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Related Concept Videos

Antigen Presenting Cells01:22

Antigen Presenting Cells

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The immune system is a complex network of cells and molecules that protects the body from foreign invaders. T cells, a type of white blood cell, play a crucial role in this process. They recognize and attack foreign substances, such as pathogens, that enter the body.
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The Uncertainty Principle04:08

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Werner Heisenberg considered the limits of how accurately one can measure properties of an electron or other microscopic particles. He determined that there is a fundamental limit to how accurately one can measure both a particle’s position and its momentum simultaneously. The more accurate the measurement of the momentum of a particle is known, the less accurate the position at that time is known and vice versa. This is what is now called the Heisenberg uncertainty principle. He...
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Hardy-Weinberg Principle01:49

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Diploid organisms have two alleles of each gene, one from each parent, in their somatic cells. Therefore, each individual contributes two alleles to the gene pool of the population. The gene pool of a population is the sum of every allele of all genes within that population and has some degree of variation. Genetic variation is typically expressed as a relative frequency, which is the percentage of the total population that has a given allele, genotype or phenotype.
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The Pauli Exclusion Principle03:06

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The Aufbau Principle and Hund's Rule03:02

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To determine the electron configuration for any particular atom, we can build the structures in the order of atomic numbers. Beginning with hydrogen, and continuing across the periods of the periodic table, we add one proton at a time to the nucleus and one electron to the proper subshell until we have described the electron configurations of all the elements. This procedure is called the aufbau principle, from the German word aufbau (“to build up”). Each added electron occupies the...
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Le Chatelier's Principle: Changing Concentration02:27

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A system at equilibrium is in a state of dynamic balance, with forward and reverse reactions taking place at equal rates. If an equilibrium system is subjected to a change in conditions that affects these reaction rates differently (a stress), then the rates are no longer equal and the system is not at equilibrium. The system will subsequently experience a net reaction in the direction of a greater rate (a shift) that will re-establish the equilibrium. This phenomenon is summarized by Le...
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Updated: Feb 7, 2026

Development of an Antigen-driven Colitis Model to Study Presentation of Antigens by Antigen Presenting Cells to T Cells
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Learning the Principles of T Cell Antigen Discernment.

François X P Bourassa1, Sooraj Achar2,3,4, Grégoire Altan-Bonnet2

  • 1Joseph Henry Laboratories of Physics, Princeton University, Princeton, New Jersey, USA;

Annual Review of Biophysics
|February 5, 2026
PubMed
Summary
This summary is machine-generated.

T cell receptors (TCRs) use complex models to distinguish antigens, integrating signals for precise immune responses. Advances in data and computation refine these models for better immunotherapy design.

Keywords:
T cellantigen discernmentbiological physicskinetic proofreadingmachine learning

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Area of Science:

  • Immunology
  • Biophysics
  • Computational Biology

Background:

  • T cells are crucial for adaptive immunity, recognizing specific antigens with high sensitivity.
  • Understanding T cell receptor (TCR) antigen discrimination requires quantitative biophysical and theoretical models.
  • Current models face challenges due to antigen potency's continuum and nonlinear effects in mixtures.

Purpose of the Study:

  • To review theoretical frameworks for T cell receptor antigen recognition.
  • To integrate modern experimental and computational advances into theoretical models.
  • To explore how complex signaling influences immune decision-making.

Main Methods:

  • Review of theoretical frameworks for T cell receptor signaling.
  • Analysis of adaptive kinetic proofreading models integrating activating and inhibitory signals.
  • Incorporation of high-throughput data and machine learning approaches.

Main Results:

  • Antigen recognition is complex, spanning a continuum with nonlinear effects, challenging simple models.
  • Integrated models, like adaptive kinetic proofreading, are necessary to account for signal complexity.
  • Large-scale quantitative datasets enable model refinement via statistical and machine learning.

Conclusions:

  • The convergence of theory, data, and computation offers deeper insights into T cell-mediated immune responses.
  • Refined models of T cell antigen recognition can guide rational immunotherapy design.
  • Advanced modeling promises to enhance the precision and efficacy of immunotherapies.