~/talk

The Adaptive Immune System as a Learning Algorithm

Talkat Computational Theories of Evolution · 2014-03-17..03-21
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slides:none on file

Frames HIV response as a search problem: simulate B cell–antigen shape space (bit strings with Hamming distance or longest common substring) and run mutation–selection with germline diversity ~10^13, peripheral sampling ~10^9 B cells, mutation rate ~10^-3 per base per generation and generation time 6–12 hours. Empirical targets: VRC01 neutralizes 90% of a 200-strain panel; broadly neutralizing antibodies (bnAbs) arise late (2–9 years), are ~0.1% of sampled B cells and carry ~5–15 VH mutations. Passive infusion of PGT121 gives a drastic viral load drop in rhesus monkeys, often with rebound.

AI-written summary · about

  1. 0:00HIV and therapeutic limits
  2. 2:20Broadly neutralizing antibodies
  3. 5:44Vaccine development challenges
  4. 9:17Immunology 101
  5. 13:26Immune system quantitative data
  6. 17:20Modeling immune response
  7. 22:50Shape space framework
  8. 26:43Evolutionary search hypothesis
  9. 30:02Q&A and immunodominance
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