Physics of the Immune System
Summer Semester 2025
Summer Semester 2025
Time: Thursdays and Fridays, 10:00 am.
April 10: The biology of a viral infection [1, 2] (See video).
April 11: What is the physics of the Immune system? [3, 4]
Exercise 1: Immunology by the numbers (Exercise sheet)
Binding kinetics, specificity and introduction to kinetic proofreading [5, 6]
Genotype-phenotype-function maps [7, 8].
Exercise 2: Immune system in bacteria (Exercise sheet)
T cells and Antigen presentation [9]
Stochastic generation and thymic selection [10]
Size of an immune repertoires [11, 12, 13]
Exercise 3: VDJ recombination (Exercise sheet)
Cooperativity in T cells [14]
B cell activation and acute infections [15]
Exercise 4: A metric shape space? (Exercise sheet)
Escape dynamics of HIV [16]
Affinity maturation [17, 18]
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Immunological memory in theory [19]
Immunological memory in practice [20]
Multi-locus antigen diversity [21]
Influenza evolution [22]
Exercise 5: Multi-epitope model (Exercise sheet)
Immune channels & vaccine selection [23, 24]
Intrinsic vs. antigenic fitness landscapes [25]
Two-headed Dragon of viral evolution (No reference for this lecture)
[1] Phillips, R., Kondev, J., Theriot, J., Garcia, H. G. & Orme, N. Physical Biology of the Cell (Garland Science, 2012).
[2] Kuriyan, J., Konforti, B. & Wemmer, D. The Molecules of Life (Garland Science, 2012).
[3] Perelson, A. S. & Weisbuch, G. Immunology for physicists. Rev. Mod. Phys. 69, 1219–1267 (1997).
[4] Altan-Bonnet, G., Mora, T. & Walczak, A. M. Quantitative immunology for physicists. Phys. Rep. 849, 1–83 (2020).
[5] Hopfield, J. J. Kinetic proofreading: A new mechanism for reducing errors in biosynthetic processes requiring high specificity. Proc. Natl. Acad. Sci. USA 71, 4135–4139 (1974).
[6] Adams, R. M., Mora, T., Walczak, A. M. & Kinney, J. B. Measuring the sequence-affinity landscape of antibodies with massively parallel titration curves. eLife 5, e23156 (2016).
[7] Stormo, G. D. & Fields, D. S. Specificity, free energy and information content in protein-DNA interactions. Trends Biochem. Sci. 23, 109–113 (1998).
[8] Mustonen, V., Kinney, J., Callan, C. G. & Lässig, M. Energy-dependent fitness: A quantitative model for the evolution of yeast transcription factor binding sites. Proc. Natl. Acad. Sci. USA 105, 12376–12381 (2008).
[9] Luksza, M. et al. A neoantigen fitness model predicts tumour response to checkpoint blockade immunotherapy. Nature 551, 517–520 (2017).
[10] Košmrlj, A., Kardar, M. & Chakraborty, A. K. Statistical physics of T-cell development and pathogen specificity. Annu. Rev. Condens. Matter Phys. 4, 339–360 (2013).
[11] Perelson, A. S. & Oster, G. F. Theoretical studies of clonal selection: Minimal antibody repertoire size and reliability of self–non-self discrimination. J. Theor. Biol. 81, 645–670 (1979).
[12] de Boer, R. J. & Perelson, A. S. How diverse should the immune system be? Proc. R. Soc. B 252, 171–175 (1993).
[13] de Boer, R. J., Kesmir, C., Perelson, A. S. & Borghans, J. A. M. Is the exquisite specificity of lymphocytes generated by thymic selection or due to evolution? Front. Immunol. 15, 1266349 (2024).
[14] Butler, T. C., Kardar, M. & Chakraborty, A. K. Quorum sensing allows T cells to discriminate between self and nonself. Proc. Natl. Acad. Sci. USA 110, 11833–11838 (2013).
[15] Morán-Tovar, R. & Lässig, M. Nonequilibrium antigen recognition during infections and vaccinations. Phys. Rev. X 14, 031026 (2024).
[16] Meijers, M., Vanshylla, K., Gruell, H., Klein, F. & Lässig, M. Predicting in vivo escape dynamics of HIV-1 from a broadly neutralizing antibody. Proc. Natl. Acad. Sci. USA 118, e2104651118 (2021).
[17] Tas, J. M. J., Mesin, L., Pasqual, G., Targ, S., Jacobsen, J. T., Mano, Y. M., Chen, C. S., Weill, J.-C., Reynaud, C.-A., Browne, E. P., Meyer-Hermann, M., & Victora, G. D. Visualizing antibody affinity maturation in germinal centers. Science 351, 1048–1054 (2016).
[18] Victora, G. D. & Nussenzweig, M. C. Germinal centers. Annu. Rev. Immunol. 40, 413–442 (2022).
[19] Chardès, V., Vergassola, M., Walczak, A. M. & Mora, T. Affinity maturation for an optimal balance between long-term immune coverage and short-term resource constraints. Proc. Natl. Acad. Sci. U.S.A. 119, e2113512119 (2022).
[20] Mesin, L. et al. Restricted Clonality and Limited Germinal Center Reentry Characterize Memory B Cell Reactivation by Boosting. Cell 180, 92-106.e11 (2020).
[21] Georgieva, M., Buckee, C. O. & Lipsitch, M. Models of immune selection for multi-locus antigenic diversity of pathogens. Nat Rev Immunol 19, 55–62 (2019).
[22] Łuksza, M. & Lässig, M. A predictive fitness model for influenza. Nature 507, 57–61 (2014).
[23] Meijers, M., Ruchnewitz, D., Eberhardt, J., Łuksza, M. & Lässig, M. Population immunity predicts evolutionary trajectories of SARS-CoV-2. Cell 186, 5151-5164.e13 (2023).
[24] Thadani, N.N., Gurev, S., Notin, P. et al. Learning from prepandemic data to forecast viral escape. Nature 622, 818–825 (2023).
[25] Taft, J. M., Weber, C. R., Gao, B., Ehling, R. A., Han, J., Frei, L., Metcalfe, S. W., Overath, M. D., Yermanos, A., Kelton, W. & Reddy, S. T. Deep mutational learning predicts ACE2 binding and antibody escape to combinatorial mutations in the SARS-CoV-2 receptor-binding domain. Cell 185, 4008–4022.e14 (2022).