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

Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

1.9K
Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.
1.9K
Molecular Models02:00

Molecular Models

43.6K
Physical models representing molecular architectures of chemical compounds play essential roles in understanding chemistry. The use of molecular models makes it easier to visualize the structures and shapes of atoms and molecules.
43.6K
The Bohr Model02:18

The Bohr Model

80.5K
Following the work of Ernest Rutherford and his colleagues in the early twentieth century, the picture of atoms consisting of tiny dense nuclei surrounded by lighter and even tinier electrons continually moving about the nucleus was well established. This picture was called the planetary model since it pictured the atom as a miniature “solar system” with the electrons orbiting the nucleus like planets orbiting the sun. The simplest atom is hydrogen, consisting of a single proton as the...
80.5K
Stereotype Content Model02:16

Stereotype Content Model

15.4K
The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
15.4K
Clearance Models: Physiological Models01:09

Clearance Models: Physiological Models

298
Drug clearance is a critical pharmacokinetic process involving the irreversible removal of drugs from the body through various organs over a specified time period. Physiological models are indispensable in determining organ-specific clearance, defined by the proportion of the drug eliminated per unit of time from the organ's blood volume.
The organ's clearance rate depends on the blood flow to the organ and the extraction ratio (E). The extraction ratio describes the organ's...
298
Clearance Models: Compartment Models01:25

Clearance Models: Compartment Models

304
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...
304

You might also read

Related Articles

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

Sort by
Same author

Structural mechanism of anti-MHC-I antibody blocking of inhibitory NK cell receptors in tumor immunity.

Communications biology·2026
Same author

Structural mechanism of anti-MHC-I antibody blocking of inhibitory NK cell receptors in tumor immunity.

Research square·2025
Same author

CountASAP: a lightweight, easy to use python package for processing ASAPseq data.

BMC bioinformatics·2025
Same author

Moderated designs can balance between batch-effect mitigation and cell loss due to hashtag-assisted pooling in single-cell experiments.

bioRxiv : the preprint server for biology·2025
Same author

PARP Inhibition Shifts Murine Myeloid Cells Toward a More Tolerogenic Profile In Vivo.

Biomolecules·2025
Same author

Granzyme K<sup>+</sup> CD8 T cells slow tauopathy progression by targeting microglia.

Nature immunology·2025
Same journal

Mapping the 3D Chromosome Organization of a Biosynthetic Gene Cluster by Capture Hi-C (CHi-C).

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

Mapping the 3D Chromosome Organization of Streptomyces by Hi-C.

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

CUT&Tag Epigenomic Profiling of Biosynthetic Gene Clusters in Arabidopsis thaliana.

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

Rhizobium rhizogenes-Mediated Hairy Root Transformation Protocol for Lotus japonicus and Other Legumes.

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

Characterization of Bioactive Saponins from Sea Cucumbers.

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

Methods for Functional Validation of Terpenoid Metabolic Clusters in Nicotiana benthamiana and Aspergillus oryzae.

Methods in molecular biology (Clifton, N.J.)·2026
See all related articles

Related Experiment Video

Updated: Jan 26, 2026

Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.5K

Using Python for Spatially Resolved Modeling with Simmune.

Bastian R Angermann1, Martin Meier-Schellersheim2

  • 1Computational Biology Section, Laboratory of Systems Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA. bastianra@gmail.com.

Methods in Molecular Biology (Clifton, N.J.)
|April 5, 2019
PubMed
Summary
This summary is machine-generated.

This study demonstrates how Simmune and IPython enable reproducible analyses of cell signal transduction in complex, spatially varied environments. These computational tools enhance understanding of quantitative biological network behavior.

Keywords:
GPCR signalingGradient sensingIPythonMechanistic modeling

More Related Videos

Mining Spatial Transcriptomics Datasets using DeepSpaceDB
10:16

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Published on: September 5, 2025

694
Spatial Molecular Imaging of the Glycome Using Mass Spectrometry
08:52

Spatial Molecular Imaging of the Glycome Using Mass Spectrometry

Published on: November 28, 2025

495

Related Experiment Videos

Last Updated: Jan 26, 2026

Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.5K
Mining Spatial Transcriptomics Datasets using DeepSpaceDB
10:16

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Published on: September 5, 2025

694
Spatial Molecular Imaging of the Glycome Using Mass Spectrometry
08:52

Spatial Molecular Imaging of the Glycome Using Mass Spectrometry

Published on: November 28, 2025

495

Area of Science:

  • Cellular Biology
  • Computational Biology
  • Systems Biology

Background:

  • Mechanistic models are crucial for understanding quantitative behavior in cell-biological signal transduction networks.
  • Analyzing these networks in spatially inhomogeneous environments presents unique challenges.

Purpose of the Study:

  • To demonstrate the utility of Simmune in conjunction with IPython for analyzing signal transduction.
  • To enable repeatable and self-contained analyses in complex biological systems.

Main Methods:

  • Utilized Simmune, a computational tool for simulating biological systems.
  • Integrated Simmune with IPython for enhanced analysis and reproducibility.
  • Focused on modeling signal transduction in spatially inhomogeneous environments.

Main Results:

  • Successfully created repeatable and self-contained analyses of signal transduction.
  • Demonstrated the capability of Simmune and IPython for complex biological modeling.
  • Provided a framework for quantitative analysis in heterogeneous cellular environments.

Conclusions:

  • Simmune and IPython provide a powerful combination for studying cell-biological signal transduction.
  • This approach facilitates reproducible research in complex, spatially inhomogeneous biological systems.
  • The methodology enhances insights into the quantitative behavior of biological networks.