Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Coordination Number and Geometry

Coordination Number and Geometry

For transition metal complexes, the coordination number determines the geometry around the central metal ion. Table 1 compares coordination numbers to molecular geometry. The most common structures of the complexes in coordination compounds are octahedral, tetrahedral, and square planar.

Predicting Molecular Geometry

Predicting Molecular Geometry

VSEPR Theory for Determination of Electron Pair Geometries

Molecular Geometry and Dipole Moments

Molecular Geometry and Dipole Moments

The VSEPR theory can be used to determine the electron pair geometries and molecular structures as follows:

Radicals: Electronic Structure and Geometry

Radicals: Electronic Structure and Geometry

This lesson delves into the geometry of a radical, which is influenced by the electronic structure of the molecule. The principle is similar to that of a lone pair, where the unpaired electron influences the geometry at the radical center.
Accordingly, the structure of a trivalent radical lies between the geometries of carbocations and carbanions. An sp2-hybridized carbocation is trigonal planar, while an sp3-hybridized carbanion is trigonal pyramidal. Here, the difference in geometry is...

Avoidance Learning and Learned Helplessness

Avoidance Learning and Learned Helplessness

Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...

What are Cells?

What are Cells?

Cells are the smallest and basic units of life, whether it is a single cell that forms the entire organism, e.g., in a bacterium or trillions of them, e.g., in humans. No matter what organism a cell is a part of, they share specific characteristics.
Basic Characteristics of Cells
A living cell has a plasma membrane, a bilayer of lipids that separates the aqueous solution inside the cell called the cytoplasm from the outside environment.
Furthermore, a living cell possesses genetic information...

You might also read

Related Articles

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

Sort by

Same author

HuBMAP Data Portal: A Resource for Multimodal Spatial and Single-Cell Data of Healthy Human Tissues.

ArXiv·2026

Same author

A multimodal adaptive optical microscope for in vivo imaging from molecules to organisms.

Nature methods·2026

Same author

The Common Fund Data Ecosystem (CFDE).

bioRxiv : the preprint server for biology·2026

Same author

Big1 is a cell-cycle regulator linking cell size to basal body number in Tetrahymena thermophila.

Current biology : CB·2026

Same author

SPRM: spatial process and relationship modeling for multiplexed images.

Bioinformatics advances·2026

Same author

Leave it alone: the natural history of growth-friendly graduates without a final fusion.

Spine deformity·2026

Same journal

Nanotechnology-Stem Cell Strategies in 3D Glioblastoma Organoid: Targeting Glioma Stem Cells Within a Complex Tumor Microenvironment.

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

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

See all related articles

Search research articles

Related Experiment Video

Updated: Jan 26, 2026

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Published on: March 3, 2023

CellOrganizer: Learning and Using Cell Geometries for Spatial Cell Simulations.

Timothy D Majarian^1,2,3, Ivan Cao-Berg¹, Xiongtao Ruan¹

¹Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, USA.

Methods in Molecular Biology (Clifton, N.J.)

|April 5, 2019

Summary

This summary is machine-generated.

This study introduces generative models from microscope images to create cell geometries for spatial simulations. These models capture cellular architecture and organelle details for enhanced biological process modeling.

Keywords:

Biochemical simulation Generative model Spatial organization

More Related Videos

Assessing Spatial Learning and Memory in Small Squamate Reptiles

Assessing Spatial Learning and Memory in Small Squamate Reptiles

Published on: January 3, 2017

Control of Cell Geometry through Infrared Laser Assisted Micropatterning

Control of Cell Geometry through Infrared Laser Assisted Micropatterning

Published on: July 10, 2021

Related Experiment Videos

Last Updated: Jan 26, 2026

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Published on: March 3, 2023

Assessing Spatial Learning and Memory in Small Squamate Reptiles

Assessing Spatial Learning and Memory in Small Squamate Reptiles

Published on: January 3, 2017

Control of Cell Geometry through Infrared Laser Assisted Micropatterning

Control of Cell Geometry through Infrared Laser Assisted Micropatterning

Published on: July 10, 2021

Area of Science:

Computational biology
Cell biology
Image analysis

Background:

Accurate spatial organization of cells is crucial for understanding biological processes.
Current methods for generating cell geometries for simulations often lack detailed architectural representation.
Integrating image data directly into model generation is needed.

Purpose of the Study:

To describe procedures for creating generative models of cellular spatial organization from microscope images.
To enable automatic generation of geometries for spatial simulations of cell processes.
To capture statistical variations in cell architecture, organelle distribution, and morphology.

Main Methods:

Image preparation and processing from microscopy data.
Learning generative models to capture cellular and subcellular structures.
Evaluation of model quality and accuracy.
Generating sampled cell geometries using diverse computational methods.
Integration of generated geometries with biochemical models for simulation.

Main Results:

Generative models accurately represent cell architecture and organelle features.
Automated generation of realistic cell geometries is achieved.
The process allows for capturing statistical variations in cell populations.
Enables the creation of detailed inputs for spatial cell simulations.

Conclusions:

Generative models derived from images provide a powerful tool for creating realistic cell geometries.
This approach enhances the accuracy and scope of spatial simulations for cell processes.
Facilitates the study of cell behavior and architecture through computational modeling.