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

Fundamental Attribution Error01:14

Fundamental Attribution Error

13.8K
According to some social psychologists, people tend to overemphasize internal factors as explanations—or attributions—for the behavior of other people. They tend to assume that the behavior of another person is a trait of that person, and to underestimate the power of the situation on the behavior of others. They tend to fail to recognize when the behavior of another is due to situational variables, and thus to the person’s state. This erroneous assumption is...
13.8K
What is a Mode?01:07

What is a Mode?

26.6K
The mode is one of the commonly used measures of a central tendency. It is defined as the most frequent value in a data set.
There can be more than one mode in a data set if multiple values have the same highest frequency. For instance, suppose that the Statistics exam scores of 20 students are: 50; 53; 59; 59; 63; 63; 72; 72; 72; 72; 72; 76; 78; 81; 83; 84; 84; 84; 90; 93. Here, the mode is 72, as it occurs most frequently, five times.
A data set with two modes is called bimodal. For example,...
26.6K
Systematic Error: Methodological and Sampling Errors01:15

Systematic Error: Methodological and Sampling Errors

11.1K
In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
11.1K
pH Scale02:41

pH Scale

80.4K
Hydronium and hydroxide ions are present both in pure water and in all aqueous solutions, and their concentrations are inversely proportional as determined by the ion product of water (Kw). The concentrations of these ions in a solution are often critical determinants of the solution’s properties and the chemical behaviors of its other solutes. Two different solutions can differ in their hydronium or hydroxide ion concentrations by a million, billion, or even trillion times. A common means of...
80.4K
Random Error01:04

Random Error

9.9K
Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...
9.9K
Margin of Error01:27

Margin of Error

7.7K
The margin of error is also called the maximum error of an estimate. The margin of error is the maximum possible or expected difference between the observed sample parameter value and the actual population parameter value. For proportion, it is the maximum difference between the value of sample proportion obtained from the data and the true value of population proportion. As the true value of the population parameter is not known, the margin of error is calculated using the sample statistic.
7.7K

You might also read

Related Articles

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

Sort by
Same author

Multi-omics analysis of saccharomyces boulardii supplementation reveals coordinated microbiome, metabolic, and immune signaling changes accompanying tumor suppression.

Gut microbes·2026
Same author

Reply to: Genome contamination may lead to an overestimation of horizontal gene transfer inferences.

Nature communications·2026
Same author

Fungal Mycelium Films Engineered as Renewable Fibrous Materials for Drug Delivery.

ACS applied materials & interfaces·2026
Same author

Omics-Based Expression Cassette for Heterologous Protein Production in <i>Y. lipolytica</i>.

ACS synthetic biology·2026
Same author

Gut microbiome-mediated transformation of dietary phytonutrients is associated with health outcomes.

Nature microbiology·2025
Same author

Orismilast, a Potent and Selective PDE4B/D Inhibitor, Reduces Protein Levels of Key Disease Driving Cytokines in the Skin of Patients With Plaque Psoriasis.

Experimental dermatology·2025

Related Experiment Video

Updated: Feb 14, 2026

Pooled CRISPR-Based Genetic Screens in Mammalian Cells
09:05

Pooled CRISPR-Based Genetic Screens in Mammalian Cells

Published on: September 4, 2019

23.3K

Diverse genetic error modes constrain large-scale bio-based production.

Peter Rugbjerg1, Nils Myling-Petersen1, Andreas Porse1

  • 1The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Building 220, DK-2800, Kongens Lyngby, Denmark.

Nature Communications
|February 22, 2018
PubMed
Summary

Microbial production declines due to evolution, not just variation. Researchers found that engineered microbes sacrifice valuable chemical production for faster growth, a problem solvable with new strategies.

More Related Videos

Author Spotlight: Advancing Cell Therapy Manufacturing with Dissolvable Microcarriers
09:44

Author Spotlight: Advancing Cell Therapy Manufacturing with Dissolvable Microcarriers

Published on: July 7, 2023

5.1K
Large-Scale, Automated Production of Adipose-Derived Stem Cell Spheroids for 3D Bioprinting
07:40

Large-Scale, Automated Production of Adipose-Derived Stem Cell Spheroids for 3D Bioprinting

Published on: March 31, 2022

3.2K

Related Experiment Videos

Last Updated: Feb 14, 2026

Pooled CRISPR-Based Genetic Screens in Mammalian Cells
09:05

Pooled CRISPR-Based Genetic Screens in Mammalian Cells

Published on: September 4, 2019

23.3K
Author Spotlight: Advancing Cell Therapy Manufacturing with Dissolvable Microcarriers
09:44

Author Spotlight: Advancing Cell Therapy Manufacturing with Dissolvable Microcarriers

Published on: July 7, 2023

5.1K
Large-Scale, Automated Production of Adipose-Derived Stem Cell Spheroids for 3D Bioprinting
07:40

Large-Scale, Automated Production of Adipose-Derived Stem Cell Spheroids for 3D Bioprinting

Published on: March 31, 2022

3.2K

Area of Science:

  • Biotechnology
  • Synthetic Biology
  • Microbial Engineering

Background:

  • Sustainable bio-based chemical production is crucial for green growth.
  • Large-scale microbial fermentation often suffers from decreased productivity and yield.
  • Phenotypic variation is the commonly cited cause, but evolutionary constraints may also play a role.

Purpose of the Study:

  • To experimentally investigate the evolutionary forces limiting microbial production during large-scale fermentation.
  • To identify the genetic mechanisms underlying production decline in engineered microbial populations.
  • To develop strategies to mitigate genetically driven declines in bio-production.

Main Methods:

  • Simulated large-scale fermentation of mevalonic acid-producing Escherichia coli.
  • Tracking of growth rate and production over 70 generations.
  • Ultra-deep time-lapse sequencing (>1000×) of microbial populations.
  • Development of population-level bioinformatics tools.
  • Quantitative modeling of population dynamics.
  • Validation by tuning production load and host escape rate.

Main Results:

  • Microbial populations rapidly sacrificed production to gain fitness within 70 generations.
  • Recurring intra-pathway genetic errors were identified as a key driver of decline.
  • Deep-sequencing and advanced bioinformatics revealed significant, previously underestimated genetic heterogeneity.
  • A quantitative model accurately explained population dynamics through the enrichment of spontaneous mutants.
  • Strategies were validated to postpone genetically driven production declines.

Conclusions:

  • Evolutionary processes, specifically the enrichment of spontaneous mutants with genetic errors, significantly constrain microbial bio-production.
  • The problem of declining productivity is likely underestimated due to limitations in detection methods.
  • The developed quantitative model and mitigation strategies offer a pathway to more stable and sustainable bio-based chemical production.