×

Quantitative immunology for physicists. (English) Zbl 1436.92005

Summary: The adaptive immune system is a dynamical, self-organized multiscale system that protects vertebrates from both pathogens and internal irregularities, such as tumors. For these reasons it fascinates physicists, yet the multitude of different cells, molecules and sub-systems is often also petrifying. Despite this complexity, as experiments on different scales of the adaptive immune system become more quantitative, many physicists have made both theoretical and experimental contributions that help predict the behavior of ensembles of cells and molecules that participate in an immune response. Here we review some recent contributions with an emphasis on quantitative questions and methodologies. We also provide a more general methods section that presents some of the wide array of theoretical tools used in the field.

MSC:

92C30 Physiology (general)
92C37 Cell biology
92C40 Biochemistry, molecular biology
92D10 Genetics and epigenetics
92D25 Population dynamics (general)
92D40 Ecology
92-02 Research exposition (monographs, survey articles) pertaining to biology
PDFBibTeX XMLCite
Full Text: DOI arXiv

References:

[1] Sompayrac, L., How the Immune System Works (1999), Blackwell Scientific Publications
[2] Burnet, F. M., A modification of Jerne’ s theory of antibody production using the concept of clonal selection, Aust. J. Sci., 20, 67-69 (1957)
[3] Ehrlich, P., On immunity with special reference to cell life, Roy. Soc. Proc., 66, 424-448 (1900)
[4] Jerne, N. K., The natural-selection theory of antibody formation, Proc. Natl. Acad. Sci. U.S.A, 41, 849-857 (1955)
[5] Hodgkin, P. D.; Heath, W. R.; Baxter, A. G., The clonal selection theory : 50 years since the revolution, Nat. Immunol., 66, 424-448 (2007)
[6] Nossal, G. J.V.; Lederberg, J., Antibody production by single cells. 1, J. Immunol., 182, 1231-1232 (2019)
[7] Billingham, R. E.; Brent, L.; Medawar, P. B., Activity acquired tolerance of foreign cells, J. Immunol., 172, 603-606 (1953)
[8] Billingham, R. E.; Brent, L.; Medawar, P. B., Quantitative studies on tissue transplantation immunity. III. Actively acquired tolerance, Philos. Trans. R. Soc. B: Biol. Sci., 239, 378-414 (1956)
[9] Lifschitz, E.; Pitaevskii, L., Physical Kinetics (1981), Butterworth-Heinemann
[10] Pecht, I.; Givol, D.; Sela, M., Dynamics of hapten-antibody interaction. studies on a myeloma protein with anti-2, 4-dinitrophenyl specificity, J. Mol. Biol., 68, 241-247 (1972)
[11] Northrup, S. H.; Erickson, H. P., Kinetics of protein-protein association explained by brownian dynamics computer simulation, Proc. Natl. Acad. Sci. U.S.A, 89, 3338-3342 (1992)
[12] Hager, G. L.; McNally, J. G.; Misteli, T., Transcription dynamics, Mol. Cell, 35, 741-753 (2009)
[13] Halford, S. E.; Marko, J. F., How do site-specific dna-binding proteins find their targets?, Nucleic Acids Res., 32, 3040-3052 (2004)
[14] Gorman, J.; Greene, E. C., Visualizing one-dimensional diffusion of proteins along dna, Nat. Struct. Mol. Biol., 15, 768-774 (2008)
[15] Slutsky, M.; Mirny, L. A., Kinetics of protein-DNA interaction : Facilitated target location in sequence-dependent potential, Biophys. J., 87, 4021-4035 (2004)
[16] Berg, H. C.; Purcell, E. M., Physics of chemoreception, Biophys. J., 20, 193-219 (1977)
[17] Altan-Bonnet, G.; Germain, R. N., Modeling T cell antigen discrimination based on feedback control of digital erk responses, PLoS Biol., 3, Article e356 pp. (2005)
[18] Feinerman, O.; Germain, R. N.; Altan-Bonnet, G., Quantitative challenges in understanding ligand discrimination by \(\alpha \beta\) T cells, Mol. Immunol., 45, 619-631 (2008)
[19] Inman, J.; Bell, G. H.; Perelson, A. S.; Pimbley, G., The antibody combining region: Speculations on the hypothesis of general multispecificity, (Bell, G. I.; Perelson, A. S.; Pimbley, G. H.Jr., Theoretical Immunology (1978), Marcel Dekker: Marcel Dekker NY), 243-278, (ed.)
[20] Yates, A. J., Theories and quantification of thymic selection, Front. Immunol., 5, 13 (2014)
[21] Perelson, A. S.; Oster, G. F., Theoretical studies of clonal selection minimal antibody repertoire size and reliability of self non self discrimination, J. Theoret. Biol., 81, 645-670 (1979)
[22] Mason, D., A very high level of crossreactivity is an essential feature of the T- cell receptor, Immunol. Today, 19, 395-404 (1998)
[23] Press, J. L.; Klinman, N. R., Frequency of hapten-specific B cells in neonatal and adult murine spleens, Eur. J. Immunol., 4, 155-159 (1974)
[24] Sigal, N. H.; Gearhart, P. J.; Press, J. L.; Klinman, N. R., Late acquisition of a germ line antibody specificity, Nature, 259, 51-52 (1976)
[25] de Boer, R. J.; Perelson, A. S., How diverse should the immune system be?, Proc. R. Soc. B: Biol. Sci., 252, 171-175 (1993)
[26] Birnbaum, M. E., Deconstructing the peptide-MHC specificity of T cell recognition, Cell, 157, 1073-1087 (2014)
[27] Adams, R.; Kinney, J. B.; Mora, T.; Walczak, A. M., Measuring the sequence-affinity landscape of antibodies with massively parallel titration curves, eLife, 5, Article e23156 pp. (2016)
[28] Glanville, J., Identifying specificity groups in the T cell receptor repertoire, Nature, 547, 94-98 (2017)
[29] Dash, P., Quantifiable predictive features define epitope-specific T cell receptor repertoires, Nature, 547, 89-93 (2017)
[30] Shugay, M., VDJdb: A curated database of T-cell receptor sequences with known antigen specificity, Nucleic Acids Res., 46, D419-D427 (2018)
[31] Tickotsky, N.; Sagiv, T.; Prilusky, J.; Shifrut, E.; Friedman, N., McPAS-TCR: A manually curated catalogue of pathology-associated T cell receptor sequences, Bioinformatics, 33, 2924-2929 (2017)
[32] Jurtz, V. I., NetTCR: sequence-based prediction of TCR binding to peptide-MHC complexes using convolutional neural networks (2018), bioRxiv 433706
[33] Sidhom, J. W.; Larman, H. B.; Pardoll, D. M.; Baras, A. S., DeepTCR: a deep learning framework for revealing structural concepts within TCR repertoire (2018), bioRxiv doi: https://doi.org/10.1101/464107
[34] Jokinen, E.; Heinonen, M.; Huuhtanen, J.; Mustjoki, S.; Harri, L., TCRGP : Determining epitope specificity of t cell receptors (2019), bioRxiv/10.1101/542332
[35] Farmer, J. D.; Packard, N. H.; Perelson, A. S., The immune system, adaptation, and machine learning, Physica D, 22, 187-204 (1986)
[36] Chao, D. L.; Davenport, M. P.; Forrest, S.; Perelson, A. S., The effects of thymic selection on the range of T cell cross-reactivity, Eur. J. Immunol., 35, 3452-3459 (2005)
[37] Lee, H. Y.; Perelson, A. S., Computational models of B cell and T cell receptors, (Flower, D.; Timmis, J., In Silico Immunology (2007), Springer: Springer Boston, Ma), 65-81
[38] Miyazawa, S.; Jernigan, R. L., Residue-residue potentials with a favorable contact pair term and an unfavorable high packing density term, for simulation and threading, J. Mol. Biol., 256, 623-644 (1996)
[39] Kosmrlj, A.; Jha, A. K.; Huseby, E. S.; Kardar, M.; Chakraborty, A. K., How the thymus designs antigen-specific and self-tolerant t cell receptor sequences, Proc. Natl. Acad. Sci. U.S.A, 105, 16671-16676 (2008)
[40] George, J. T.; Kessler, D. A.; Levine, H., Effects of thymic selection on T cell recognition of foreign and tumor antigenic peptides, Proc. Natl. Acad. Sci. U.S.A, 114, E7875-E7881 (2017)
[41] Košmrlj, A.; Chakraborty, A. K.; Kardar, M.; Shakhnovich, E. I., Thymic selection of T-cell receptors as an extreme value problem, Phys. Rev. Lett., 103, Article 068103 pp. (2009)
[42] Butler, T. C.; Kardar, M.; Chakraborty, A. K., Quorum sensing allows T cells to discriminate between self and nonself, Proc. Natl. Acad. Sci. U.S.A, 110, 11833-11838 (2013)
[43] Detours, V.; Mehr, R.; Perelson, A. S., A quantitative theory of affinity-driven T cell repertoire selection, J. Theoret. Biol., 200, 389-403 (1999)
[44] Detours, V.; Mehr, R.; Perelson, a., Deriving quantitative constraints on T cell selection from data on the mature T cell repertoire, J. Immunol., 164, 121-128 (2000)
[45] Wang, S., Manipulating the selection forces during affinity maturation to generate cross-reactive HIV antibodies, Cell, 160, 785-797 (2015)
[46] Nourmohammad, A.; Otwinowski, J.; Plotkin, J. B., Host-pathogen co-evolution and the emergence of broadly neutralizing antibodies in chronic infections, PLoS Genet., 12, Article e1006171 pp. (2015)
[47] Luo, S.; Perelson, A. S., Competitive exclusion by autologous antibodies can prevent broad HIV-1 antibodies from arising, Proc. Natl. Acad. Sci. U.S.A, 112, 11654-11659 (2015)
[48] Adams, R. M.; Kinney, J. B.; Walczak, A. M.; Mora, T., Epistasis in a fitness landscape defined by antibody-antigen binding free energy, Cell Syst., 8, 1, 86-93 (2019)
[49] Luksza, M., A neoantigen fitness model predicts tumour response to checkpoint blockade immunotherapy, Nature, 551, 517-520 (2017)
[50] Andreatta, M.; Nielsen, M., Gapped sequence alignment using artificial neural networks: Application to the MHC class I system, Bioinformatics, 32, 511-517 (2015)
[51] T. W., McKeithan, Kinetic proofreading in T-cell receptor signal transduction, Proc. Natl. Acad. Sci. U.S.A, 92, 5042-5046 (1995)
[52] Hopfield, J. J., Kinetic proofreading: a new mechanism for reducing errors in biosynthetic processes requiring high specificity, Proc. Natl. Acad. Sci. U.S.A, 71, 4135-4319 (1974)
[53] Ninio, J., Kinetic amplification of enzyme discrimination, Biochimie, 57, 587-595 (1975)
[54] François, P.; Voisinne, G.; Siggia, E. D.; Altan-Bonnet, G.; Vergassola, M., Phenotypic model for early T-cell activation displaying sensitivity, specificity, and antagonism, Proc. Natl. Acad. Sci. U.S.A, 110, E888-E897 (2013)
[55] Germain, R. N., Modeling T cell antigen discrimination based on feedback control of digital ERK responses, PLoS Biol., 3, 11, Article e356 pp. (2005)
[56] Sykulev, Y.; Joo, M.; Vturina, I.; Tsomides, T. J.; Eisen, H. N., Evidence that a single peptide-MHC complex on a target cell can elicit a cytolytic T cell response, Immunity, 4, 565-571 (1996)
[57] Irvine, D. J.; Purbhoo, M. A.; Krogsgaard, M.; Davis, M. M., Direct observation of ligand recognition by T cells, Nature, 419, 845-849 (2002)
[58] Stefanova, I., Tcr ligand discrimination is enforced by competing erk positive and shp-1 negative feedback pathways, Nat. Immunol., 4, 248-254 (2003)
[59] Lalanne, J. B.; François, P., Principles of adaptive sorting revealed by in silico evolution, Phys. Rev. Lett., 110, 21, Article 218102 pp. (2013)
[60] Qi, S. Y.; Groves, J. T.; Chakraborty, A. K., Synaptic pattern formation during cellular recognition, Proc. Natl. Acad. Sci. U.S.A, 98, 6548-6553 (2001)
[61] Huse, M.; Lillemeier, B. F.; Kuhns, M. S.; Chen, D. S.; Davis, M. M., T cells use two directionally distinct pathways for cytokine secretion, Nat. Immunol., 7, 247-255 (2006)
[62] Choudhuri, K.; Wiseman, D.; Brown, M. H.; Gould, K.; van der Merwe, P. A., T-cell receptor triggering is critically dependent on the dimensions of its peptide-MHC ligand, Nature, 436, 578-582 (2005)
[63] Choudhuri, K.; van der Merwe, P. A., Molecular mechanisms involved in T cell receptor triggering, Semin. Immunol., 19, 255-261 (2007)
[64] Valitutti, S.; Dessing, M.; Aktories, K.; Gallati, H.; Lanzavecchia, A., Sustained signaling leading to T cell activation results from prolonged T cell receptor occupancy, role of T cell actin cytoskeleton, J. Exp. Med., 181, 577-584 (1995)
[65] Kalergis, A. M., Efficient T cell activation requires an optimal dwell-time of interaction between the TCR and the pMHC complex, Nat. Immunol., 2, 229-234 (2001)
[66] Holler, P. D., In vitro evolution of a t cell receptor with high affinity for peptide/MHC, Proc. Natl. Acad. Sci. U.S.A, 97, 5387-5392 (2000)
[67] Lever, M., Architecture of a minimal signaling pathway explains the T-cell response to a 1 million-fold variation in antigen affinity and dose, Proc. Natl. Acad. Sci. U.S.A, 113, E6630-E6638 (2016)
[68] Posey, A. D.J., Engineered car T cells targeting the cancer-associated tn-glycoform of the membrane mucin muc1 control adenocarcinoma, Immunity, 44, 1444-1454 (2016)
[69] Schmitt, T. M., Generation of higher affinity T cell receptors by antigen-driven differentiation of progenitor T cells in vitro, Nat. Biotechnol., 35, 1188-1195 (2017)
[70] Valitutti, S.; Muller, S.; Cella, M.; Padovan, E.; Lanzavecchia, A., Serial triggering of many T-cell receptors by a few peptide-MHC complexes, Nature, 375, 148-151 (1995)
[71] Zhu, C.; Jiang, N.; Huang, J.; Zarnitsyna, V. I.; Evavold, B. D., Insights from in situ analysis of TCR-pMHC recognition: response of an interaction network, Immunol. Rev., 251, 49-64 (2013)
[72] Liu, B.; Chen, W.; Evavold, B. D.; Zhu, C., Accumulation of dynamic catch bonds between TCR and agonist peptide-MHC triggers T cell signaling, Cell, 157, 357-368 (2014)
[73] Dembo, M.; Torney, D.; Saxman, K.; Hammer, D., The reaction-limited kinetics of membrane-to-surface adhesion and detachment, Proc. R. Soc. Lond. B, 234, 55-83 (1988)
[74] Sibener, L. V., Isolation of a structural mechanism for uncoupling T cell receptor signaling from peptide-MHC binding, Cell, 174, 672-687 (2018), e27
[75] Wu, P., Mechano-regulation of peptide-MHC class I conformations determines TCR antigen recognition, Mol. Cell, 73, 1015-1027 (2019), e7
[76] Cai, E., Visualizing dynamic microvillar search and stabilization during ligand detection by T cells, Science, 356, Article eaal3118 pp. (2017)
[77] Dustin, M. L.; Chakraborty, A. K.; Shaw, A. S., Understanding the structure and function of the immunological synapse, Cold Spring Harb. Perspect. Biol., 2, Article a002311 pp. (2010)
[78] Hearty, S.; Leonard, P.; Ma, H.; O’Kennedy, R., Measuring antibody-antigen binding kinetics using surface plasmon resonance, Methods Mol. Biol., 1827, 421-455 (2018)
[79] Tsourkas, P. K.; Liu, W.; Das, S. C.; Pierce, S. K.; Raychaudhuri, S., Discrimination of membrane antigen affinity by B cells requires dominance of kinetic proofreading over serial engagement, Cell Mol. Immunol., 9, 62-74 (2012)
[80] Blinov, M. L.; Faeder, J. R.; Goldstein, B.; W. S., Hlavacek, A network model of early events in epidermal growth factor receptor signaling that accounts for combinatorial complexity, Biosystems, 83, 136-151 (2006)
[81] Lemmon, M. A.; Schlessinger, J., Cell signaling by receptor tyrosine kinases, Cell, 141, 1117-1134 (2010)
[82] Lee, J.; Sengupta, P.; Brzostowski, J.; Lippincott-Schwartz, J.; Pierce, S. K., The nanoscale spatial organization of b-cell receptors on immunoglobulin M- and G-expressing human B-cells, Mol. Biol. Cell., 28, 511-523 (2017)
[83] Dintzis, H. M.; Dintzis, R. Z., A molecular basis for immune regulation: The immunon hypothesis, (Perelson, A. S., Theoretical Immunology, Part I (1988), Addison-Wesley: Addison-Wesley Redwood City, CA), 83-103
[84] Perelson, A. S., (Hoffmann, G. W.; Levy, J. G.; Nepom, G. T., Paradoxes in B Cell Stimulation By Polymeric Antigen and the Immunon Concept in Paradoxes in Immunology (1986), CRC Press: CRC Press Boca Raton, Florida, CA), 199-214
[85] Yang, J.; Reth, M., The dissociation activation model of B cell antigen receptor triggering, FEBS Lett., 584, 4872-4877 (2010)
[86] Fleire, S. J., B cell ligand discrimination through a spreading and contraction response, Science, 312, 738-741 (2006)
[87] François, P.; Hakim, V., Design of genetic networks with specified functions by evolution in silico, Proc. Natl. Acad. Sci. U.S.A, 101, 580-585 (2004)
[88] Proulx-Giraldeau, F.; Rademaker, T. J.; François, P., Untangling the hairball: Fitness-based asymptotic reduction of biological networks, Biophys. J., 113, 1893-1906 (2017)
[89] Lipniacki, T.; Hat, B.; Faeder, J. R.; Hlavacek, W. S., Stochastic effects and bistability in T cell receptor signaling, J. Theoret. Biol., 254, 110-122 (2008) · Zbl 1400.92173
[90] Nathan, C.; Sporn, M., Cytokines in context, J. Cell Biol., 113, 5, 981-986 (1991)
[91] Casciari, J. J.; Sato, H.; Durum, S. K.; Fiege, J.; Weinstein, J. N., Reference databases of cytokine structure and function, Cancer Chemother. Biol. Response Modif., 16, 315-346 (1996)
[92] Altan-Bonnet, G.; Mukherjee, H., Cytokine-mediated communication: a quantitative appraisal of immune complexity, Nat. Rev. Immunol., 19, 4, 205-217 (2019)
[93] Vogel, R. M.; Erez, A.; Altan-Bonnet, G., Dichotomy of cellular inhibition by small-molecule inhibitors revealed by single-cell analysis, Nature Commun., 7, 12428 (2016)
[94] Goldstein, B.; Jones, D.; Kevrekidis, I. G.; Perelson, A. S., Evidence for p55-p75 heterodimers in the absence of IL-2 from scatchard plot analysis, Int. Immunol., 4, 23-32 (1992)
[95] Feinerman, O., Single-cell quantification of IL-2 response by effector and regulatory T cells reveals critical plasticity in immune response, Mol. Syst. Biol., 6, 437 (2010)
[96] Cotari, J. W.; Voisinne, G.; Dar, O. E.; Karabacak, V.; Altan-Bonnet, G., Cell-to-cell variability analysis dissects the plasticity of signaling of common \(\gamma\) chain cytokines in T cells, Sci. Signaling, 6, ra17 (2013)
[97] Busse, D., Competing feedback loops shape IL-2 signaling between helper and regulatory T lymphocytes in cellular microenvironments, Proc. Natl. Acad. Sci. U.S.A, 107, 3058-3063 (2010)
[98] Hofer, T.; Krichevsky, O.; Altan-Bonnet, G., Competition for IL-2 between regulatory and effector T cells to chisel immune responses, Front. Immunol., 3, 268 (2012)
[99] Levine, S. J., Mechanisms of soluble cytokine receptor generation, J. Immunol., 173, 5343-5348 (2004)
[100] Rose-John, S.; Neurath, M. F., Il-6 trans-signaling: the heat is on, Immunity, 20, 2-4 (2004)
[101] Kirchner, G. I., Pharmacokinetics of recombinant human interleukin-2 in advanced renal cell carcinoma patients following subcutaneous application, Br. J. Clin. Pharmacol., 46, 5-10 (1998)
[102] Cendrowski, J.; Maminska, A.; Miaczynska, M., Endocytic regulation of cytokine receptor signaling, Cytokine Growth Factor Rev., 32, 63-73 (2016)
[103] Tkach, K. E., T cells translate individual quantal activation into collective, analog cytokine responses via time-integrated feedbacks, Elife, 3, Article e01944 pp. (2014)
[104] Voisinne, G., T cells integrate local and global cues to discriminate between structurally similar antigens, Cell Rep., 11, 1208-1219 (2015)
[105] Polonsky, M., Induction of CD4 T cell memory by local cellular collectivity, Science, 360, Article eaaj1853 pp. (2018)
[106] Becker, V., Covering a broad dynamic range: information processing at the erythropoietin receptor, Science, 328, 1404-1408 (2010)
[107] Raue, A.; Kreutz, C.; Maiwald, T.; Klingmuller, U.; Timmer, J., Addressing parameter identifiability by model-based experimentation, IET Syst. Biol., 5, 120-130 (2011)
[108] Bachmann, J., Division of labor by dual feedback regulators controls jak2/stat5 signaling over broad ligand range, Mol. Syst. Biol., 7, 516 (2011)
[109] Karr, J. R., Summary of the dream8 parameter estimation challenge: Toward parameter identification for whole-cell models, PLoS Comput. Biol., 11, Article e1004096 pp. (2015)
[110] Shi, T., Conservation of protein abundance patterns reveals the regulatory architecture of the egfr-mapk pathway, Sci. Signal, 9, rs6 (2016)
[111] Mitchell, S.; Roy, K.; Zangle, T. A.; Hoffmann, A., Nongenetic origins of cell-to-cell variability in b lymphocyte proliferation, Proc. Natl. Acad. Sci. U.S.A, 115, E2888-E2897 (2018)
[112] Thurley, K.; Gerecht, D.; Friedmann, E.; Hofer, T., Three-dimensional gradients of cytokine signaling between t cells, PLoS Comput. Biol., 11, Article e1004206 pp. (2015)
[113] Berezhkovskii, A. M.; Sample, C.; Shvartsman, S. Y., How long does it take to establish a morphogen gradient?, Biophys. J., 99, L59-L61 (2010)
[114] Kolomeisky, A., Formation of a morphogen gradient: Acceleration by degradation, J. Phys. Chem. Lett., 2, 1502-1505 (2011)
[115] Oyler-Yaniv, A., A tunable diffusion-consumption mechanism of cytokine propagation enables plasticity in cell-to-cell communication in the immune system, Immunity, 46, 609-620 (2017)
[116] Marcou, Q., A model for the integration of conflicting exogenous and endogenous signals by dendritic cells, Phys. Biol., 15, Article 056001 pp. (2018)
[117] Huang, J., A single peptide-major histocompatibility complex ligand triggers digital cytokine secretion in CD4(+) T cells, Immunity, 39, 846-857 (2013)
[118] Zhu, J.; Yamane, H.; Paul, W. E., Differentiation of effector CD4 T cell populations, Annu. Rev. Immunol., 28, 445-489 (2010)
[119] Fishman, M. A.; Perelson, A. S., Th1/Th2 cross-regulation, J. Theoret. Biol., 170, 25-56 (1994)
[120] Chaouat, G., Th1/Th2 paradigm in pregnancy: paradigm lost? cytokines in pregnancy/early abortion: reexamining the Th1/Th2 paradigm, Int. Arch. Allergy Immunol., 134, 93-119 (2004)
[121] Yates, A.; Callard, R.; Stark, J., Combining cytokine signalling with T-bet and GATA-3 regulation in Th1 and Th2 differentiation: a model for cellular decision-making, J. Theoret. Biol., 231, 181-196 (2004) · Zbl 1447.92130
[122] Antebi, Y. E., Mapping differentiation under mixed culture conditions reveals a tunable continuum of T cell fates, PLoS Biol., 11, Article e1001616 pp. (2013)
[123] Hong, T.; Xing, J.; Li, L.; Tyson, J. J., A mathematical model for the reciprocal differentiation of T helper 17 cells and induced regulatory T cells, PLoS Comput. Biol., 7, Article e1002122 pp. (2011)
[124] Laslo, P., Multilineage transcriptional priming and determination of alternate hematopoietic cell fates, Cell, 126, 755-766 (2006)
[125] Kueh, H. Y.; Champhekar, A.; Nutt, S. L.; Elowitz, M. B.; Rothenberg, E. V., Positive feedback between PU.1 and the cell cycle controls myeloid differentiation, Science, 341, 670-673 (2013)
[126] Walczak, A. M.; Sasai, M.; Wolynes, P. G., Self-consistent proteomic field theory of stochastic gene switches, Biophys. J., 88, 828-850 (2005)
[127] Walczak, A. M.; Onuchic, J. N.; Wolynes, P. G., Absolute rate theories of epigenetic stability, Proc. Natl. Acad. Sci. USA, 102, 18926-18931 (2005)
[128] Friedman, N.; Cai, L.; Xie, X. S., Linking stochastic dynamics to population distribution: an analytical framework of gene expression, Phys. Rev. Lett., 97, Article 168302 pp. (2006)
[129] Elowitz, M. B.; Levine, A. J.; Siggia, E. D.; Swain, P. S., Stochastic gene expression in a single cell, Science, 297, 1183-1186 (2002)
[130] Feinerman, O.; Veiga, J.; Dorfman, J. R.; Germain, R. N.; Altan-Bonnet, G., Variability and robustness in T cell activation from regulated heterogeneity in protein levels, Science, 321, 1081-1084 (2008)
[131] Szabo, S. J., A novel transcription factor, T-bet, directs Th1 lineage commitment, Cell, 100, 655-669 (2000)
[132] Palma, A.; Jarrah, A. S.; Tieri, P.; Cesareni, G.; Castiglione, F., Gene regulatory network modeling of macrophage differentiation corroborates the continuum hypothesis of polarization states, Front. Physiol., 9, 1659 (2018)
[133] Kueh, H. Y., Asynchronous combinatorial action of four regulatory factors activates Bcl11b for T cell commitment, Nat. Immunol., 17, 956-965 (2016)
[134] Peine, M., Stable T-bet \({}^+\) GATA-\(3{}^+\) Th1/Th2 hybrid cells arise in vivo, can develop directly from naive precursors, and limit immunopathologic inflammation, PLoS Biol., 11, Article e1001633 pp. (2013)
[135] Bianconi, E., An estimation of the number of cells in the human body, Ann. Hum. Biol., 40, 6, 463-471 (2013)
[136] Moran, U.; Phillips, R.; Milo, R., SnapShot: key numbers in biology, Cell, 141, 1262-1262.e1 (2010)
[137] Milo, R.; Phillips, R., Cell Biology By the Numbers (2015), Garland Science
[138] Abkowitz, J. L.; Catlin, S. N.; Mccallie, M. T.; Guttorp, P., Evidence that the number of hematopoietic stem cells per animal is conserved in mammals, Blood, 100, 2665-2667 (2002)
[139] Busch, K., Fundamental properties of unperturbed haematopoiesis from stem cells in vivo, Nature, 518, 542-546 (2015)
[140] Höfer, T.; Barile, M.; Flossdorf, M., Stem-cell dynamics and lineage topology from in vivo fate mapping in the hematopoietic system, Curr. Opin. Biotechnol., 39, 150-156 (2016)
[141] Zilman, A.; Ganusov, V. V.; Perelson, A. S., Stochastic models of lymphocyte proliferation and death, PLoS ONE, 5, 1-14 (2010)
[142] Perelson, A. S., Modelling viral and immune system dynamics, Nature Rev. Immunol., 2, 28-36 (2002)
[143] Yates, A., Reconstruction of cell population dynamics using CFSE, BMC Bioinformatics, 20, 1-20 (2007)
[144] Seita, J.; Weissman, I. L., Hematopoietic stem cell: self-renewal versus differentiation, WIREs Syst. Biol. Med., 2, 640-653 (2010)
[145] Schoedel, K. B., The bulk of the hematopoietic stem cell population is dispensable for murine steady-state and stress hematopoiesis, Blood, 128, 2285-2297 (2016)
[146] Perié, L., Determining lineage pathways from cellular barcoding experiments, Cell Rep., 6, 617-624 (2014)
[147] Ogawa, M.; Porter, P. N.; Nakahata, T., Renewal and commitment to differentiation of hemopoietic stem cells ( an interpretive review ), Blood, 5, 823-829 (1983)
[148] Tsuji, K.; Nakahata, T., Stochastic model for multipotent hemopoietic progenitor differentiation, J. Cell. Physiol., 653, 647-653 (1989)
[149] Pei, W., Polylox barcoding reveals haematopoietic stem cell fates realized in vivo, Nature Publ. Group, 548, 456-460 (2017)
[150] Murphy, K.; Travers, P.; Walport, M., Janeway’s Immunology (2007), Garland Science
[151] Laffleur, B., Immunoglobulin genes undergo legitimate repair in human B cells not only after cis - but also frequent trans -class switch recombination, Genes Immun., 15, 341-346 (2014)
[152] Murphy, K. M., Signaling and transcription in T helper development, Annu. Rev. Immunol., 18, 451-494 (2000)
[153] Hawkins, E. D.; Turner, M. L.; Dowling, M. R.; Gend, C. V.; Hodgkin, P. D., A model of immune regulation as a consequence of randomized lymphocyte division and death times, Proc. Natl. Acad. Sci. USA, 104, 5032-5037 (2007)
[154] Marchingo, J. M., Antigen affinity, costimulation, and cytokine inputs sum linearly to amplify T cell expansion, Science, 346, 1123-1128 (2014)
[155] Marchingo, J. M., T-cell stimuli independently sum to regulate an inherited clonal division fate, Nature Commun., 7, 1-12 (2016)
[156] Duffy, K. R.; Hodgkin, P. D., Intracellular competition for fates in the immune system, Trends Cell Biol., 22, 457-464 (2012)
[157] Deenick, E. K.; Gett, A. V.; Hodgkin, P. D., Stochastic model of T cell proliferation: A calculus revealing IL-2 regulation of precursor frequencies, cell cycle time, and survival, J. Immunol., 170, 4963-4972 (2003)
[158] Tangye, S. G.; Avery, D. T.; Deenick, E. K.; Hodgkin, P. D., Intrinsic differences in the proliferation of naive and memory human B cells as a mechanism for enhanced secondary immune responses, J. Immunol., 170, 686-694 (2003)
[159] Bendall, S. C.; Nolan, G. P.; Roederer, M.; Chattopadhyay, P. K., A deep profiler ’ s guide to cytometry, Trends Immunol., 33, 323-332 (2012)
[160] Buchholz, V. R., Disparate individual fates compose robust CD8+ T cell immunity, Science, 340, 630-636 (2013)
[161] Flossdorf, M.; Rössler, J.; Buchholz, V. R.; Busch, D. H.; Höfer, T., CD8 + T cell diversification by asymmetric cell division, Nature Immunol., 16, 891-893 (2015)
[162] Dowling, M. R., Stretched cell cycle model for proliferating lymphocytes, Proc. Natl. Acad. Sci. USA, 111, 6377-6382 (2014)
[163] Miles, A. S.; Hodgkin, P. D.; Duffy, K. R., Inferring differentiation order in adaptive immune responses from population level data (2019), arXiv:1908.03482 [q-bio.CB]
[164] Kinjyo, I., Real-time tracking of cell cycle progression during CD8+ effector and memory T-cell differentiation, Nature Commun., 6, 1-13 (2015)
[165] Jenkins, M. K.; Chu, H. H.; McLachlan, J. B.; Moon, J. J., On the composition of the preimmune repertoire of T cells specific for peptide-major histocompatibility complex ligands, Annu. Rev. Immunol., 28, 275-294 (2009)
[166] Glanville, J., Precise determination of the diversity of a combinatorial antibody library gives insight into the human immunoglobulin repertoire, Proc. Natl. Acad. Sci. USA, 106, 20216-20221 (2009)
[167] Han, A.; Glanville, J.; Hansmann, L.; Davis, M. M., Linking T-cell receptor sequence to functional phenotype at the single-cell level, Nature Biotechnol., 32, 684-692 (2014)
[168] Dupic, T.; Marcou, Q.; Walczak, A. M.; Mora, T., Genesis of the \(\alpha\beta\) T-cell receptor (2018), arXiv:1806.11030
[169] Arstila, T. P., A direct estimate of the human \(\alpha - \beta\) T cell receptor diversity, Science, 286, 958-961 (1999)
[170] Weinstein, J. A.; Jiang, N.; White, R. A.; Fisher, D. S.; Quake, S. R., High-throughput sequencing of the zebrafish antibody repertoire, Science, 324, 807-810 (2009)
[171] Robins, H. S., Comprehensive assessment of T-cell receptor beta-chain diversity in \(\alpha \beta\) T cells, Blood, 114, 4099-4107 (2009) · Zbl 1180.35239
[172] Boyd, S. D., Measurement and clinical monitoring of human lymphocyte clonality by massively parallel VDJ pyrosequencing, Sci. Transl. Med., 1, 12ra23 (2009)
[173] Benichou, J.; Ben-Hamo, R.; Louzoun, Y.; Efroni, S., Rep-Seq: Uncovering the immunological repertoire through next-generation sequencing, Immunology, 135, 183-191 (2012)
[174] Six, A., The past, present and future of immune repertoire biology - the rise of next-generation repertoire analysis, Front. Immunol., 4, 413 (2013)
[175] Robins, H., Immunosequencing: applications of immune repertoire deep sequencing, Curr. Opin. Immunol., 25, 646-652 (2013)
[176] Georgiou, G., The promise and challenge of high-throughput sequencing of the antibody repertoire, Nature Biotechnol., 32, 158-168 (2014)
[177] Heather, J. M.; Ismail, M.; Oakes, T.; Chain, B., High-throughput sequencing of the T-cell receptor repertoire: pitfalls and opportunities, Brief. Bioinform., 19, 554-565 (2017)
[178] Minervina, A.; Pogorelyy, M.; Mamedov, I., TCR and BCR repertoire profiling in adaptive immunity, Transpl. Int., 32, 1111-1123 (2019)
[179] Mora, T.; Walczak, A. M., How many different clonotypes do immune repertoires contain?, Current Opinion Syst. Biol., 18, 104-110 (2020)
[180] Rubelt, F., Adaptive immune receptor repertoire community recommendations for sharing immune-repertoire sequencing data, Nature Immunol., 18, 1274-1278 (2017)
[181] Fisher, R.; Steven Corbet, A.; Williams, C., The relation between the number of species and the number of individuals in a random sample of an animal population, J. Anim. Ecol., 12, 42-58 (2016)
[182] Qi, Q., Diversity and clonal selection in the human T-cell repertoire, Proc. Natl. Acad. Sci. USA, 111, 13139-13144 (2014)
[183] Chao, A.; Bunge, J., Estimating the number of species in a stochastic abundance model, Biometrics, 58, 531-539 (2002) · Zbl 1210.62225
[184] DeWitt, W. S., A public database of memory and naive B-cell receptor sequences, Plos One, 11, Article e0160853 pp. (2016)
[185] Lythe, G.; Callard, R. E.; Hoare, R.; Molina-París, C., How many TCR clonotypes does a body maintain?, J. Theoret. Biol., 389, 214-224 (2015) · Zbl 1343.92095
[186] Mora, T.; Walczak, A., Quantifying lymphocyte receptor diversity, (Das, J. D.; Jayaprakash, C., Systems Immunology (2018), CRC Press), 185-199
[187] Wardemann, H., Predominant autoantibody production by early human B cell precursors, Science, 301, 1374-1377 (2003)
[188] Lauemøller, S. L., Sensitive quantitative predictions of peptide-MHC binding by a ‘ Query by Committee ’ artificial neural network approach, Tissue Antigens, 62, 378-384 (2003)
[189] Jensen, K. K., Improved methods for predicting peptide binding affinity to MHC class II molecules, Immunology, 154, 394-406 (2018)
[190] Moon, J. J., Naive CD4+ T cell frequency varies for different epitopes and predicts repertoire diversity and response magnitude, Immunity, 27, 203-213 (2007)
[191] Jenkins, M. K.; Moon, J. J., The role of naive T cell precursor frequency and recruitment in dictating immune response magnitude, J. Immunol., 188, 4135-4140 (2012)
[192] Murugan, A.; Mora, T.; Walczak, A. M.; Callan, C. G., Statistical inference of the generation probability of T-cell receptors from sequence repertoires, Proc. Natl. Acad. Sci. USA, 109, 16161-16166 (2012)
[193] Elhanati, Y., Inferring processes underlying B-cell repertoire diversity, Philos. Trans. R. Soc. B, 370, Article 20140243 pp. (2015)
[194] Pogorelyy, M. V., Persisting fetal clonotypes influence the structure and overlap of adult human T cell receptor repertoires, PLoS Comput. Biol., 13, Article e1005572 pp. (2017)
[195] Toledano, A., Evidence for shaping of light chain repertoire by structural selection, Front. Immunol., 9, 1-9 (2018)
[196] Sethna, Z., Insights into immune system development and function from mouse T-cell repertoires, Proc. Natl. Acad. Sci. USA, 114, 2253-2258 (2017)
[197] Magadan, S., Origin of public memory B cell clones in fish after antiviral vaccination, Front. Immunol., 9, 2115 (2018)
[198] Marcou, Q.; Mora, T.; Walczak, A. M., High-throughput immune repertoire analysis with IGoR, Nature Commun., 9, 1, 561 (2018)
[199] Sethna, Z.; Elhanati, Y.; Callan, C. G.; Walczak, A. M.; Mora, T., OLGA: fast computation of generation probabilities of B- and T-cell receptor amino acid sequences and motifs, Bioinformatics, 35, 2974-2981 (2019)
[200] Munshaw, S.; Kepler, T. B., SoDA2: a Hidden Markov Model approach for identification of immunoglobulin rearrangements, Bioinformatics, 26, 867-872 (2010)
[201] Elhanati, Y.; Marcou, Q.; Mora, T.; Walczak, A. M., RepgenHMM: a dynamic programming tool to infer the rules of immune receptor generation from sequence data, Bioinformatics, 32, 1943-1951 (2015)
[202] Ralph, D. K.; Matsen, F. A., Consistency of VDJ rearrangement and substitution parameters enables accurate B cell receptor sequence annotation, PLoS Comput. Biol., 12, 1-25 (2016)
[203] Ralph, D. K.; Matsen, F. A., Likelihood-based inference of B cell clonal families, PLoS Comput. Biol., 12, 1-28 (2016)
[204] Wang, Y., Genomic screening by 454 pyrosequencing identifies a new human IGHV gene and sixteen other new IGHV allelic variants, Immunogenetics, 63, 259-265 (2011)
[205] Pogorelyy, M. V., Method for identification of condition-associated public antigen receptor sequences, eLife, 7, 1-13 (2018)
[206] Pogorelyy, M. V., Detecting T-cell receptors involved in immune responses from single repertoire snapshots, PLoS Biol., 17, Article e3000314 pp. (2018)
[207] Jiang, W., Normal values for CD4 and CD8 lymphocyte subsets in healthy Chinese adults from Shanghai, Clin. Diagn. Lab. Immunol., 11, 811-813 (2004)
[208] Wing, K.; Sakaguchi, S., Regulatory T cells exert checks and balances on self tolerance and autoimmunity, Nature Immunol., 11, 7-13 (2010)
[209] Bains, I.; van Santen, H. M.; Seddon, B.; Yates, A. J., Models of self-peptide sampling by developing T cells identify candidate mechanisms of thymic selection, PLoS Comput. Biol., 9, Article e1003102 pp. (2013)
[210] Le Borgne, M., The impact of negative selection on thymocyte migration in the medulla, Nature Immunol., 10, 823-830 (2009)
[211] Kosmrlj, A., Effects of thymic selection of the T-cell repertoire on HLA class I-associated control of HIV infection, Nature, 465, 350-354 (2010)
[212] Elhanati, Y.; Murugan, A.; Callan, C. G.; Mora, T.; Walczak, A. M., Quantifying selection in immune receptor repertoires, Proc. Natl. Acad. Sci. USA, 111, 9875-9880 (2014)
[213] Kaplinsky, J., Antibody repertoire deep sequencing reveals antigen-independent selection in maturing B cells, Proc. Natl. Acad. Sci. USA, 111, E2622-9 (2014)
[214] Elhanati, Y.; Sethna, Z.; Callan, C. G.; Mora, T.; Walczak, A. M., Predicting the spectrum of TCR repertoire sharing with a data-driven model of recombination, Immunol. Rev., 284, 167-179 (2018)
[215] Mora, T.; Walczak, A. M., Renyi entropy, abundance distribution and the equivalence of ensembles, Phys. Rev. E, 95, 052418 (2016)
[216] Mora, T.; Walczak, A. M.; Bialek, W.; Callan, C. G., Maximum entropy models for antibody diversity, Proc. Natl. Acad. Sci. USA, 107, 5405-5410 (2010)
[217] Desponds, J.; Mora, T.; Walczak, A. M., Fluctuating fitness shapes the clone-size distribution of immune repertoires, Proc. Natl. Acad. Sci. USA, 113, 274-279 (2016)
[218] Desponds, J.; Mayer, A.; Mora, T.; Walczak, A. M., Population dynamics of immune repertoires (2017), arxiv.org:1703.00226
[219] Greef, P. C.D., The naive T-cell receptor repertoire has an extremely broad distribution of clone sizes (2019), bioRxiv:691501
[220] Best, K.; Oakes, T.; Heather, J. M.; Shawe-Taylor, J.; Chain, B., Computational analysis of stochastic heterogeneity in PCR amplification efficiency revealed by single molecule barcoding, Sci. Rep., 5, 14629 (2015)
[221] Vollmers, C.; Sit, R. V.; Weinstein, J. A.; Dekker, C. L.; Quake, S. R., Genetic measurement of memory B-cell recall using antibody repertoire sequencing, Proc. Natl. Acad. Sci. USA, 110, 13463-13468 (2013)
[222] Shugay, M., Towards error-free profiling of immune repertoires, Nature Methods, 11, 653-655 (2014)
[223] Kaplinsky, J.; Arnaout, R., Robust estimates of overall immune-repertoire diversity from high-throughput measurements on samples, Nature Commun., 7, 11881 (2016)
[224] Laydon, D. J.; Bangham, C. R.M.; Asquith, B.; Crm, B., Estimating T-cell repertoire diversity: limitations of classical estimators and a new approach, Philos. Trans. R. Soc. B, 370, Article 20140291 pp. (2015)
[225] Haegeman, B., Robust estimation of microbial diversity in theory and in practice, ISME J., 7, 1092-1101 (2013)
[226] Emerson, R. O., Immunosequencing identifies signatures of cytomegalovirus exposure history and HLA-mediated effects on the T cell repertoire, Nature Genet., 49, 659-665 (2017)
[227] Mayer, A.; Balasubramanian, V.; Mora, T.; Walczak, A. M., How a well-adapted immune system is organized, Proc. Natl. Acad. Sci. USA, 112, 5950-5955 (2015)
[228] Emerson, R. O., Immunosequencing identifies signatures of cytomegalovirus exposure history and HLA-mediated effects on the T cell repertoire, Nature Genet., 49, 659-665 (2017)
[229] Faham, M., Discovery of T cell receptor \(\beta\) motifs specific to HLA-B27-positive ankylosing spondylitis by deep repertoire sequence analysis, Arthritis Rheumatol., 69, 774-784 (2017)
[230] Venturi, V., Sharing of T cell receptors in antigen-specific responses is driven by convergent recombination, Proc. Natl. Acad. Sci. USA, 103, 18691-18696 (2006)
[231] Venturi, V., A mechanism for TCR sharing between T cell subsets and individuals revealed by pyrosequencing, J. Immunol., 186, 4285-4294 (2011)
[232] Madi, A., T-cell receptor repertoires share a restricted set of public and abundant CDR3 sequences that are associated with self-related immunity, Genome Res., 24, 1603-1612 (2014)
[233] Perelson, A.; Weisbuch, G., Immunology for physicists, Rev. Modern Phys., 69, 1219-1268 (1997)
[234] Mayer, A.; Balasubramanian, V.; Mora, T.; Walczak, A. M., How a well-adapted immune system is organized, Proc. Natl. Acad. Sci. USA, 112, 5950-5955 (2015)
[235] Thomas, N., Tracking global changes induced in the CD4 T-cell receptor repertoire by immunization with a complex antigen using short stretches of CDR3 protein sequence, Bioinformatics, 30, 3181-3188 (2014)
[236] Cinelli, M., Feature selection using a one dimensional naïve Bayes’ classifier increases the accuracy of support vector machine classification of CDR3 repertoires, Bioinformatics, 33, btw771 (2017)
[237] Castro, R., Teleost fish mount complex clonal IgM and IgT responses in spleen upon systemic viral infection, PLoS Pathogens, 9, Article e1003098 pp. (2013)
[238] Laserson, U., High-resolution antibody dynamics of vaccine-induced immune responses, Proc. Natl. Acad. Sci. USA, 111, 4928-4933 (2014)
[239] Wang, C., B-cell repertoire responses to varicella-zoster vaccination in human identical twins, Proc. Natl. Acad. Sci. USA, 112, 500-505 (2015)
[240] Qi, Q., Diversification of the antigen-specific T cell receptor repertoire after varicella zoster vaccination, Sci. Transl. Med., 8, 332ra46 (2016)
[241] DeWitt, W. S., Dynamics of the cytotoxic T cell response to a model of acute viral infection, J. Virol., 89, 4517-4526 (2015)
[242] Pogorelyy, M. V., Precise tracking of vaccine-responding T-cell clones reveals convergent and personalized response in identical twins, Proc. Natl. Acad. Sci. USA, 115, 12704-12709 (2018)
[243] Dessalles, R.; D’Orsogna, M. R.; Chou, T., How heterogeneous thymic output and homeostatic proliferation shape naive T cell receptor clone abundance distributions (2019), arxiv.org:1906.07463
[244] Lythe, G.; Molina-Paris, C., Some deterministic and stochastic mathematical models of naive T-cell homeostasis, Immunol. Rev., 285, 206-217 (2018)
[245] Borghans, J. A.M.; De Boer, R. J., Quantification of T-cell dynamics: From telomeres to DNA labeling, Immunol. Rev., 216, 35-47 (2007)
[246] De Boer, R. J.; Perelson, A. S., Quantifying T lymphocyte turnover, J. Theoret. Biol., 327, 45-87 (2013) · Zbl 1322.92018
[247] Bains, I.; Antia, R.; Callard, R.; Yates, A. J., Quantifying the development of the peripheral naive CD4+ T-cell pool in humans, Blood, 113, 5480-5487 (2009)
[248] Thomas-Vaslin, V.; Altes, H. K.; de Boer, R. J.; Klatzmann, D., Comprehensive assessment and mathematical modeling of T cell population dynamics and homeostasis, J. Immunol., 180, 2240-2250 (2008)
[249] Bains, I.; Antia, R.; Callard, R.; Yates, A. J., Quantifying the development of the peripheral naive CD4+ T-cell pool in humans, Blood, 113, 5480-5487 (2009)
[250] A. Murugan, private communication, 2012.
[251] Zheng, G. X., Massively parallel digital transcriptional profiling of single cells, Nature Commun., 8, 1-12 (2017)
[252] Hubbell, S. P., The Unified Neutral Theory of Biodiversity and Biogeography (2001), Princeton University Press: Princeton University Press Princeton, NJ
[253] Jerne, N., Towards a network theory of the immune system, Ann. Immunol. (Paris), 125C, 373-389 (1974)
[254] Perelson, A. S., Immune network theory, Immunol. Rev., 110, 5-36 (1989)
[255] De Boer, R. J.; Perelson, A. S., T cell repertoires and competitive exclusion, J. Theoret. Biol., 169, 375-390 (1994)
[256] De Boer, R. J.; Perelson, A. S., Competitive control of the self-renewing T cell repertoire, Int. Immunol., 9, 779-790 (1997)
[257] De Boer, R. J.; Freitas, A. A.; Perelson, A. S., Resource competition determines selection of B cell repertoires, J. Theoret. Biol., 212, 333-343 (2001)
[258] Mayer, A.; Balasubramanian, V.; Walczak, A. M.; Mora, T., How a well-adapting immune system remembers, Proc. Natl. Acad. Sci. USA, 116, 8815-8823 (2019)
[259] Perelson, A. S.; Ribeiro, R. M., Modeling the within-host dynamics of HIV infection, BMC Biol., 11, 96 (2013)
[260] Perelson, A. S.; Neumann, A. U.; Markowitz, M.; Leonard, J. M.; Ho, D. D., HIV-1 dynamics in vivo: virion clearance rate, infected cell life-span, and viral generation time, Science, 271, 1582-1586 (1996)
[261] Perelson, A. S., Decay characteristics of HIV-1-infected compartments during combination therapy, Nature, 187, 188-191 (1997)
[262] Feder, A.; Harper, K.; Pennings, P. S., Challenging conventional wisdom on the evolution of resistance to multi-drug HIV treatment: Lessons from data and modeling (2019), bioR \(\chi\) iv:10.1101/807560v1
[263] Fletcher, C. V., Persistent HIV-1 replication is associated with lower antiretroviral drug concentrations in lymphatic tissues, Proc. Natl. Acad. Sci. USA, 111, 2307-2312 (2014)
[264] Mccoy, C. O., Quantifying evolutionary constraints on B-cell affinity maturation, Philos. Trans. R. Soc. B, 370, Article 20140244 pp. (2015)
[265] Yaari, G.; Kleinstein, S. H., Practical guidelines for B-cell receptor repertoire sequencing analysis, Genome Med., 7, 121 (2015)
[266] Hoehn, K. B.; Fowler, A.; Lunter, G.; Pybus, O. G., The diversity and molecular evolution of B-cell receptors during infection, Mol. Biol. Evol., 33, 1147-1157 (2016)
[267] Gupta, N. T., Change-O: A toolkit for analyzing large-scale B cell immunoglobulin repertoire sequencing data, Bioinformatics, 31, 3356-3358 (2015)
[268] Vander Heiden, J. A., PRESTO: A toolkit for processing high-throughput sequencing raw reads of lymphocyte receptor repertoires, Bioinformatics, 30, 1930-1932 (2014)
[269] Yaari, G.; Benichou, J. I.C.; Heiden, J. A.V.; Kleinstein, S. H.; Louzoun, Y., The mutation patterns in B-cell immunoglobulin receptors reflect the influence of selection acting at multiple time-scales, Philos. Trans. R. Soc. B, 370, Article 20140242 pp. (2015)
[270] Cui, A., A model of somatic hypermutation targeting in mice based on high-throughput Ig sequencing data, J. Immunol. Methods, 197, 3566-3574 (2016)
[271] Yaari, G., Models of somatic hypermutation targeting and substitution based on synonymous mutations from high-throughput immunoglobulin sequencing data, Front. Immunol., 4, 1-11 (2013)
[272] Dhar, A.; Davidsen, K.; Matsen, F. A.; Minin, V. N., Predicting B cell receptor substitution profiles using public repertoire data, PLoS Genet., 14, Article e1006388 pp. (2018)
[273] DeWitt, W. S.; Mesin, L.; Victora, G. D.; Minin, V. N.; Matsen, F. A., Using genotype abundance to improve phylogenetic inference, Mol. Biol. Evol., 35, 1253-1265 (2018)
[274] Jacob, J.; Kassir, R.; Kelsoe, G., In situ studies of the primary immune response to (4-hydroxy-3-nitrophenyl) acetyl. I. The architecture and dynamics of responding cell populations, J. Exp. Med., 173, 1165-1175 (1991)
[275] Nieuwenhuis, P.; Opstelten, D., Functional anatomy of germinal centers, Amer. J. Anat., 435, 421-435 (1984)
[276] Shapiro, G. S.; Aviszus, K.; Ikle, D.; Wysocki, L. J., Predicting regional mutability in antibody v genes based solely on di-and trinucleotide sequence composition, J. Immunol., 163, 259-268 (1999)
[277] Uduman, M., Detecting selection in immunoglobulin sequences, Nucleic Acids Res., 39, W499-W504 (2011)
[278] Yaari, G., Models of somatic hypermutation targeting and substitution based on synonymous mutations from high-throughput immunoglobulin sequencing data, Front. Immunol., 4, 358 (2013)
[279] Feng, J.; Shaw, D. A.; Minin, V. N.; Simon, N.; Matsen IV, F. A., Survival analysis of DNA mutation motifs with penalized proportional hazards (2017), arxiv:1711.04057 · Zbl 1423.62142
[280] Unniraman, S.; Schatz, D. G., Strand-biased spreading of mutations during somatic hypermutation, Science, 317, 1227-1230 (2007)
[281] Kepler, T. B.; Perelson, A. S., Somatic hypermutation in B cells: An optimal control treatment, J. Theoret. Biol., 164, 37-64 (1993)
[282] Kepler, T. B.; Perelson, A. S., Cyclic re-entry of germinal center B cells and the efficiency of affinity maturation, Immunol. Today, 14, 412-415 (1993)
[283] Oprea, M.; Perelson, A. S., Somatic mutation leads to efficient affinity maturation when centrocytes recycle back to centroblasts, J. Immunol., 158, 5155-5162 (1997)
[284] Oprea, M.; van Nimwegen, E.; Perelson, A. S., Dynamics of one-pass germinal center models : implications for affinity maturation, Bull. Math. Biol., 62, 121-153 (2000) · Zbl 1323.92059
[285] Victora, G. D.; Nussenzweig, M. C., Germinal centers, Annu. Rev. Immunol., 30, 429-457 (2012)
[286] Berek, C.; Milstein, C., Mutation drift and repertoire shift in the maturation of the immune response, Immunol. Rev., 96, 23-41 (1987)
[287] Eisen, H. N.; Siskind, G. W., Variations in affinities of antibodies during the immune response, Biochemistry, 3, 996-1008 (1964)
[288] Tas, J. M.J., Visualizing antibody affinity maturation in germinal centers, Science, 3439, 1-11 (2016)
[289] Kuraoka, M., Complex antigens drive permissive clonal selection in germinal centers, Immunity, 44, 542-552 (2016)
[290] Victora, G. D., Germinal center dynamics revealed by multiphoton microscopy with a photoactivatable fluorescent reporter, Cell, 143, 592-605 (2010)
[291] Lee, J., Molecular-level analysis of the serum antibody repertoire in young adults before and after seasonal influenza vaccination, Nat. Med., 22, 1456-1464 (2016)
[292] Wang, S.; Burton, D.; Kardar, M.; Chakraborty, A., Guiding the evolution to catch the virus: An in silico study of affinity maturation against rapidly mutating antigen, Bull. Amer. Phys. Soc., 59, 1 (2014)
[293] Murugan, R., Clonal selection drives protective memory B cell responses in controlled human malaria infection, Sci. Immunol., 3, eaap8029 (2018)
[294] Neu, K. E., Spec-seq unveils transcriptional subpopulations of antibody-secreting cells following influenza vaccination, J. Clin. Investig., 129, 93-105 (2019)
[295] Dunn-Walters, D. K.; Hare, J. S.O., (Fulop, T.; etal., Handbook of Immunosenescence (2018), Springer International Publishing AG)
[296] Wendel, B. S., Accurate immune repertoire sequencing reveals malaria infection driven antibody lineage diversification in young children, Nature Commun., 8, 531 (2017)
[297] Nourmohammad, A.; Otwinowski, J.; Luksza, M.; Mora, T.; Walczak, A. M., Clonal competition in B-cell repertoires during chronic HIV-1 infection, Mol. Biol. Evol., 36, 2184-2194 (2018)
[298] Horns, F.; Vollmers, C.; Dekker, C. L.; Quake, S. R., Signatures of selection in the human antibody repertoire: Selective sweeps, competing subclones, and neutral drift, Proc. Natl. Acad. Sci. USA, 116, 1261-1266 (2019)
[299] Vieira, M. C.; Zinder, D.; Cobey, S., Selection and neutral mutations drive pervasive mutability losses in long-lived anti-HIV B-cell lineages, Mol. Biol. Evol., 35, 1135-1146 (2018)
[300] Nourmohammad, A.; Otwinowski, J.; Plotkin, J. B., Host-pathogen coevolution and the emergence of broadly neutralizing antibodies in chronic infections, PLoS Genet., 12, Article e1006171 pp. (2016)
[301] Blanquart, F.; Gandon, S., Time-shift experiments and patterns of adaptation across time and space, Ecol. Lett., 16, 31-38 (2013)
[302] Richman, D. D.; Wrin, T.; Little, S. J.; Petropoulos, C. J., Rapid evolution of the neutralizing antibody response to HIV type 1 infection, Proc. Natl. Acad. Sci. USA, 100, 4144-4149 (2003)
[303] Frost, S. D.W., Neutralizing antibody responses drive the evolution of human immunodeficiency virus type 1 envelope during recent HIV infection, Proc. Natl. Acad. Sci. USA, 102, 18514-18519 (2005)
[304] Moore, P. L., Limited neutralizing antibody specificities drive neutralization escape in early HIV-1 subtype C infection, PLoS Pathogens, 5, Article e1000598 pp. (2009)
[305] Luksza, M.; Lässig, M., A predictive fitness model for influenza, Nature, 507, 57-61 (2014)
[306] Ferguson, A. L., Translating HIV sequences into quantitative fitness landscapes predicts viral vulnerabilities for rational immunogen design, Immunity, 38, 606-617 (2013)
[307] Shekhar, K., Spin models inferred from patient-derived viral sequence data faithfully describe HIV fitness landscapes, Phys. Rev. E, 88, 62705 (2013)
[308] Barton, J. P., Relative rate and location of intra-host HIV evolution to evade cellular immunity are predictable, Nature Commun., 7, 1-10 (2016)
[309] Eigen, M., Selforganization of matter and the evolution of biological macromolecules, Naturwissenschaften, 58, 65-523 (1971)
[310] Leuthäusser, I.; Leuthausser, I., An exact correspondence between Eigen’s evolution model and a two- dimensional Ising system, J. Chem. Phys., 1884, 1985-1987 (1998)
[311] Phillips, R.; Kondev, J.; Theriot, J.; Garcia, H., Physical Biology of the Cell (2012), Garland Science: Garland Science NY, NY
[312] Zanini, F.; Brodin, J.; Thebo, L.; Lanz, C.; Albert, J., Population genomics of intrapatient HIV-1 evolution, eLife, 4, Article e11282 pp. (2015)
[313] Smith, D. J.; Lapedes, A. S.; Jong, J. C.D., Mapping the antigenic and genetic evolution of influenza virus, Science, 305, 371-377 (2004)
[314] Neher, R. A.; Russell, C. A.; Shraiman, B. I., Predicting evolution from the shape of genealogical trees, eLife, 3, 1-18 (2014)
[315] Bao, Y., The influenza virus resource at the national center for biotechnology information, J. Virol., 82, 596-601 (2008)
[316] Grenfell, B. T., Measles: Nonlinearity and Stochasticity in an Epidemic Metapopulation (2008), Oxford University Press
[317] Rouzine, I. M.; Rozhnova, G., Antigenic evolution of viruses in host populations, PLoS Pathogens, 14, Article e1007291 pp. (2018)
[318] Yan, L.; Neher, R.; Shraiman, B. I., Phylodynamics of rapidly adapting pathogens: extinction and speciation of a red queen, eLife, 8, Article e44205 pp. (2018)
[319] Desai, M. M.; Fisher, D. S., Beneficial mutation selection balance and the effect of linkage on positive selection, Genetics, 176, 1759-1798 (2007)
[320] Bedford, T.; Rambaut, A.; Pascual, M., Canalization of the evolutionary trajectory of the human influenza virus, BMC Biol., 10, 38 (2012)
[321] Marchi, J.; Lässig, M.; Mora, T.; Walczak, A. M., Multi-lineage evolution in viral populations driven by host immune systems (2019), arXiv:1906.07444
[322] Regev, A., The human cell atlas, eLife, 6, Article e27041 pp. (2017)
[323] Mehta, P., A high-bias, low-variance introduction to machine learning for physicists, Phys. Rep., 810, 1-124 (2018)
[324] Walczak, A.; Mugler, A.; Wiggins, C., Computational Modeling of Signaling Networks, 273-322 (2012)
[325] Tkačik, G.; Walczak, A. M., Information transmission in genetic regulatory networks: a review, J. Phys. Condens. Matter. Inst. Phys. J., 23, Article 153102 pp. (2011)
[326] (Kawamoto, H.; Miyake, S.; Miyasaka, M.; Ohteki, T.; Sorimachi, N.; Takahama, Y.; Taki, S., Your Amazing Immune System (2009), The Japanese Society for Immunology, Wiley-Blackwell: The Japanese Society for Immunology, Wiley-Blackwell Hoboken, NJ)
[327] Hartl, D. L., Genetics: Analysis of Genes and Genomes (2011), ed Learning J & B
[328] Gillespie, John H., Population Genetics : A Concise Guide (2004), Johns Hopkins University Press
[329] Takahata, N.; Ishii, K.; Matsuda, H., Effect of temporal fluctuation of selection coefficient on gene frequency in a population, Proc. Natl. Acad. Sci. USA, 72, 4541-4545 (1975) · Zbl 0321.92011
[330] Nourmohammad, A.; Schiffels, S.; Lässig, M., Evolution of molecular phenotypes under stabilizing selection, J. Stat. Mech. Theory Exp., 2013, P01012 (2013) · Zbl 1456.92100
[331] Allen, L. J.S., An Introduction to Stochastic Processes with Biology Applications (2003), Prentice-Hall: Prentice-Hall New York · Zbl 1205.60001
[332] Zapperi, S.; Ba, K.; Stanley, H. E., Self-organized branching processes: Mean-field theory for Avalanche, Phys. Rev. Lett., 75, 4071-4074 (1995)
[333] Beggs, J. M.; Plenz, D., Neuronal avalanches in neocortical circuits, J. Neurosci., 23, 11167-11177 (2003)
[334] Neher, R. A., Genetic draft, selective interference, and population genetics of rapid adaptation, Annu. Rev. Ecol. Evol. Syst., 44, 195-215 (2013)
[335] Mustonen, V.; Lässig, M., Fitness flux and ubiquity of adaptive evolution, Proc. Natl. Acad. Sci. USA, 107, 4248-4253 (2010)
[336] Everitt, B., Cluster Analysis (2011), Wiley: Wiley Chichester, West Sussex, U.K · Zbl 1274.62003
[337] Yang, Z., Computational Molecular Evolution (2006), Oxford University Press
[338] Stamatakis, A., RAxML version 8 : a tool for phylogenetic analysis and post-analysis of large phylogenies, Bioinformatics, 30, 1312-1313 (2014)
[339] Hoehn, K. B.; Lunter, G.; Pybus, O. G., A phylogenetic codon substitution model for antibody lineages, Genetics, 206, 417-427 (2017)
[340] Davidsen, K.; Matsen IV, F. A., Benchmarking tree and ancestral sequence inference for B cell receptor sequences, Front. Immunol., 9, 1-13 (2018)
[341] Kussell, E.; Leibler, S., Phenotypic diversity, population growth, and information in fluctuating environments, Science, 309, 2075-2078 (2005)
[342] Rivoire, O.; Leibler, S., The value of information for populations in varying environments, J. Stat. Phys., 142, 1124-1166 (2011) · Zbl 1216.92052
[343] Bradde, S.; Mora, T.; Walczak, A. M., Cost and benefits of CRISPR spacer acquisition, Philos. Trans. R. Soc. B, 374, Article 20180095 pp. (2019)
[344] Lotka, A. J., Analytical note on certain rhythmic relations in organic systems, Proc. Natl. Acad. Sci. USA, 6, 410-415 (1920)
[345] Volterra, V., (dei Lincei, A. N., Opere Mathematiche. Opere Mathematiche, Memorie e Note (1956), Accademia Nazionale dei Lincei: Accademia Nazionale dei Lincei Rome), xxxiii + 604
[346] Edelstein-Keshet, L., Mathematical Models in Biology (2005), SIAM: SIAM Philadelphia · Zbl 1100.92001
[347] May, R. M., Stability and Complexity in Model Ecosystems (2001), Princeton University Press · Zbl 1044.92047
[348] Marchenko, V.; Pastur, L., Distribution of eigenvalues for some sets of random matrices, Mat. Sb., 72, 507-536 (1967) · Zbl 0152.16101
[349] Yule, U. G., A mathematical theory of evolution, based on the conclusions of dr j.c. willis, f.r.s, Phil. Trans. R. Soc. B, 213, 21-87 (1925)
[350] Simon, H. A., On a class of skew distribution functions, Biometrika Trust, 42, 425-440 (1955) · Zbl 0066.11201
[351] Kepler, T. B., Reconstructing a B-cell clonal lineage. II. Mutation, selection, and affinity maturation, Front. Immunol., 5, 170 (2014)
[352] Elhanati, Y.; Marcou, Q.; Mora, T.; Walczak, A. M., RepgenHMM: A dynamic programming tool to infer the rules of immune receptor generation from sequence data, Bioinformatics, 32, 1943-1951 (2016)
[353] Pressé, S., Principles of maximum entropy and maximum caliber in statistical physics, Rev. Modern Phys., 85, 1115-1141 (2013)
[354] Barton, J. P.; De Leonardis, E.; Coucke, A.; Cocco, S., ACE: Adaptive cluster expansion for maximum entropy graphical model inference, Bioinformatics, 32, 3089-3097 (2016)
[355] Nguyen, H. C.; Zecchina, R.; Berg, J., Inverse statistical problems: from the inverse ising problem to data science, Adv. Phys., 66, 197-261 (2017)
[356] Dekosky, B. J., In-depth determination and analysis of the human paired heavy- and light-chain antibody repertoire, Nat. Med., 21, 1-8 (2014)
[357] Klein, A. M., Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells resource droplet barcoding for single-cell transcriptomics applied to embryonic stem cells, Cell, 161, 1187-1201 (2015)
[358] Mcdaniel, J. R.; DeKosky, B. J.; Tanno, H.; Ellington, A. D.; Georgiou, G., Ultra-high-throughput sequencing of the immune receptor repertoire from millions of lymphocytes, Nat. Protoc., 11, 429-442 (2016)
[359] Grigaityte, K., Single-cell sequencing reveals \(\alpha \beta\) chain pairing shapes the T cell repertoire (2017), bioRxiv:213462
[360] Howie, B., High-throughput pairing of T cell receptor \(\alpha\) and \(\beta\) sequences, Sci. Transl. Med., 7, Article 301ra131 pp. (2015)
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.