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

Trihybrid Crosses02:27

Trihybrid Crosses

24.9K
Trihybrid Crosses
Some of Mendel’s crosses examined three pairs of contrasting characteristics. Such a cross is called a trihybrid cross. A trihybrid cross is a combination of three individual monohybrid crosses. For example, plant height (tall vs. short), seed shape (round vs. wrinkled), and seed color (yellow vs. green).
The F1 generation plants of a trihybrid cross are heterozygous for all three traits and produce eight gametes. Upon self-fertilization, these gametes have an equal...
24.9K
Dihybrid Crosses01:18

Dihybrid Crosses

80.3K
Overview
80.3K
Wald-Wolfowitz Runs Test I01:17

Wald-Wolfowitz Runs Test I

853
The Wald-Wolfowitz test, also known as the runs test, is a nonparametric statistical test used to assess the randomness of a sequence of two different types of elements (e.g., positive/negative values, successes/failures). It examines whether the order of the elements in a sequence is random or if there is a pattern or trend present. This nonparametric test applies to any ordered data despite the population and sample data distribution, even if a higher sample size is available.
The test works...
853
Survival Tree01:19

Survival Tree

309
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
309
Behavioral Genetics and Its Designs01:23

Behavioral Genetics and Its Designs

830
Behavior genetics explores how genetic inheritance influences human behavior. It focuses on how genes, passed from parents to offspring, contribute to the development of behavioral traits and tendencies. This branch of genetics seeks to understand the complex interplay between inherited genetic factors and environmental influences in shaping our behaviors.
The primary methodologies used in behavior genetics include family studies, twin studies, and adoption studies, each providing unique...
830
Randomized Experiments01:13

Randomized Experiments

8.7K
The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
8.7K

You might also read

Related Articles

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

Sort by
Same author

Modeling of bioprocess pre-stages for optimization of perfusion profiles and increased process understanding.

Biotechnology and bioengineering·2023
Same author

Correction note to: View on a mechanistic model of Chlorella vulgaris in incubated shake flasks.

Bioprocess and biosystems engineering·2022
Same author

View on a mechanistic model of Chlorella vulgaris in incubated shake flasks.

Bioprocess and biosystems engineering·2021
Same author

Model-assisted DoE software: optimization of growth and biocatalysis in Saccharomyces cerevisiae bioprocesses.

Bioprocess and biosystems engineering·2021
Same author

Dynamic parameter estimation and prediction over consecutive scales, based on moving horizon estimation: applied to an industrial cell culture seed train.

Bioprocess and biosystems engineering·2020
Same author

Estimation of Process Model Parameters.

Methods in molecular biology (Clifton, N.J.)·2019
Same journal

Valorization of Agricultural Residues Through Nutrient Enrichment for Animal Farming.

Advances in biochemical engineering/biotechnology·2026
Same journal

Safety Aspects of Cell Culture-Derived Food for Human Consumption.

Advances in biochemical engineering/biotechnology·2026
Same journal

Correction to: Perspectives Towards AI and ML.

Advances in biochemical engineering/biotechnology·2026
Same journal

Valorization of Agricultural Residues for Biohydrogen Production via Dark Fermentation.

Advances in biochemical engineering/biotechnology·2026
Same journal

Composting of Agricultural Residues into Organic Fertilizers for Sustainable Agriculture.

Advances in biochemical engineering/biotechnology·2026
Same journal

Correction to: Theoretical Perspectives for Biomolecular Crystallization Prediction.

Advances in biochemical engineering/biotechnology·2026
See all related articles

Related Experiment Video

Updated: Dec 11, 2025

Reliable Method for Assessing Seed Germination, Dormancy, and Mortality under Field Conditions
07:03

Reliable Method for Assessing Seed Germination, Dormancy, and Mortality under Field Conditions

Published on: November 6, 2016

10.9K

Digital Seed Train Twins and Statistical Methods.

Tanja Hernández Rodríguez1, Björn Frahm2

  • 1Biotechnology and Bioprocess Engineering, Ostwestfalen-Lippe University of Applied Sciences and Arts, Lemgo, Germany.

Advances in Biochemical Engineering/Biotechnology
|August 16, 2020
PubMed
Summary
This summary is machine-generated.

Digital seed train twins enhance biopharmaceutical production by modeling cell proliferation. A Bayesian approach improves parameter estimation and prediction, accounting for prior knowledge and uncertainty.

Keywords:
BayesDigital twinParameter estimationSeed trainUncertainty

More Related Videos

BEST: Barcode Enabled Sequencing of Tetrads
12:59

BEST: Barcode Enabled Sequencing of Tetrads

Published on: May 1, 2014

10.5K
Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

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

Published on: March 1, 2024

1.1K

Related Experiment Videos

Last Updated: Dec 11, 2025

Reliable Method for Assessing Seed Germination, Dormancy, and Mortality under Field Conditions
07:03

Reliable Method for Assessing Seed Germination, Dormancy, and Mortality under Field Conditions

Published on: November 6, 2016

10.9K
BEST: Barcode Enabled Sequencing of Tetrads
12:59

BEST: Barcode Enabled Sequencing of Tetrads

Published on: May 1, 2014

10.5K
Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

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

Published on: March 1, 2024

1.1K

Area of Science:

  • Biopharmaceutical manufacturing
  • Process engineering
  • Digitalization in biotechnology

Background:

  • Cell proliferation (seed train) is critical but complex in biopharmaceutical production.
  • Challenges include metabolic complexity, batch variations, and environmental influences.
  • Digital solutions are needed for real-time process monitoring and decision-making.

Purpose of the Study:

  • To outline the necessity of digital solutions for seed train processes.
  • To describe the construction of a digital seed train twin.
  • To explore parameter estimation and statistical methods for bioprocessing.

Main Methods:

  • Development of digital seed train twins using mathematical models.
  • Application of parameter estimation techniques.
  • Utilizing a Bayesian approach for case study analysis.

Main Results:

  • Digital seed train twins effectively model time-dependent process variables.
  • A Bayesian approach was successfully applied to parameter estimation and prediction.
  • Prior knowledge and input uncertainty were incorporated into predictive uncertainty.

Conclusions:

  • Digital seed train twins are efficient tools for biopharmaceutical production.
  • Bayesian methods offer robust parameter estimation and uncertainty quantification.
  • Digitalization, including digital twins, is crucial for optimizing bioprocesses.