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

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic Models: Compartment Models in Individual and Population Analysis

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...

PI3K/mTOR/AKT Signaling Pathway

PI3K/mTOR/AKT Signaling Pathway

The mammalian target of rapamycin (mTOR) is a serine/threonine kinase that regulates growth, proliferation, and cell survival in response to hormones, growth factors, or nutrient availability. This kinase exists in two structurally and functionally distinct forms: mTOR complex 1 (mTORC1) and mTOR complex 2 (mTORC2). The first form (mTORC1) is composed of a rapamycin-sensitive Raptor and proline-rich Akt substrate, PRAS40. In contrast, mTORC2 consists of a...

You might also read

Related Articles

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

Sort by

Same author

Microbiome-Genome Crosstalk in Colorectal Cancer: Colibactin Signatures and <i>Fusobacterium nucleatum</i> in Epidemiology, Driver Selection, and Translation.

International journal of molecular sciences·2026

Same author

Targeted Next-Generation Sequencing in Drug-Resistant Tuberculosis: WHO Guidance and Practical Implementation Priorities.

Biomedicines·2026

Same author

From CMS to iCMS/IMF: Developing Roadmap to Precision Therapy in Colorectal Cancer.

International journal of molecular sciences·2025

Same author

PredCMB: predicting changes in microbial metabolites based on the gene-metabolite network analysis of shotgun metagenome data.

Bioinformatics (Oxford, England)·2025

Same author

GVAF: generalized, flexible filtering software for annotated variant files.

Genes & genomics·2024

Same author

The lung microbiota in Korean patients with non-tuberculous mycobacterial pulmonary disease.

BMC microbiology·2021

Same journal

Cathepsin A deficiency exacerbates LPS-induced inflammatory liver injury and apoptosis.

Animal cells and systems·2026

Same journal

CD47-mediated purification of human ventricular cardiomyocytes derived from pluripotent stem cells.

Animal cells and systems·2026

Same journal

Influences of pupal presence and worker number on the survival and development of pharaoh ant (<i>Monomorium pharaonis</i>) brood under laboratory conditions.

Animal cells and systems·2026

Same journal

Manic switching from depression is affected by adult hippocampal neurogenesis.

Animal cells and systems·2026

Same journal

Oxidative stress and metabolic dysfunction in oral lichen planus pathogenesis.

Animal cells and systems·2026

Same journal

Enhancer RNA-mediated transcriptional regulation of TDP-43 during early neural lineage specification.

Animal cells and systems·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 2, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

Advances in modeling cellular state dynamics: integrating omics data and predictive techniques.

Sungwon Jung^1,2

¹Department of Genome Medicine and Science, Gachon University College of Medicine, Incheon, Republic of Korea.

Animal Cells and Systems

|January 14, 2025

Summary

This summary is machine-generated.

Dynamic modeling of cellular states is crucial for understanding biology. This review covers methods like network and deep learning models, integrating omics data to predict cell behavior and advance precision medicine.

Keywords:

Cellular state dynamics cell phenotype modeling cellular reprogramming disease progression modeling

More Related Videos

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

Related Experiment Videos

Last Updated: Jun 2, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

Area of Science:

Computational Biology
Systems Biology
Genomics

Background:

Cellular state dynamics are fundamental to biological processes like differentiation and disease.
Understanding these dynamics requires advanced computational approaches.

Purpose of the Study:

To provide a comprehensive review of current dynamic modeling approaches for cellular states.
To highlight the integration of omics data with these models for predictive insights.
To discuss applications in gene function, therapeutic design, and developmental biology.

Main Methods:

Review of dynamic and static biomolecular network models.
Exploration of deep learning methodologies for cellular state modeling.
Integration strategies with omics data (e.g., transcriptomics, single-cell RNA sequencing).

Main Results:

Various modeling techniques can capture and predict cellular behavior and transitions.
Modeling aids in predicting gene knockout effects and designing interventions.
These models are valuable for simulating complex biological systems like organ development.

Conclusions:

Selecting appropriate modeling strategies depends on system complexity, scalability, and resolution.
Advancements in modeling are crucial for developing robust, interpretable tools.
Improved models will enhance understanding and manipulation of cellular dynamics, advancing therapeutic strategies and precision medicine.