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Mathematical Models of Dividing Cell Populations: Applicationto CFSE Data

Published online by Cambridge University Press:  17 October 2012

H.T. Banks  and
W. Clayton Thompson
H.T. Banks*
Affiliation:
Center for Research in Scientific Computation Center for Quantitative Sciences in Biomedicine, N.C. State University Raleigh, NC
W. Clayton Thompson
Affiliation:
Center for Research in Scientific Computation Center for Quantitative Sciences in Biomedicine, N.C. State University Raleigh, NC ICREA Infection Biology Lab Department of Experimental and Health Sciences Universitat Pompeu Fabra, Barcelona
*
⋆ Corresponding author. E-mail: [email protected]
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Abstract

Flow cytometric analysis using intracellular dyes such as CFSE is a powerful experimentaltool which can be used in conjunction with mathematical modeling to quantify the dynamicbehavior of a population of lymphocytes. In this survey we begin by providing an overviewof the mathematically relevant aspects of the data collection procedure. We then presentan overview of the large body of mathematical models, along with their assumptions anduses, which have been proposed to describe the dynamics of proliferating cell populations.While much of this body of work has been aimed at modeling the generation structure (cellsper generation) of the proliferating population, several recent models have considered themore fundamental task of modeling CFSE histogram data directly. Such models are analyzedand recent results are discussed. Finally, directions for future research aresuggested.

Keywords

cell proliferationcell division numberCFSEordinary differential equationscytonslabel structured population dynamicspartial differential equationsinverse problems
Type
Research Article
Information
Mathematical Modelling of Natural Phenomena , Volume 7 , Issue 5: Immunology , 2012 , pp. 24 - 52
Copyright
© EDP Sciences, 2012

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References

Références

Arino, O., Sanchez, E., Webb, G.F.. Necessary and sufficient conditions for asynchronous exponential growth in age structured cell populations with quiescence. Mathematical Analysis and Applications, 215 (1997), 499–513. CrossRefGoogle Scholar
Asquith, B., Debacq, C., Florins, A., Gillet, N., Sanchez-Alcaraz, T., Mosley, A., Willems, L.. Quantifying lymphocyte kinetics in vivo using carboxyfluorein diacetate succinimidyl ester. Proc. R. Soc. B, 273 (2006), 1165–1171. CrossRefGoogle ScholarPubMed
H.T. Banks. A Functional Analysis Framework for Modeling, Estimation and Control in Science and Engineering. CRC Press/Taylor-Francis, Boca Raton London New York, 2012.
Banks, H.T., Bokil, V. A., Hu, S., Allnutt, F.C.T., Bullis, R., Dhar, A.K., Browdy, C.L., Shrimp biomass and viral infection for production of biological countermeasures, CRSC-TR05-45. North Carolina State University, December 2005 ; Mathematical Biosciences and Engineering, 3 (2006), 635–660. Google Scholar
Banks, H.T., Bortz, D.M., Holte, S.E., Incorporation of variability into the mathematical modeling of viral delays in HIV infection dynamics, Math. Biosciences. 183 (2003), 63–91. CrossRefGoogle ScholarPubMed
H.T. Banks, D. M. Bortz, G.A. Pinter, L.K. Potter. Modeling and imaging techniques with potential for application in bioterrorism. CRSC-TR03-02, North Carolina State University, January 2003 ; Chapter 6 in Bioterrorism : Mathematical Modeling Applications in Homeland Security, (H.T. Banks and C. Castillo-Chavez, eds.), Frontiers in Applied Math, FR28, SIAM, Philadelphia, PA, 2003, 129–154.
H.T. Banks, L.W. Botsford, F. Kappel, C. Wang, Modeling and estimation in size structured population models. LCDS/CSS Report 87-13, Brown University, March 1987 ; Proc. 2nd Course on Math. Ecology (Trieste, December 8-12, 1986), World Scientific Press, Singapore, 1988, 521–541.
Banks, H.T., Charles, F., Doumic, M., Sutton, K. L., C. Thompson, W.. Label structured cell proliferation models. CRSC-TR10-10, North Carolina State University, June 2010 ; Appl. Math. Letters, 23 (2010), 1412–1415. CrossRefGoogle Scholar
Banks, H.T., Davis, J.L., Ernstberger, S.L., Hu, S., Artimovich, E., Dhar, A.K., Browdy, C.L.. A comparison of probabilistic and stochastic differential equations in modeling growth uncertainty and variability. CRSC-TR08-03, North Carolina State University, February 2008 ; Journal of Biological Dynamics, 3 (2009), 130–148. Google Scholar
H.T. Banks, J.L. Davis, S. Hu, A computational comparison of alternatives to including uncertainty in structured population models. CRSC-TR09-14, North Carolina State University June 2009 ; in Three Decades of Progress in Systems and Control, X. Hu, U. Jonsson, B. Wahlberg, B. Ghosh (Eds.), Springer, 2010, 19–33.
Banks, H.T., Fitzpatrick, B.F.. Estimation of growth rate distributions in size-structured population models. CAMS Tech. Rep. 90-2, Univ. of Southern California, January 1990 ; Quart. Appl. Math., 49 (1991), 215–235. CrossRefGoogle Scholar
H.T. Banks, B.G. Fitzpatrick, L.K. Potter, Y. Zhang, Estimation of probability distributions for individual parameters using aggregate population observations. CRSC-TR98-06, North Carolina State University, January 1998 ; Stochastic Analysis, Control, Optimization and Applications (W.McEneaney, G. Yin, and Q. Zhang, eds.), Birkhauser, 1998, 353–371.
Banks, H.T., Gibson, N.L., Well-posedness in Maxwell systems with distributions of polarization relaxation parameters. CRSC-TR04-01, North Carolina State University, January 2004 ; Applied Math. Letters, 18 (2005), 423–430. CrossRefGoogle Scholar
Banks, H.T., Gibson, N.L., Electromagnetic inverse problems involving distributions of dielectric mechanisms and parameters. CRSC-TR05-29, North Carolina State University, August2005 ; Quarterly of Applied Mathematics, 64 (2006), 749–795. Google Scholar
Banks, H.T., Holm, K., Kappel, F., Comparison of optimal design methods in inverse problems. CRSC-TR10-11, North Carolina State University, May 2011 ; Inverse Problems, 27 (2011), 075002. Google ScholarPubMed
Banks, H.T., Hu, S.. Nonlinear stochastic Markov processes and modeling uncertainty in populations. CRSC-TR11-02, North Carolina State University, January 2011 ; Mathematical Bioscience and Engineering, 9 (2012), 1–25. Google Scholar
H.T. Banks, S. Hu. Uncertainty propagation in physiologically structured population models. CRSC-TR12-08, North Carolina State University, Raleigh, NC, March 2012 ; Journal on Mathematical Modelling of Natural Phenomena, submitted.
H.T. Banks, K. Kunisch. Estimation Techniques for Distributed Parameter Systems, Birkhauser, Boston, 1989.
Banks, H.T. and Pinter, G.A.. A probabilistic multiscale approach to hysteresis in shear wave propagation in biotissue. CRSC-TR04-03, North Carolina State University, January 2004 ; IAM J. Multiscale Modeling and Simulation, 3 (2005), 395–412. Google Scholar
Banks, H.T., Potter, L.K.. Probabilistic methods for addressing uncertainty and variability in biological models : Application to a toxicokinetic model. CRSC-TR02-27, North Carolina State University, September 2002 ; Math. Biosci., 192 (2004), 193–225. Google Scholar
H.T. Banks, Karyn L. Sutton, W. Clayton Thompson, G. Bocharov, Marie Doumic, Tim Schenkel, Jordi Argilaguet, Sandra Giest, Cristina Peligero, Andreas Meyerhans. A New Model for the Estimation of Cell Proliferation Dynamics Using CFSE Data. CRSC-TR11-05, North Carolina State University, Revised July 2011 ; J. Immunological Methods, 373 (2011), 143–160 ; DOI :10.1016/j.jim.2011.08.014.
Banks, H.T., Sutton, Karyn L., Clayton Thompson, W., Bocharov, G., Roose, D., Schenkel, T., Meyerhans, A.. Estimation of cell proliferation dynamics using CFSE data. CRSC-TR09-17, North Carolina State University, August 2009 ; Bull. Math. Biol., 70 (2011), 116–150. Google Scholar
H.T. Banks, W. Clayton Thompson, Cristina Peligero, Sandra Giest, Jordi Argilaguet, Andreas Meyerhans. A Division-Dependent Compartmental Model for Computing Cell Numbers in CFSE-based Lymphocyte Proliferation Assays. CRSC-TR12-03, North Carolina State University, January 2012 ; Math Biosci. Eng., to appear.
Banks, H.T., Clayton Thompson, W.. A division-dependent compartmental model with cyton and intracellular label dynamics. CRSC-TR12-12, North Carolina State University, May 2012 ; Intl. J. Pure and Appl. Math 77 (2012), 119–147. Google Scholar
Banks, H.T., Tran, H.T., Woodward, D.E.. Estimation of variable coefficients in the Fokker-Planck equations using moving node finite elements. SIAM J. Numer. Anal., 30 (1993), 1574–1602. Google Scholar
Basse, B., Baguley, B., Marshall, E., Wake, G., Wall, D.. Modelling the flow cytometric data obtained from unperturbed human tumour cell lines : Parameter fitting and comparison. Bull. Math. Biol., 67 (2005), 815–830. CrossRefGoogle ScholarPubMed
Bekkal Brikci, F., Clairambault, J., Ribba, B., Perthame, B.. An age-and-cyclin-structured cell population model for healthy and tumoral tissues. Math. Biol., 57 (2008), 91–110. CrossRefGoogle ScholarPubMed
Bell, G., Anderson, E.. Cell Growth and Division I. A Mathematical Model with Applications to Cell Volume Distributions in Mammalian Suspension Cultures, Biophysical Journal, 7 (1967), 329–351. CrossRefGoogle ScholarPubMed
Bernard, S., Pujo-Menjouet, L., Mackey, M.C.. Analysis of cell kinetics using a cell division marker : Mathematical modeling of experimental data. Biophysical Journal, 84 (2003), 3414–3424. Google ScholarPubMed
Bonhoeffer, S., Mohri, H., Ho, D., Perelson, A.S.. Quantification of cell turnover kinetics using 5-Bromo-2’-deoxyuridine. Immunology, 64 (2000), 5049–5054. CrossRefGoogle Scholar
Borghans, Jose A. M., de Boer, R.J.. Quantification of T-cell dynamics : from telomeres to DNA labeling. Immunological Reviews, 216 (2007), 35–47. CrossRefGoogle ScholarPubMed
K.P. Burnham, D.R. Anderson. Model Selection and Multimodel Inference : A Practical Information-Theoretic Approach (2nd Edition), Springer, New York, 2002.
Callard, R., Hodgkin, P.D.. Modeling T- and B-cell growth and differentiation. Immunological Reviews, 216 (2007), 119–129. CrossRefGoogle Scholar
“Cyton Calculator”, Walter and Eliza Ball Institute of Medical Research. Available Online. Accessed 16 March 2012. http://www.wehi.edu.au/faculty_members/research_projects/cyton_calculator
DeBoer, R.J., Ganusov, V.V., Milutinovic, D., Hodgkin, P.D., Perelson, A.S.. Estimating lymphocyte division and death rates from CFSE data. Bull. Math. Biol., 68 (2006), 1011–1031. Google Scholar
DeBoer, R.J., Perelson, A. S.. Estimating division and death rates from CFSE data. Comp. and Appl. Mathematics, 184 (2005), 140–164. CrossRefGoogle Scholar
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. Immunology, 170 (2003), 4963–4972. CrossRefGoogle Scholar
Duffy, K., Subramanian, V.. On the impact of correlation between collaterally consanguineous cells on lymphocyte population dynamics. Math. Biol., 59 (2009), 255–285. CrossRefGoogle Scholar
Farkas, J.Z.. Stability conditions for the non-linear McKendrick equations. Appl. Math. and Comp., 156 (2004), 771–777. CrossRefGoogle Scholar
Farkas, J.Z.. Stability conditions for a non-linear size-structured model Nonlinear Analysis : Real World Applications, 6 (2005), 962–969. CrossRefGoogle Scholar
Ganusov, V. V., Milutinovi, D., De Boer, R. J.. IL-2 regulates expansion of CD4+ T cell populations by affecting cell death : insights from modeling CFSE data. Immunology, 179 (2007), 950–957. CrossRefGoogle ScholarPubMed
Ganusov, V.V., Pilyugin, S.S., De Boer, R.J., Murali-Krishna, K., Ahmed, R., Antia, R.. Quantifying cell turnover using CFSE data. Immunological Methods, 298 (2005), 183–200. Google Scholar
Gett, A.V., Hodgkin, P.D.. A cellular calculus for signal integration by T cells. Nature Immunology, 1 (2000), 239–244. CrossRefGoogle Scholar
Gyllenberg, M., Webb, G.F.. Age-size structure in populations with quiescence. Mathematical Biosciences, 86 (1987), 67–95. CrossRefGoogle Scholar
Gyllenberg, M., Webb, G.F.. A nonlinear structured population model of tumor growth with quiescence. J. Math. Biol., 28 (1990), 671–694. CrossRefGoogle ScholarPubMed
J. Hasenauer, D. Schittler, F. Allgöwer. A computational model for proliferation dynamics of division- and label-structured populations. arXive.org, arXiv :1202.4923v1,22Feb,2012.
Hawkins, E.D., Hommel, Mirja, Turner, M.L., Battye, F., Markham, J., Hodgkin, P.D.. Measuring lymphocyte proliferation, survival and differentiation using CFSE time-series data. Nature Protocols, 2 (2007), 2057–2067. CrossRefGoogle ScholarPubMed
Hawkins, E.D., Turner, M.L., Dowling, M.R., van Gend, C., Hodgkin, P.D.. A model of immune regulation as a consequence of randomized lymphocyte division and death times. Proc. Natl. Acad. Sci., 104 (2007), 5032–5037. CrossRefGoogle ScholarPubMed
Hawkins, E.D., Markham, J.F., McGuinness, L.P., Hodgkin, P.D.. A single-cell pedigree analysis of alternative stochastic lymphocyte fates. Proc. Natl. Acad. Sci., 106 (2009), 13457–13462. Google ScholarPubMed
Hyrien, O., Zand, M.S.. A mixture model with dependent observations for the analysis of CFSE-labeling experiments. American Statistical Association, 103 (2008), 222–239. CrossRefGoogle Scholar
O. Hyrien, R. Chen, M.S. Zand. An age-dependent branching process model for the analysis of CFSE-labeling experiments. Biology Direct, 5 (2010), Published Online.
Lee, H.Y., Hawkins, E.D., Zand, M.S., Mosmann, T., Wu, H., Hodgkin, P.D., Perelson, A.S.. Interpreting CFSE obtained division histories of B cells in vitro with Smith-Martin and Cyton type models. Bull. Math. Biol., 71 (2009), 1649–1670. CrossRefGoogle ScholarPubMed
Lee, H.Y., Perelson, A.S.. Modeling T cell proliferation and death in vitro based on labeling data : generalizations of the Smith-Martin cell cycle model. Bull. Math. Biol., 70 (2008), 21–44. CrossRefGoogle ScholarPubMed
Leon, K., Faro, J., Carneiro, J.. A general mathematical framework to model generation structure in a population of asynchronously dividing cells. Theoretical Biology, 229 (2004), 455–476. CrossRefGoogle Scholar
Luzyanina, T., Roose, D., Bocharov, G.. Distributed parameter identification for a label-structured cell population dynamics model using CFSE histogram time-series data. Math. Biol., 59 (2009), 581–603. CrossRefGoogle ScholarPubMed
T. Luzyanina, D. Roose, T. Schenkel, M. Sester, S. Ehl, A. Meyerhans, G. Bocharov. Numerical modelling of label-structured cell population growth using CFSE distribution data. Theoretical Biology and Medical Modelling, 4 (2007), Published Online.
Lyons, A.B.. Divided we stand : tracking cell proliferation with carboxyfluorescein diacetate succinimidyl ester. Immunology and Cell Biology, 77 (1999), 509–515. Google ScholarPubMed
Lyons, A.B., Hasbold, J., Hodgkin, P.D.. Flow cytometric analysis of cell division history using diluation of carboxyfluorescein diacetate succinimidyl ester, a stably integrated fluorescent probe. Methods in Cell Biology, 63 (2001), 375–398. CrossRefGoogle Scholar
Lyons, A.B., Doherty, K.V.. Flow cytometric analysis of cell division by dye dilution. Current Protocols in Cytometry, (2004), 9.11.1-9.11.10. Google ScholarPubMed
Lyons, A.B., Parish, C.R.. Determination of lymphocyte division by flow cytometry. Immunol. Methods, 171 (1994), 131–137. CrossRefGoogle Scholar
Matera, G., Lupi, M., Ubezio, P.. Heterogeneous cell response to topotecan in a CFSE-based proliferative test. Cytometry A, 62 (2004), 118–128. CrossRefGoogle Scholar
J.A. Metz, O. Diekmann. The Dynamics of Physiologically Structured Populations. Springer Lecture Notes in Biomathematics 68, Heidelberg, 1986.
Miao, H., Jin, X., Perelson, A., Wu, H.. Evaluation of multitype mathemathematical modelsfor CFSE-labeling experimental data. Bull. Math. Biol., 74 (2012), 300–326 ; DOI 10.1007/s11538-011-9668-y. CrossRefGoogle Scholar
K. Murphy, aneway’s Immunobiology, 8th[entity !#x20 !]Edition. Garland Science, London New York, 2012.
Nordon, R.E., Ko, Kap-Hyoun, Odell, R., Schroeder, T.. Multi-type branching models to describe cell differentiation programs. Theoretical Biology, 277 (2011), 7–18. Google ScholarPubMed
Nordon, R.E., Nakamura, M., Ramirez, C., Odell, R.. Analysis of growth kinetics by division tracking. Immunology and Cell Biology, 77 (1999), 523–529. CrossRefGoogle ScholarPubMed
Parish, C.. Fluorescent dyes for lymphocyte migration and proliferation studies. Immunology and Cell Biol., 77 (1999), 499–508. CrossRefGoogle ScholarPubMed
B. Perthame. Transport Equations in Biology. Birkhauser Frontiers in Mathematics, Basel, 2007.
Pilyugin, S. S., Ganusov, V. V., Murali-Krishnac, K., Ahmed, R., Antia, R.. The rescaling method for quantifying the turnover of cell populations. Theoretical Biology, 225 (2003), 275–283. CrossRefGoogle ScholarPubMed
B.J.C. Quah, C.R. Parish. New and improved methods for measuring lymphocyte proliferation in vitro and in vivo using CFSE-like fluorescent dyes. Immunological Methods, (2012), to appear.
Quah, B., Warren, H., Parish, C.. Monitoring lymphocyte proliferation in vitro and in vivo with the intracellular fluorescent dye carboxyfluorescein diacetate succinimidyl ester. Nature Protocols, 2 (2007), 2049–2056. CrossRefGoogle Scholar
Revy, P., Sospedra, M., Barbour, B., Trautmann, A.. Functional antigen-independent synapses formed between T cells and dendritic cells. Nature Immunology, 2 (2001), 925–931. CrossRefGoogle Scholar
Roederer, M.. Interpretation of cellular proliferation data : Avoid the panglossian, Cytometry A, 79 (2011), 95–101. Google Scholar
D. Schittler, J. Hasenauer, F. Allgöwer. A generalized model for cell proliferation : Integrating division numbers and label dynamics. Proc. Eighth International Workshop on Computational Systems Biology (WCSB 2011), June 2001, Zurich, Switzerland, p. 165–168.
Sinko, J., Streifer, W.. A New Model for Age-Size Structure of a Population. Ecology, 48 (1967), 910–918. CrossRefGoogle Scholar
Smith, J.A., Martin, L.. Do Cells Cycle ? Proc. Natl. Acad. Sci., 70 (1973), 1263–1267. CrossRefGoogle ScholarPubMed
Subramanian, V.G., Duffy, K.R., Turner, M.L., Hodgkin, P.D.. Determining the expected variability of immune responses using the cyton model. Math. Biol., 56 (2008), 861–892. CrossRefGoogle ScholarPubMed
Veiga-Fernandez, H., Walter, U., Bourgeois, C., McLean, A., Rocha, B.. Response of naive and memory CD8+ T cells to antigen stimulation in vivo, Nature Immunology. 1 (2000), 47–53. Google Scholar
W. C. Thompson. Partial Differential Equation Modeling of Flow Cytometry Data from CFSE-based Proliferation Assays. Ph.D. Dissertation, Dept. of Mathematics, North Carolina State University, Raleigh, December, 2011.
Wallace, P.K., Tario, J.D. Jr., Fisher, J.L., Wallace, S.S., Ernstoff, M.S., Muirhead, K.A.. Tracking antigen-driven responses by flow cytometry : monitoring proliferation by dye dilution. Cytometry A, 73 (2008), 1019–1034. CrossRefGoogle ScholarPubMed
Warren, H. S.. Using carboxyfluorescein diacetate succinimidyl ester to monitor human NK cell division : Analysis of the effect of activating and inhibitory class I MHC receptors. Immunology and Cell Biology, 77 (1999), 544–551. CrossRefGoogle ScholarPubMed
Wellard, C., Markham, J., Hawkins, E.D., Hodgkin, P.D.. The effect of correlations on the population dynamics of lymphocytes. Theoretical Biology, 264 (2010), 443–449. CrossRefGoogle ScholarPubMed
Witkowski, J.M.. Advanced application of CFSE for cellular tracking. Current Protocols in Cytometry, 44 (2008), 9.25.1–9.25.8. Google Scholar
A. Yates, C. Chan, J. Strid, S. Moon, R. Callard, A.J.T. George, J. Stark. Reconstruction of cell population dynamics using CFSE. BMC Bioinformatics, 8 (2007), Published Online.