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

Block Diagram Reduction01:22

Block Diagram Reduction

727
The process of deriving the transfer function of a control system often involves reducing its block diagram to a single block. This simplification can be achieved through a series of strategic operations, including relocating branch points and comparators. These operations preserve the overall function of the system while allowing for easier manipulation and combination of blocks.
The first step in this process is the identification and relocation of a branch point. A branch point, where a...
727
State Space Representation01:27

State Space Representation

785
The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
785
Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

587
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...
587
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

359
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...
359
Woodward–Hoffmann Selection Rules and Microscopic Reversibility01:34

Woodward–Hoffmann Selection Rules and Microscopic Reversibility

2.2K
Electrocyclic reactions, cycloadditions, and sigmatropic rearrangements are concerted pericyclic reactions that proceed via a cyclic transition state. These reactions are stereospecific and regioselective. The stereochemistry of the products depends on the symmetry characteristics of the interacting orbitals and the reaction conditions. Accordingly, pericyclic reactions are classified as either symmetry-allowed or symmetry-forbidden. Woodward and Hoffmann presented the selection criteria for...
2.2K
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

438
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...
438

You might also read

Related Articles

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

Sort by
Same author

Multiphase coupling of phosphorus and organic matter across water-particle interaction in aquaculture ponds.

Journal of hazardous materials·2026
Same author

Particle size-dependent partitioning of phosphorus in aquaculture pond and estuarine systems.

Water research·2025
Same author

Gene-ius at work: Hemophilia B treatment enters a new era.

American journal of health-system pharmacy : AJHP : official journal of the American Society of Health-System Pharmacists·2025
Same author

Ultrafast Optically Induced Perturbation of Oxygen Octahedral Rotations in Multiferroic BiFeO<sub>3</sub> Thin Films.

Nano letters·2024
Same author

Time-Resolved Structural Measurement of Thermal Resistance across a Buried Semiconductor Heterostructure Interface.

Materials (Basel, Switzerland)·2023
Same author

Structural measurement of electron-phonon coupling and electronic thermal transport across a metal-semiconductor interface.

Scientific reports·2022

Related Experiment Video

Updated: May 3, 2026

RBDT: A Computerized Task System based in Transposition for the Continuous Analysis of Relational Behavior Dynamics in Humans
11:09

RBDT: A Computerized Task System based in Transposition for the Continuous Analysis of Relational Behavior Dynamics in Humans

Published on: July 17, 2021

2.5K

Model-independent decomposition of two-state data.

Eric C Landahl1, Sarah E Rice2

  • 1Department of Physics, DePaul University, Chicago, Illinois 60614, USA.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|February 4, 2014
PubMed
Summary

This study introduces a novel method to determine protein state populations directly from experimental data. It bypasses the need for pre-existing models, simplifying the analysis of protein dynamics.

More Related Videos

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

5.8K
Generating Strictly Controlled Stimuli for Figure Recognition Experiments
05:39

Generating Strictly Controlled Stimuli for Figure Recognition Experiments

Published on: March 18, 2019

4.7K

Related Experiment Videos

Last Updated: May 3, 2026

RBDT: A Computerized Task System based in Transposition for the Continuous Analysis of Relational Behavior Dynamics in Humans
11:09

RBDT: A Computerized Task System based in Transposition for the Continuous Analysis of Relational Behavior Dynamics in Humans

Published on: July 17, 2021

2.5K
Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

5.8K
Generating Strictly Controlled Stimuli for Figure Recognition Experiments
05:39

Generating Strictly Controlled Stimuli for Figure Recognition Experiments

Published on: March 18, 2019

4.7K

Area of Science:

  • Biophysics
  • Structural Biology
  • Biochemistry

Background:

  • Two-state models are commonly used to approximate protein behaviors like binding, folding, and conformational changes.
  • Existing methods for determining population ratios often require fitting spectra to known basis spectra or using least-squares optimization with assumed models.
  • These traditional approaches can be limited by the need for a priori information about the system.

Purpose of the Study:

  • To develop a method for directly determining the population ratio in a two-state protein system from experimental data.
  • To eliminate the requirement for pre-defined basis spectra or population models in data analysis.
  • To provide a more direct and model-independent approach to analyzing protein conformational dynamics.

Main Methods:

  • Singular Value Decomposition (SVD) applied to optical fluorescence, X-ray scattering, and Electron Paramagnetic Resonance (EPR) data.
  • Iterative application of physical constraints to the SVD results.
  • Model-independent data analysis without a priori assumptions on basis spectra or populations.

Main Results:

  • Successfully determined population ratios in two-state systems directly from raw data.
  • Demonstrated the applicability of the method across diverse biophysical techniques (fluorescence, X-ray scattering, EPR).
  • Validated a direct approach that circumvents the limitations of model-dependent fitting.

Conclusions:

  • The developed method offers a powerful, model-independent approach for analyzing two-state protein systems.
  • This technique simplifies the analysis of protein conformational changes and interactions.
  • It has broad implications for understanding protein dynamics across various experimental conditions and data types.