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

Modeling with Differential Equations01:25

Modeling with Differential Equations

Population dynamics can be described mathematically by considering the population size P(t) as a function of time. The rate of change of the population is then represented by the derivative of P(t). A simple assumption is that the rate of growth is proportional to the size of the population itself. This leads to an exponential growth model, where the population increases rapidly without bound. While this is a useful first approximation, it does not reflect realistic long-term...
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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...
Pharmacodynamic Models: Link Model and Systems Pharmacodynamic Model01:14

Pharmacodynamic Models: Link Model and Systems Pharmacodynamic Model

The link model is a fundamental pharmacokinetic-pharmacodynamic (PK–PD) approach to account for delayed drug responses when the observed effect does not immediately correlate with the drug's plasma concentration peak. This delay is mathematically addressed by introducing an effect compartment concentration, Ce, which is kinetically linked to the plasma concentration, Cp, via a first-order rate constant, ke0. The linkage allows for a more accurate prediction of drug effects over time. A higher...
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...
Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

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...
Pharmacodynamic Models: Overview01:27

Pharmacodynamic Models: Overview

Pharmacodynamic (PD) responses describe the interaction between a drug and its biological target, culminating in a physiological effect. These responses can be classified into different types: continuous variables, such as blood glucose levels; categorical outcomes, like survival rates; and time-to-event metrics, such as disease progression. Understanding and modeling PD responses are critical for optimizing drug efficacy and safety.PD models describe the relationship between drug concentration...

You might also read

Related Articles

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

Sort by
Same author

Driving forces in the origins of life.

Open biology·2021
Same author

Identification of structural fingerprints for ABCG2 inhibition by using Monte Carlo optimization, Bayesian classification, and structural and physicochemical interpretation (SPCI) analysis.

SAR and QSAR in environmental research·2020
Same author

Idiopathic CD4+ T lymphocytopenia: Still a long way to understand the disease.

Journal of postgraduate medicine·2020
Same author

Impact of functional IL-18 polymorphisms on genetic predisposition and diverse clinical manifestations of the disease in Indian SLE patients.

Lupus·2019
Same author

Proptosis with hemiplegia: Unusual presentation of multiple myeloma.

Journal of postgraduate medicine·2018
Same author

Acute myeloid leukemia with 3q26 abnormality: An editorial perspective.

Journal of postgraduate medicine·2018
Same journal

From Cation Solvation to Anion Coordination: Lewis-Acidic Boranes Enable Halide Salt Electrolytes.

The journal of physical chemistry. B·2026
Same journal

In Vitro-Prepared A30P Alpha-Synuclein Fibrils Adopt the Conserved and Disease-Relevant Greek Key Fold.

The journal of physical chemistry. B·2026
Same journal

Metastructure Analysis of Self-Assembled Nanocubes with Different Equatorial Methyl Groups Based on Molecular Dynamics Simulations.

The journal of physical chemistry. B·2026
Same journal

A Cocoordinated <sup>1</sup>H Internal Reference Quantifies Proton-Exchange Bias in Coordinated-Water Diffusion.

The journal of physical chemistry. B·2026
Same journal

Unveiling Electrolyte-Dependent Coordination Site Dynamics for Redox Mediator Design in Lithium-O<sub>2</sub> Batteries: Exchange vs Rearrangement.

The journal of physical chemistry. B·2026
Same journal

The Role of Functional Groups in Substituted Benzoic Acids Used as Dopants in Liquid Crystal Mixtures on the Nematic-Isotropic Transitions.

The journal of physical chemistry. B·2026
See all related articles

Related Experiment Video

Updated: Jun 2, 2026

The Use of Chemostats in Microbial Systems Biology
13:19

The Use of Chemostats in Microbial Systems Biology

Published on: October 14, 2013

Modeling stochastic dynamics in biochemical systems with feedback using maximum caliber.

S Pressé1, K Ghosh, K A Dill

  • 1Department of Pharmaceutical Chemistry, University of California, San Francisco, California, USA.

The Journal of Physical Chemistry. B
|April 29, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces Maximum Caliber (MaxCal), a new stochastic modeling method for biological feedback systems. MaxCal simplifies modeling by requiring fewer parameters and assumptions, accurately capturing system dynamics.

More Related Videos

Modeling Fast-scan Cyclic Voltammetry Data from Electrically Stimulated Dopamine Neurotransmission Data Using QNsim1.0
07:41

Modeling Fast-scan Cyclic Voltammetry Data from Electrically Stimulated Dopamine Neurotransmission Data Using QNsim1.0

Published on: June 5, 2017

Design and Use of Multiplexed Chemostat Arrays
19:40

Design and Use of Multiplexed Chemostat Arrays

Published on: February 23, 2013

Related Experiment Videos

Last Updated: Jun 2, 2026

The Use of Chemostats in Microbial Systems Biology
13:19

The Use of Chemostats in Microbial Systems Biology

Published on: October 14, 2013

Modeling Fast-scan Cyclic Voltammetry Data from Electrically Stimulated Dopamine Neurotransmission Data Using QNsim1.0
07:41

Modeling Fast-scan Cyclic Voltammetry Data from Electrically Stimulated Dopamine Neurotransmission Data Using QNsim1.0

Published on: June 5, 2017

Design and Use of Multiplexed Chemostat Arrays
19:40

Design and Use of Multiplexed Chemostat Arrays

Published on: February 23, 2013

Area of Science:

  • Biochemistry
  • Systems Biology
  • Computational Biology

Background:

  • Complex biological feedback systems are common.
  • Current modeling approaches often require hard-to-obtain data like reaction rates and topologies.
  • This necessitates treating these parameters as adjustable, limiting model accuracy.

Purpose of the Study:

  • To present a general stochastic modeling method for small chemical and biochemical systems, particularly those with feedback.
  • To introduce Maximum Caliber (MaxCal) as a more parsimonious alternative to existing dynamical modeling techniques.
  • To demonstrate MaxCal's ability to capture feedback effects with fewer assumptions and parameters.

Main Methods:

  • Developed Maximum Caliber (MaxCal), a dynamical modeling approach analogous to Maximum Entropy.
  • Utilized average rate quantities and correlations from short experimental trajectories.
  • Applied the method to model a bistable genetic toggle switch.

Main Results:

  • MaxCal reliably inferred the statistics of stochastic bistability in simulated data.
  • The method accurately predicted full dynamical distributions without complex reaction schemes.
  • Demonstrated the parsimony of MaxCal, requiring fewer model assumptions and parameters.

Conclusions:

  • Maximum Caliber (MaxCal) offers a more efficient and accurate stochastic modeling approach for biological feedback systems.
  • The method successfully models complex dynamics, such as stochastic bistability, from limited data.
  • MaxCal is broadly applicable to various small chemical and biochemical systems.