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

The Multiscale Systems Immunology project: software for cell-based immunological simulation.

Faheem Mitha1, Timothy A Lucas, Feng Feng

  • 1Center for Computational Immunology, Department of Biostatistics & Bioinformatics, Duke University Medical Center, 2424 Erwin Road, Hock Plaza Suite G06, Durham NC 27705, USA. faheem@email.unc.edu.

Source Code for Biology and Medicine
|April 30, 2008
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

A Tree Perspective on Stick-Breaking Models in Covariate-Dependent Mixtures (with Discussion).

Bayesian analysis·2026
Same author

Coarsened Mixtures of Hierarchical Skew Normal Kernels for Flow and Mass Cytometry Analyses.

Bayesian analysis·2026
Same author

Polygenic risk scores and HLA class II variants are biomarkers of corticosteroid response in childhood nephrotic syndrome.

Kidney international·2026
Same author

Profiling Allogeneic HLA-specific B-cell Responses Utilizing a 64-plex Single-HLA Reporter Cell Panel.

bioRxiv : the preprint server for biology·2026
Same author

A rhesus macaque model of congenital cytomegalovirus infection reveals a spectrum of vertical transmission outcomes.

Communications biology·2025
Same author

HIV-associated non-Hodgkin lymphoma tumor-microenvironment axes differ by EBV status across cellular origins.

bioRxiv : the preprint server for biology·2025
Same journal

2DKD: a toolkit for content-based local image search.

Source code for biology and medicine·2020
Same journal

Computing and graphing probability values of pearson distributions: a SAS/IML macro.

Source code for biology and medicine·2020
Same journal

iPBAvizu: a PyMOL plugin for an efficient 3D protein structure superimposition approach.

Source code for biology and medicine·2019
Same journal

Social support for collaboration and group awareness in life science research teams.

Source code for biology and medicine·2019
Same journal

MZPAQ: a FASTQ data compression tool.

Source code for biology and medicine·2019
Same journal

IPCAPS: an R package for iterative pruning to capture population structure.

Source code for biology and medicine·2019
See all related articles

This study introduces the Multiscale Systems Immunology (MSI) simulation framework, a flexible agent-based model for predicting immune responses to vaccination. MSI enhances biological simulations with realistic biophysics and intracellular dynamics for high-performance computing.

Area of Science:

  • Computational Biology
  • Immunology
  • Systems Biology

Background:

  • Computer simulations are vital for predicting biological phenomena and guiding experimental research.
  • Modeling the immune system requires capturing its multi-scale complexity and dynamics.
  • Agent-based modeling offers a powerful approach to simulating biological systems.

Purpose of the Study:

  • To develop a flexible and high-performance agent-based simulation framework for modeling the early immune response to vaccination.
  • To incorporate realistic biophysics and intracellular dynamics into immune response simulations.
  • To enable the accurate modeling of multi-scale processes within the immune system.

Main Methods:

  • Developed the Multiscale Systems Immunology (MSI) simulation framework using C++ and Python.

Related Experiment Videos

  • Implemented an object-oriented and modular design for flexible component configuration.
  • Enabled parameter initialization for simulations across different temporal and spatial scales.

    • Main Results:

    • The MSI framework provides a modular and flexible simulation environment.
    • The software facilitates the modeling of complex immune system processes.
    • Simulations can be configured to represent diverse biological scales.

    Conclusions:

    • MSI fulfills the need for a versatile and efficient agent-based model of the immune system.
    • The framework supports advanced computational immunology research.
    • MSI is suitable for high-performance scientific computing applications in immunology.