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

Synthetic Biology02:55

Synthetic Biology

5.4K
Synthetic biology is an interdisciplinary science that involves using principles from disciplines such as engineering, molecular biology, cell biology, and systems biology. It involves remodeling existing organisms from nature or constructing completely new synthetic organisms for applications such as protein or enzyme production, bioremediation, value-added macromolecule production, and the addition of desirable traits to crops, to name a few.
Golden rice
Golden rice is a genetically modified...
5.4K
Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

281
Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
281
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

193
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
193
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

210
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
210
Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

227
Noncompartmental analyses offer an alternative method for describing drug pharmacokinetics without relying on a specific compartmental model. In this approach, the drug's pharmacokinetics are assumed to be linear, with the terminal phase log-linear. This assumption allows for simplified analysis and interpretation of the drug's behavior in the body.
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This...
227
Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

416
Compartmental analysis is a widely adopted approach to characterizing drug pharmacokinetics. It uses compartment models that conceptualize the body as a collection of reversibly communicating compartments, each representing a group of tissues exhibiting similar drug distribution characteristics. The movement rate of the drug between these compartments is typically described by first-order kinetics.
Two primary types of compartment models are recognized: mammillary and catenary. The more...
416

You might also read

Related Articles

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

Sort by
Same author

Second regulatory workshop on utilising in silico models to expedite vaccine development, testing, and lifecycle management - an expert meeting report.

Vaccine·2026
Same author

Molecular Scale Hydrophobicity and Adsorption Thermodynamics on Hydrophobic-Charged Surfaces.

ACS nano·2026
Same author

PepFoundry: A Pipeline for Building Machine-Learning Ready Representations of Nonstandard Peptides Containing Cycles, Non-natural Residues, Polymer Units, and More.

Journal of chemical information and modeling·2026
Same author

A Guide to Bayesian Optimization in Bioprocess Engineering.

Biotechnology and bioengineering·2026
Same author

Influence of ionic liquids on enzymatic asymmetric carboligations.

Computational and structural biotechnology journal·2025
Same author

Measuring adsorption equilibria: The determination of the maximum binding capacity depends strongly on the method of resin preparation.

Journal of chromatography. A·2025

Related Experiment Video

Updated: Dec 13, 2025

Operation of the Collaborative Composite Manufacturing CCM System
10:09

Operation of the Collaborative Composite Manufacturing CCM System

Published on: October 1, 2019

7.0K

Toward in silico CMC: An industrial collaborative approach to model-based process development.

David Roush1, Dilip Asthagiri2, Deenesh K Babi3

  • 1Merck & Co., Inc., Kenilworth, New Jersey.

Biotechnology and Bioengineering
|July 30, 2020
PubMed
Summary
This summary is machine-generated.

This manuscript summarizes the Third Modeling Workshop on bioprocess modeling. It highlights the need for investment in biotechnology modeling to save time and resources, covering all scales and life cycle management.

Keywords:
computational fluid dynamicsmechanistic modelingmolecular modelingplant simulation

More Related Videos

In Silico Clinical Trials for Cardiovascular Disease
09:09

In Silico Clinical Trials for Cardiovascular Disease

Published on: May 27, 2022

2.1K
Automated Robotic Liquid Handling Assembly of Modular DNA Devices
11:22

Automated Robotic Liquid Handling Assembly of Modular DNA Devices

Published on: December 1, 2017

12.7K

Related Experiment Videos

Last Updated: Dec 13, 2025

Operation of the Collaborative Composite Manufacturing CCM System
10:09

Operation of the Collaborative Composite Manufacturing CCM System

Published on: October 1, 2019

7.0K
In Silico Clinical Trials for Cardiovascular Disease
09:09

In Silico Clinical Trials for Cardiovascular Disease

Published on: May 27, 2022

2.1K
Automated Robotic Liquid Handling Assembly of Modular DNA Devices
11:22

Automated Robotic Liquid Handling Assembly of Modular DNA Devices

Published on: December 1, 2017

12.7K

Area of Science:

  • Biotechnology
  • Bioprocess Engineering
  • Computational Modeling

Background:

  • Bioprocess modeling is a critical research area in biotechnology.
  • There is a recognized need to assess the current state and future potential of modeling.
  • Models offer significant opportunities for resource and time savings in bioprocess development and production.

Purpose of the Study:

  • To summarize the proceedings of the Third Modeling Workshop.
  • To assess the current state and future directions of bioprocess modeling.
  • To identify opportunities for investment and standardization in the field.

Main Methods:

  • The manuscript is based on a summary of the Third Modeling Workshop proceedings.
  • Discussions covered various scales of modeling, from molecular to facility-level.
  • Model life cycle management was also a key topic.

Main Results:

  • The workshop provided a comprehensive overview of bioprocess modeling.
  • Discussions encompassed diverse modeling scales and applications.
  • Key areas for improvement and future directions were identified.

Conclusions:

  • Bioprocess modeling is vital for the biotechnology industry.
  • Standardized approaches and continued investment are recommended.
  • Achieving an ideal future state requires industry-wide alignment on modeling practices.