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Related Concept Videos

T Cell Types and Functions01:24

T Cell Types and Functions

When T cells with CD4 markers are activated, they give rise to two types of effector cells: helper T cells and regulatory T cells. Meanwhile, T cells with CD8 markers differentiate into effector cytotoxic T cells. The differentiation of CD4 T cells into helper T cell subsets, such as Th1, Th2, and Th17 cells, is dependent on the antigen type, antigen-presenting cell, and regulatory cytokines.
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Cytotoxic T cells are a vital component of the immune system. They have the remarkable ability to identify and target antigens on infected or abnormal cells. These antigens often originate from intracellular pathogens such as viruses or abnormal proteins cancer cells produce.
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T Cell Activation and Clonal Selection

T cells are integral to our adaptive immune system, recognizing and effectively responding to foreign antigens. T cell activation and clonal selection are pivotal in orchestrating this immune response. This article elucidates these mechanisms, detailing the roles of cluster of differentiation (CD) markers, major histocompatibility complex (MHC) molecules, costimulatory signals, and the process of clonal selection.
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NF-κB-dependent Signaling Pathway02:26

NF-κB-dependent Signaling Pathway

The transcription factor NF-κB was discovered in 1986 in the lab of Nobel laureate Professor David Baltimore, for its interaction with the immunoglobulin light chain enhancer in B-cells. After more than three decades of study, it is now evident that NF-κB regulates the expression of over 100 genes. Most of these genes play an essential role in the innate and adaptive immune responses as well as the inflammatory responses of animals.
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Cells of the Innate Immune Response01:28

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Related Experiment Video

Updated: Jun 2, 2026

Determination of the Relative Potency of an Anti-TNF Monoclonal Antibody (mAb) by Neutralizing TNF Using an In Vitro Bioanalytical Method
16:07

Determination of the Relative Potency of an Anti-TNF Monoclonal Antibody (mAb) by Neutralizing TNF Using an In Vitro Bioanalytical Method

Published on: September 16, 2017

Modular model of TNFalpha cytotoxicity.

Roberto Chignola1, Vladislav Vyshemirsky, Marcello Farina

  • 1Dipartimento di Biotecnologie, Università di Verona, Strada Le Grazie 15-CV1, I-37134 Verona, Italy.

Bioinformatics (Oxford, England)
|May 13, 2011
PubMed
Summary

We developed a robust computational model for Tumour Necrosis Factor alpha (TNF) signaling, differentiating between cell survival and death pathways. This model accurately describes TNF-R1 interactions and is stable across various parameters.

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Last Updated: Jun 2, 2026

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Published on: September 16, 2017

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Generation of Orthotopic Pancreatic Tumors and Ex vivo Characterization of Tumor-Infiltrating T Cell Cytotoxicity
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Published on: December 7, 2019

Area of Science:

  • Biochemistry
  • Systems Biology
  • Computational Biology

Background:

  • Tumour Necrosis Factor alpha (TNF) binding to its receptor (TNF-R1) triggers complex intracellular signaling pathways.
  • These pathways determine cellular fate, distinguishing between survival and apoptotic signals based on TNF/TNF-R1 complex localization.
  • Recent research has elucidated the molecular regulation of these signaling complexes.

Purpose of the Study:

  • To develop a reduced, effective computational model of TNF signaling.
  • To accurately describe the mechanisms leading to cell survival or death.
  • To analyze the model's stability and robustness.

Main Methods:

  • Development of a simplified model with few parameters, grouping intricate mechanisms into effective modules.
  • Parameter space analysis to demonstrate structural stability and robustness.
  • Application of Bayesian inference methods, specifically a Sequential Monte Carlo sampler, for parameter estimation from experimental data.

Main Results:

  • A robust computational model for TNF signaling pathways was successfully developed.
  • The model accurately describes the complex actions leading to cell survival or death.
  • The model demonstrates structural stability and robustness over a wide range of parameter values.

Conclusions:

  • The developed model provides a solid foundation for future TNF signaling research.
  • This model is suitable for integration into multi-scale simulation programs for studying tumor cell populations.
  • Supplementary materials including code and models are available.