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T Cell Activation and Clonal Selection

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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Cells of the Adaptive Immune Response

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Updated: Jun 26, 2026

VDJ-Seq: Deep Sequencing Analysis of Rearranged Immunoglobulin Heavy Chain Gene to Reveal Clonal Evolution Patterns of B Cell Lymphoma

VDJ-Seq: Deep Sequencing Analysis of Rearranged Immunoglobulin Heavy Chain Gene to Reveal Clonal Evolution Patterns of B Cell Lymphoma

Published on: December 28, 2015

A dynamic clonal selection immune clustering algorithm.

Yao-wen Chen¹, Lin Huang, Wei-ming Luo

¹Shantou University Medical College, Shantou 515041, China. ywchen@stu.edu.cn

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|January 24, 2009

Summary

This study introduces a novel dynamic clonal selection immune clustering algorithm. It offers improved clustering performance and real-world applicability without requiring prior knowledge.

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Single-cell Screening Method for the Selection and Recovery of Antibodies with Desired Specificities from Enriched Human Memory B Cell Populations

Single-cell Screening Method for the Selection and Recovery of Antibodies with Desired Specificities from Enriched Human Memory B Cell Populations

Published on: August 22, 2019

Area of Science:

Computational Intelligence
Bio-inspired Computing
Data Mining

Background:

Clustering algorithms often require pre-defined parameters or knowledge.
Immune system principles offer robust frameworks for complex problem-solving.
Dynamic adaptation is crucial for efficient clustering in evolving datasets.

Purpose of the Study:

To develop a novel dynamic clustering algorithm based on clonal selection and immune principles.
To eliminate the need for pre-existing knowledge in the clustering process.
To enhance clustering accuracy and adaptability.

Main Methods:

The algorithm employs clonal selection and hierarchical clustering principles.
It utilizes antibody affinity for antigen recognition, antibody regulation, and merging.
The aiNET immune network model facilitates dynamic mutation rate adjustment based on evolutionary generations.

Main Results:

The proposed algorithm demonstrates superior performance compared to traditional clustering methods.
Experimental results indicate a higher degree of coincidence with real-world clustering scenarios.
The dynamic mutation strategy improves adaptability and efficiency.

Conclusions:

The dynamic clonal selection immune clustering algorithm provides an effective, knowledge-free approach to data clustering.
The algorithm's adaptive nature and reliance on immune principles lead to enhanced performance.
This method offers a promising alternative for complex and dynamic clustering tasks.