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

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

56
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...
56
Clearance Models: Compartment Models01:25

Clearance Models: Compartment Models

82
Clearance measures drug elimination from the central compartment, including plasma and highly perfused organs like kidneys and liver. Its calculation varies depending on pharmacokinetic models and administration routes. The one-compartment model, for instance, portrays the pharmacokinetics of polar drugs such as aminoglycoside antibiotics administered intravenously and readily excreted in urine. In this case, clearance is influenced by the terminal rate constant (λz) and the total volume...
82
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

43
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...
43
Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

86
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...
86
Modeling and Similitude01:12

Modeling and Similitude

268
Scaled modeling is a fundamental technique in engineering, enabling the study of large and complex systems by creating smaller, manageable replicas that recreate critical characteristics of the original. In hydrology and civil infrastructure, for example, scaled models of dams help analyze water flow, turbulence, and pressure. This method allows for accurate predictions of real-world behavior within a controlled environment, significantly reducing the cost and time involved in full-scale...
268
Per-Unit Sequence Models01:26

Per-Unit Sequence Models

74
An ideal Y-Y transformer, grounded through neutral impedances, displays per-unit sequence networks akin to those of a single-phase ideal transformer when subjected to balanced positive- or negative-sequence currents. These currents do not produce neutral currents, and their associated voltage drops.
Zero-sequence currents, which are identical in magnitude and phase, generate a neutral current, resulting in voltage drops across the neutral impedance and the low-voltage winding. If the...
74

You might also read

Related Articles

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

Sort by
Same author

Guiding AlphaFold predictions with experimental knowledge to inform dynamics and interactions with VAIRO.

Protein science : a publication of the Protein Society·2026
Same author

George M. Sheldrick (1942-2025).

Acta crystallographica. Section A, Foundations and advances·2025
Same author

SymProFold: Structural prediction of symmetrical biological assemblies.

Nature communications·2024
Same author

ARCIMBOLDO at low resolution: Verification for coiled coils and globular proteins.

Protein science : a publication of the Protein Society·2024
Same author

The molecular architecture of <i>Lactobacillus</i> S-layer: Assembly and attachment to teichoic acids.

Proceedings of the National Academy of Sciences of the United States of America·2024
Same author

Molecular and structural basis of oligopeptide recognition by the Ami transporter system in pneumococci.

PLoS pathogens·2024
Same journal

Scotty: lattice coincidences in the Protein Data Bank.

Acta crystallographica. Section D, Structural biology·2026
Same journal

Scotty: lattice coincidences for macromolecular crystallographic phasing.

Acta crystallographica. Section D, Structural biology·2026
Same journal

Miroslav Z. Papiz (1955-2026).

Acta crystallographica. Section D, Structural biology·2026
Same journal

Structural basis of regioselective double halogenation of the β-carboline tryptoline by the single-component halogenase AetF.

Acta crystallographica. Section D, Structural biology·2026
Same journal

Simulating neutron protein crystallography experiments: applications to the development of the NMX instrument at ESS.

Acta crystallographica. Section D, Structural biology·2026
Same journal

Molecular architecture of the human citrate synthase-malate dehydrogenase 2 metabolon.

Acta crystallographica. Section D, Structural biology·2026
See all related articles

Related Experiment Video

Updated: Jul 8, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.4K

Modes and model building in SHELXE.

Isabel Usón1, George M Sheldrick2

  • 1ICREA, Institució Catalana de Recerca i Estudis Avançats, Passeig Lluís Companys, 23, Barcelona, E-08003, Spain.

Acta Crystallographica. Section D, Structural Biology
|December 13, 2023
PubMed
Summary
This summary is machine-generated.

Density modification refines crystallographic electron density maps, aiding structure solution through experimental phasing. SHELXE program enhancements improve model building and phase accuracy for complete structural determination.

Keywords:
MRSADSHELXEdensity modificationmodel buildingphasing

More Related Videos

Finite Element Modelling of a Cellular Electric Microenvironment
08:23

Finite Element Modelling of a Cellular Electric Microenvironment

Published on: May 18, 2021

3.5K
A Rapid Method for Modeling a Variable Cycle Engine
04:58

A Rapid Method for Modeling a Variable Cycle Engine

Published on: August 13, 2019

7.6K

Related Experiment Videos

Last Updated: Jul 8, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.4K
Finite Element Modelling of a Cellular Electric Microenvironment
08:23

Finite Element Modelling of a Cellular Electric Microenvironment

Published on: May 18, 2021

3.5K
A Rapid Method for Modeling a Variable Cycle Engine
04:58

A Rapid Method for Modeling a Variable Cycle Engine

Published on: August 13, 2019

7.6K

Area of Science:

  • Crystallography
  • Structural Biology
  • Computational Chemistry

Background:

  • Density modification is crucial for solving experimental phasing methods.
  • Popular methods include single-wavelength and multi-wavelength anomalous diffraction.
  • It extends partial models and fragments into complete structures.

Purpose of the Study:

  • To illustrate the effect of density modification on starting maps using SHELXE.
  • To review the different operational modes of the SHELXE program.
  • To discuss extensions to the tracing algorithm for complete model building.

Main Methods:

  • Utilizing density modification to improve initial phase sets.
  • Employing polyalanine tracing for structure extension.
  • Implementing model bias elimination and sequence docking.
  • Analyzing phase distributions using Hendrickson-Lattman coefficients.

Main Results:

  • SHELXE program enhancements improve model building and phase accuracy.
  • The tracing algorithm now includes extensions for side-chain fitting.
  • A correlation coefficient threshold indicates successful structure solution.
  • Density modification effectively refines electron density maps.

Conclusions:

  • SHELXE's density modification and tracing extensions facilitate routine structure solution.
  • The program offers advanced features for handling phase information.
  • Enhanced algorithms lead to improved phasing performance and complete model building.