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

Energy to Drive Translocation01:37

Energy to Drive Translocation

2.2K
Mitochondrial protein import is powered by two distinct energy sources: ATP hydrolysis and electrochemical potential across the inner membrane. Newly synthesized precursors are bound by cytosolic chaperones of the Hsp70 family, which guide them to the import receptors on the mitochondrial surface. Utilizing the energy of ATP hydrolysis, Hsp70 chaperones transfer these precursors to the TOM receptors on the mitochondrial outer membrane.
Generally, polypeptides are unfolded by two distinct...
2.2K
Cell Potential and Free Energy02:58

Cell Potential and Free Energy

42.4K
Thermodynamics of a Redox Reaction
Thermodynamics is the branch of physics dealing with the relationship between heat and other forms of energy. In an electrochemical cell, chemical energy is converted into electrical energy.
Thus, a link can be predicted between cell potential, free energy change, and the equilibrium constant for the reaction. Cell potential can also be measured as the oxidant or the reducing strength, and similar acid-base strength measures are reflected in equilibrium...
42.4K
Non-equilibrium in the Cell01:16

Non-equilibrium in the Cell

4.9K
An important concept in studying metabolism and energy is that of chemical equilibrium. Most chemical reactions are reversible. They can proceed in both directions, releasing energy into their environment in one direction, and absorbing it from the environment in the other direction. The same is true for the chemical reactions involved in cell metabolism, such as the breaking down and building up of proteins into and from individual amino acids, respectively. Reactants within a closed system...
4.9K
Cell Specific Gene Expression01:58

Cell Specific Gene Expression

4.9K
4.9K
Cellular Differentiation00:57

Cellular Differentiation

4.0K
How does a complex organism such as a human develop from a single cell? It all starts from a single fertilized egg which gives rise to a vast array of cell types, such as nerve cells, muscle cells, and epithelial cells that characterize the adult? Throughout development and adulthood, cellular differentiation leads cells to assume their final morphology and physiology. Differentiation is the process by which unspecialized cells become specialized to carry out distinct functions.
A zygote is a...
4.0K
Protein Dynamics in Living Cells01:19

Protein Dynamics in Living Cells

2.3K
Different fluorescence-based techniques are used to study the protein dynamics in living cells. These techniques include FRAP, FRET, and PET.
Fluorescent recovery after photobleaching (FRAP) is a fluorescent-protein-based detection technique used to quantify protein movement rates within the cell. This method exposes a small portion of the cell to an intense laser beam. The laser beam causes permanent photobleaching of the fluorophore-tagged proteins in the exposed region. As the bleached...
2.3K

You might also read

Related Articles

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

Sort by
Same author

CPS: mapping physical coordinates to high-fidelity spatial transcriptomics via privileged multi-scale context distillation.

Bioinformatics (Oxford, England)·2026
Same author

Learning collective multicellular dynamics with an interacting mean field neural SDE model.

PLoS computational biology·2026
Same author

A physics-informed neural SDE network for learning cellular dynamics from time-series scRNA-seq data.

Bioinformatics (Oxford, England)·2024
Same author

scVIC: deep generative modeling of heterogeneity for scRNA-seq data.

Bioinformatics advances·2024
Same author

A multi-view graph contrastive learning framework for deciphering spatially resolved transcriptomics data.

Briefings in bioinformatics·2024
Same author

Association of perchlorate, thiocyanate, and nitrate with dyslexic risk.

Chemosphere·2023

Related Experiment Video

Updated: Oct 5, 2025

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

2.3K

Dynamic inference of cell developmental complex energy landscape from time series single-cell transcriptomic data.

Qi Jiang1,2, Shuo Zhang1,2, Lin Wan1,2

  • 1NCMIS, LSC, LSEC, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China.

Plos Computational Biology
|January 24, 2022
PubMed
Summary

GraphFP reconstructs cell differentiation dynamics from single-cell RNA sequencing data. This model reveals cell-cell interactions and potential energy landscapes driving cell state transitions.

More Related Videos

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues
10:12

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

18.7K
Visualization and Analysis of mRNA Molecules Using Fluorescence In Situ Hybridization in Saccharomyces cerevisiae
07:00

Visualization and Analysis of mRNA Molecules Using Fluorescence In Situ Hybridization in Saccharomyces cerevisiae

Published on: June 14, 2013

35.0K

Related Experiment Videos

Last Updated: Oct 5, 2025

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

2.3K
Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues
10:12

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

18.7K
Visualization and Analysis of mRNA Molecules Using Fluorescence In Situ Hybridization in Saccharomyces cerevisiae
07:00

Visualization and Analysis of mRNA Molecules Using Fluorescence In Situ Hybridization in Saccharomyces cerevisiae

Published on: June 14, 2013

35.0K

Area of Science:

  • Computational Biology
  • Developmental Biology
  • Systems Biology

Background:

  • Time series single-cell RNA sequencing (scRNA-seq) data enable studying cellular dynamics.
  • Inferring cell population evolution from scRNA-seq is complex due to biological stochasticity and nonlinearity.
  • Advanced mathematical models are needed to reconstruct dynamic cell transitions and interactions.

Purpose of the Study:

  • To develop GraphFP, a novel framework for dynamic inference from time series scRNA-seq data.
  • To reconstruct cell state-transition potential energy landscapes and uncover nonlinear cell-cell interactions.
  • To provide a robust method for analyzing cellular differentiation processes.

Main Methods:

  • GraphFP utilizes a nonlinear Fokker-Planck equation on a graph.
  • The model incorporates cell-cell interactions via a nonlinear quadratic term in free energy.
  • Inference is framed as a dynamic optimal transport problem, solved using optimal control adjoint methods.

Main Results:

  • GraphFP successfully reconstructs cell state potential energy, indicating cellular differentiation potency.
  • The framework accurately maps probability flows between cell states during differentiation.
  • It quantifies stochastic cell type frequency dynamics on a probability simplex in continuous time.
  • GraphFP demonstrates robustness to variations in clustering resolution and parameter choices.

Conclusions:

  • GraphFP offers a powerful, model-based approach to analyze cell differentiation dynamics from scRNA-seq data.
  • The framework effectively delineates cell-cell interactions driving developmental processes.
  • GraphFP provides insights into the complex potential energy landscape governing cell state transitions.