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 Experiment Videos

A general computational framework for modeling cellular structure and function

J Schaff1, C C Fink, B Slepchenko

  • 1Center for Biomedical Imaging Technology, Department of Physiology, University of Connecticut Health Center, Farmington 06030-1269, USA.

Biophysical Journal
|September 1, 1997
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Optogenetic manipulation of cardiac electrical dynamics using sub-threshold illumination: dissecting the role of cardiac alternans in terminating rapid rhythms.

Basic research in cardiology·2022
Same author

Arrhythmia susceptibility in a rat model of acute atrial dilation.

Progress in biophysics and molecular biology·2020
Same author

Real-time optical manipulation of cardiac conduction in intact hearts.

The Journal of physiology·2018
Same author

Development of a universal double-digest RAD sequencing approach for a group of nonmodel, ecologically and economically important insect and fish taxa.

Molecular ecology resources·2016
Same author

Paenibacillus larvae Phage Tripp Genome Has 378-Base-Pair Terminal Repeats.

Genome announcements·2016
Same author

Novel insights on the relationship between T-tubular defects and contractile dysfunction in a mouse model of hypertrophic cardiomyopathy.

Journal of molecular and cellular cardiology·2015
Same journal

Enhanced-Sampling Simulations Reveal Distinct Intermediates in SARS-CoV-2 FSE Pseudoknot Interconversion.

Biophysical journal·2026
Same journal

Structure-based simulations of the full Flock House virus capsid reveal pathways and energetics of an infection-critical peptide externalization event.

Biophysical journal·2026
Same journal

Quantifying the Peripheral Surface Information Entropy from Conformational Ensembles of Globular Protein-Peptide Complexes.

Biophysical journal·2026
Same journal

Anisotropic unbinding and location-dependent hovering of a kinesin motor head over microtubule.

Biophysical journal·2026
Same journal

Kinesin-5/Cut7 C-terminal tail phosphorylation influence on motor regulation through multi-scale molecular modeling.

Biophysical journal·2026
Same journal

Dynamic conformations of fluorophores on self-labeling protein tags.

Biophysical journal·2026
See all related articles

The Virtual Cell system models intracellular biochemical processes by integrating reaction data with microscopy images. This computational framework aids in understanding cell biology mechanisms and dynamics.

Area of Science:

  • Cell Biology
  • Computational Biology
  • Biophysics

Background:

  • Intracellular biochemical processes are complex, involving intricate distributions and dynamics.
  • Existing frameworks struggle to integrate diverse data types for comprehensive mechanism testing.

Purpose of the Study:

  • To introduce the Virtual Cell, a general system for testing cell biological mechanisms.
  • To create a framework for encapsulating knowledge on intracellular biochemical processes.

Main Methods:

  • Associating biochemical and electrophysiological data with experimental microscopic image data for subcellular localization.
  • Utilizing a physical and computational infrastructure to model molecular mechanisms expressible as rate equations or membrane fluxes.
  • Performing dynamic simulations, such as IP3-mediated Ca2+ release in neuronal cells.

Related Experiment Videos

Main Results:

  • The Virtual Cell system successfully integrates diverse data for modeling intracellular dynamics.
  • Dynamic simulation of Ca2+ release provided insights comparable to experimental observations.
  • The framework accommodates various molecular mechanisms.

Conclusions:

  • The Virtual Cell offers a robust platform for testing and understanding cell biological mechanisms.
  • It facilitates the integration of experimental and computational data for deeper biological insights.
  • The system enhances the study of experimentally inaccessible components within cellular mechanisms.