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

What is the Immune System?01:38

What is the Immune System?

Overview
Introduction to Innate and Adaptive Immunity01:21

Introduction to Innate and Adaptive Immunity

The human immune system is a complex defense mechanism that protects the body from harmful pathogens and foreign substances. It comprises two crucial components: innate and adaptive immunity.
Innate immunity is the body's natural, nonspecific defense system that acts quickly to protect against pathogens. It incorporates physical barriers like skin and mucous membranes and cellular elements such as phagocytes and natural killer cells. This part of our immune system provides an immediate,...
Introduction to Lymphatic and Immune System01:23

Introduction to Lymphatic and Immune System

Immunity is a crucial biological concept about our body's inherent capacity to prevent infections and diseases. A complex network of cells and tissues collectively known as the immune system facilitates this natural defense mechanism. The immune system plays an integral role in maintaining our health and well-being, shielding us from potential health threats.
The immune responses can be categorized into two types: innate and adaptive. Innate immunity comprises nonspecific defenses we are born...
Functions of the Lymphatic and Immune System01:28

Functions of the Lymphatic and Immune System

The lymphatic system plays a crucial role in bolstering our immune system. It consists of a network of lymphoid organs, lymph, and lymphatic vessels that provide structural and functional support in safeguarding the body against pathogens such as viruses and bacteria.
The primary lymphoid organs, including the bone marrow and the thymus, serve as the maturation sites for lymphocytes. Secondary lymphoid organs, like the mucosa-associated lymphoid tissue, activate these lymphocytes and serve as...
Immune Response Against Viral Pathogens01:29

Immune Response Against Viral Pathogens

The immune system's response to viral infections is a complex and coordinated process involving natural killer (NK) cells, T cell-mediated responses, and antibody-mediated responses.
NK Cells
NK cells are a crucial part of our innate immune system, acting as the first line of defense against viral infections. These cells can recognize and kill infected cells without prior exposure to the virus, effectively slowing down the spread of infection. Additionally, NK cells produce proinflammatory...
Development of Immunocompetence01:22

Development of Immunocompetence

The initiation of cell-mediated immunity can be observed as early as the third month of fetal growth, with active antibody-mediated immunity following approximately one month later.
The initial cells that migrate from the fetal thymus settle within the skin and epithelial tissues lining the mouth, digestive tract, and in females, the uterus and vagina. These cells, including skin-based dendritic cells, serve as antigen-presenting cells, playing a key role in T cell activation.
Subsequent T...

You might also read

Related Articles

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

Sort by
Same author

Microbial dysbiosis and inferred functional profiling reveals the potential role of <i>Methylobacterium</i> in prostate cancer.

Frontiers in cellular and infection microbiology·2026
Same author

Ensemble threshold Boolean modeling reveals robust attractors and regulatory drivers in pediatric leukemia.

Computers in biology and medicine·2026
Same author

Translational bioinformatics stalls at implementation.

Briefings in bioinformatics·2026
Same author

B-cell epitope prediction in the age of machine learning: advancements and challenges.

Journal of translational medicine·2026
Same author

Quantitative Method for Monitoring Tumor Evolution During and After Therapy.

Journal of personalized medicine·2025
Same author

Mimicking cancer therapy in an agent-based model: The case of hepatoblastoma.

Computer methods and programs in biomedicine·2025
Same journal

MCFST: Spatial domain identification method based on multi-view graph convolutional network and graph fusion network.

Bioinformatics (Oxford, England)·2026
Same journal

SpaBiT: Enhancing Spatial Transcriptomics Resolution via Bidirectional Attention Transformers.

Bioinformatics (Oxford, England)·2026
Same journal

EDEL: Enhancing Dense Retrievers for Curation of Biomedical Knowledge Bases.

Bioinformatics (Oxford, England)·2026
Same journal

Informative Relational Learning for Adverse Reaction Prediction with Enhanced Generalization to Novel Drugs.

Bioinformatics (Oxford, England)·2026
Same journal

An interpretable deep learning framework uncovers features governing CRISPR-Cas9 genome-editing efficiency.

Bioinformatics (Oxford, England)·2026
Same journal

3DICE: Interpretable 3D Cross-Modal Learning for Drug-Target Interaction Prediction and Large-Scale Drug Discovery.

Bioinformatics (Oxford, England)·2026
See all related articles

Related Experiment Video

Updated: May 31, 2026

Immunocompetent Intestine-on-Chip Model for Analyzing Gut Mucosal Immune Responses
08:48

Immunocompetent Intestine-on-Chip Model for Analyzing Gut Mucosal Immune Responses

Published on: May 24, 2024

Immune system simulation online.

Nicolas Rapin1, Ole Lund, Filippo Castiglione

  • 1Biotech Research & Innovation Centre and Bioinformatics Centre, University of Copenhagen, DK-2200 Copenhagen N, Denmark.

Bioinformatics (Oxford, England)
|June 21, 2011
PubMed
Summary
This summary is machine-generated.

This study enhances immune system simulations by integrating immuno-informatics for accurate antigenic peptide recognition. The improved model simulates B cell and T cell interactions with antigens, offering a more realistic approach to immune response modeling.

More Related Videos

A Data-Driven Approach to Quantifying Immune States in Sepsis
07:42

A Data-Driven Approach to Quantifying Immune States in Sepsis

Published on: February 7, 2025

Predictive Immune Modeling of Solid Tumors
08:50

Predictive Immune Modeling of Solid Tumors

Published on: February 25, 2020

Related Experiment Videos

Last Updated: May 31, 2026

Immunocompetent Intestine-on-Chip Model for Analyzing Gut Mucosal Immune Responses
08:48

Immunocompetent Intestine-on-Chip Model for Analyzing Gut Mucosal Immune Responses

Published on: May 24, 2024

A Data-Driven Approach to Quantifying Immune States in Sepsis
07:42

A Data-Driven Approach to Quantifying Immune States in Sepsis

Published on: February 7, 2025

Predictive Immune Modeling of Solid Tumors
08:50

Predictive Immune Modeling of Solid Tumors

Published on: February 25, 2020

Area of Science:

  • Computational immunology
  • Bioinformatics
  • Systems biology

Background:

  • Antigenic peptide recognition is a critical step in immune responses.
  • Existing mesoscopic-scale immune system simulators approximate this process.
  • A more accurate simulation of antigenic recognition is needed.

Purpose of the Study:

  • To enhance immune system simulations by incorporating advanced immuno-informatics methods.
  • To accurately model the cardinal events of antigenic recognition from peptides to proteomes.
  • To provide a more realistic simulation of lymphocyte-antigen interactions.

Main Methods:

  • Agent-based modeling of the immune system.
  • Integration of immuno-informatics tools for epitope prediction.
  • Calculation of B cell epitope affinity using Parker-scale estimation.
  • Prediction and binding of peptides to HLA class I and II molecules using position-specific scoring matrices.
  • TCR binding to HLA-peptide complexes modeled using residue-residue contact potentials.
  • Monte Carlo simulation of lymphocyte agent interactions.

Main Results:

  • Developed an agent-based model incorporating immuno-informatics for antigenic recognition simulation.
  • Enabled simulation of B cell epitope prediction and affinity estimation.
  • Implemented prediction and binding of peptides to HLA molecules.
  • Modeled T cell receptor binding to HLA-peptide complexes.
  • Simulated interactions for all lymphocytes encountering antigens in a Monte Carlo framework.

Conclusions:

  • The enhanced model provides a more accurate simulation of antigenic recognition in the immune system.
  • This approach allows for the study of immune responses at a finer level of detail.
  • The model offers a valuable tool for understanding immune system dynamics and developing new therapeutic strategies.