The Influence of T Cell Development on Pathogen Specificity and Autoreactivity

@article{Komrlj2012TheIO,
  title={The Influence of T Cell Development on Pathogen Specificity and Autoreactivity},
  author={Andrej Ko{\vs}mrlj and Mehran Kardar and Arup K. Chakraborty},
  journal={Journal of Statistical Physics},
  year={2012},
  volume={149},
  pages={203-219}
}
T cells orchestrate adaptive immune responses upon activation. T cell activation requires sufficiently strong binding of T cell receptors on their surface to short peptides derived from foreign proteins bound to protein products of the major histocompatibility (MHC) gene products, which are displayed on the surface of antigen presenting cells. T cells can also interact with peptide-MHC complexes, where the peptide is derived from host (self) proteins. A diverse repertoire of relatively self… 
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6 Citations

6 Citations

Influence of correlated antigen presentation on T-cell negative selection in the thymus
TLDR
The results suggest that heterogeneity of gene co-expression in mTECs has an effect on the probability of escape of autoreactive thymocytes, and correlated patterns of peptide presentation may be advantageous in negatively selecting low-degeneracyThymocytes.
Broad cross-reactivity of the T-cell repertoire achieves specific and sufficiently rapid target searching.
Statistical Physics of T-Cell Development and Pathogen Specificity
In addition to an innate immune system that battles pathogens in a nonspecific fashion, higher organisms, such as humans, possess an adaptive immune system to combat diverse (and evolving) microbial…
Immunological Recognition by Artificial Neural Networks
TLDR
This work confirmed that ANNs could learn the immunological recognition of TCRs and peptide sequences and found that a homogenized encoding of amino acid sequence was more effective for the supervised learning task.
Artificial neural networks for immunological recognition
TLDR
This work examined whether the affinity-based discrimination of peptide sequences is learnable and generalizable by artificial neural networks that process the digital information of receptors and peptides and successfully captured the relevant information of molecular interactions and discriminated between strong and weak affinity pairs.
A Complete Bibliography of the Journal of Statistical Physics: 2000{2009
(2 + 1) [XTpXpH12, CTH11]. + [Zuc11b]. 0 [Fed17]. 1 [BELP15, CAS11, Cor16, Fed17, GDL10, GBL16, Hau16, JV19, KT12, KM19c, Li19, MN14b, Nak17, Pal11, Pan14, RT14, RBS16b, RY12, SS18c, Sug10, dOP18]. 1…

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