Basel, Switzerland)·2026
Same author

Run-to-run optimization of CHO cell culture media using high-throughput microscale bioreactor system and a hybrid modeling approach.

Journal of biotechnology·2026
Same author

Rational engineering of facultative anaerobiosis enables commensal survival in the oxygenated gut.

bioRxiv : the preprint server for biology·2026
Same author

A Hybrid Modeling Framework for Predictive Digital Twins of CHO Cell Culture.

Computational and structural biotechnology journal·2026

Related Experiment Video

Updated: Dec 21, 2025

Workflow Based on the Combination of Isotopic Tracer Experiments to Investigate Microbial Metabolism of Multiple Nutrient Sources
12:47

Workflow Based on the Combination of Isotopic Tracer Experiments to Investigate Microbial Metabolism of Multiple Nutrient Sources

Published on: January 22, 2018

9.8K

Dynamic resource allocation drives growth under nitrogen starvation in eukaryotes.

Juan D Tibocha-Bonilla1,2,3, Manish Kumar2, Anne Richelle2

  • 1Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA, 92093-0760, USA.

NPJ Systems Biology and Applications
|May 17, 2020
PubMed
Summary

Cells adapt biomass composition to nutrient availability. Metabolic models reveal free energy, not molecular weight, drives biosynthetic costs, explaining high amino acid expenses during stress.

More Related Videos

Quantification of the Abundance and Charging Levels of Transfer RNAs in Escherichia coli
10:34

Quantification of the Abundance and Charging Levels of Transfer RNAs in Escherichia coli

Published on: August 22, 2017

9.7K
The Use of Chemostats in Microbial Systems Biology
13:19

The Use of Chemostats in Microbial Systems Biology

Published on: October 14, 2013

31.6K

Related Experiment Videos

Last Updated: Dec 21, 2025

Workflow Based on the Combination of Isotopic Tracer Experiments to Investigate Microbial Metabolism of Multiple Nutrient Sources
12:47

Workflow Based on the Combination of Isotopic Tracer Experiments to Investigate Microbial Metabolism of Multiple Nutrient Sources

Published on: January 22, 2018

9.8K
Quantification of the Abundance and Charging Levels of Transfer RNAs in Escherichia coli
10:34

Quantification of the Abundance and Charging Levels of Transfer RNAs in Escherichia coli

Published on: August 22, 2017

9.7K
The Use of Chemostats in Microbial Systems Biology
13:19

The Use of Chemostats in Microbial Systems Biology

Published on: October 14, 2013

31.6K

Area of Science:

  • Cellular metabolism
  • Systems biology
  • Biomass composition

Background:

  • Cells dynamically adjust biomass composition in response to environmental cues, particularly nutrient availability.
  • Nutrient limitation triggers significant shifts in resource allocation towards carbon-rich molecules.
  • Understanding these adaptive mechanisms is crucial for predicting cellular behavior under stress.

Purpose of the Study:

  • To predict cellular growth and metabolic flux changes using genome-scale metabolic models.
  • To investigate the impact of nitrogen depletion on metabolic pathway and organelle function over time.
  • To identify key drivers of biosynthetic costs in eukaryotic cells.

Main Methods:

  • Utilized dynamic biomass composition data from five eukaryotic organisms (three heterotrophs, two phototrophs).
  • Employed genome-scale metabolic models to simulate and predict metabolic flux distributions.
  • Analyzed model sensitivity and calculated biosynthetic costs of biomass metabolites.

Main Results:

  • Identified temporal metabolic flux profiles indicating long-term functional trends in response to nitrogen depletion.
  • Demonstrated that the free energy of biomass metabolites, not molecular weight, is the primary determinant of biosynthetic cost.
  • Explained the high metabolic cost associated with synthesizing arginine and histidine.

Conclusions:

  • Genome-scale metabolic models can accurately predict complex cellular responses to stress.
  • Cellular adaptation to nutrient stress involves intricate interwoven mechanisms.
  • Free energy is a critical factor in understanding metabolic efficiency and resource allocation.