Abstract
Computational models of biological processes are important building blocks in Systems Biology studies. Calibration and validation are two important steps for moving a mathematical model to a computational model. While calibration refers to finding numerical value of the coefficients such as rate constants in a mathematical model, validation refers to verifying that the calibrated model behaves the same as the biological system under previously unseen conditions such as environmental changes (e.g., drug treatment) or mutations. In lieu of direct measurements of rate constants, modeling of the molecular mechanisms that govern biological behaviors may be able to use dynamic expression profiles of reactant biomolecules for calibration. For validation, similar data, obtained under new conditions, are probably better than direct measurements of rate constants. In any case, direct measurement of rate constants is almost always impractical and difficult or impossible. Here, we show a computer-assisted methodology to extract embedded dynamic profiles of cell-cycle proteins from statically sampled, multivariate cytometry data guided by heuristics assembled from canonical cell-cycle knowledge. The methodology is implemented using standard “list mode” cytometry data-processing software followed by CytoSys – a software tool with an easy-to-use graphical interface. We demonstrate the use of CytoSys with a case study of exponentially growing, human erythroleukemia cells and extract the dynamic expression profiles of cyclin A for calibrating an existing deterministic mathematical model of the cell cycle.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Mesarovic, M.D., Sreenath, S.N. and Keene, J.D. (2004) Search for organising principles: understanding in systems biology. IEE Syst. Biol. (Stevenage) 1, 19–27.
Chen, W.W., Schoeberl, B., Jasper, P.J., et al (2009) Input–output behavior of ErbB signaling pathways as revealed by a mass action model trained against dynamic data. Mol. Syst. Biol. 5, 1–19.
Janes, K.A., Albeck, J.G., Gaudet, S., Sorger, P.K., Lauffenburger, D.A. and Yaffe, M.B. (2005) A systems model of signaling identifies a molecular basis set for cytokine-induced apoptosis. Science 310, 1646–1653.
Voit, E.O. and Ferreira, A.E. (2000) Computational Analysis of Biochemical Systems: A Practical Guide for Biochemists and Molecular Biologists, first edition. Cambridge University Press, Cambridge.
Burnette, W.N. (1981) “Western blotting”: electrophoretic transfer of proteins from sodium dodecyl sulfate – polyacrylamide gels to unmodified nitrocellulose and radiographic detection with antibody and radioiodinated protein A. Anal. Biochem. 112, 195–203.
Leng, S., McElhaney, J., Walston, J., Xie, D., Fedarko, N. and Kuchel, G. (2008) Elisa and multiplex technologies for cytokine measurement in inflammation and aging research.J. Gerontol. A Biol. Sci. Med. Sci. 63, 879–884.
Hawley, T.S. and Hawley, R.G. (2004) Methods in Molecular Biology: Flow Cytometry Protocols, second edition. Humana Press, Totowa, vol. 263, pp. 1–424.
Albieri, R., Barberis, M., Chiaradonna, F., Gaglio, D., Milanesi, L., Vanoni, M., Klipp, E. and Alberghina, L. (2009) Towards a systems biology approach to mammalian cell cycle: modeling the entrance into S phase of quiescent fibroblasts after serum stimulation. BMC Bioinformatics 10, S16.
Pomerening, J.R., Kim, S.Y. and Ferrell Jr., J.E. (2005) Systems-level dissection of the cell-cycle oscillator: bypassing positive feedback produces damped oscillations. Cell 122, 565–578.
Gonze, D. and Goldbeter, A. (2001) A model for a network of phosphorylation-dephosphorylation cycles displaying the dynamics of dominoes and clocks. J. Theor. Biol. 210, 167–186.
Tyson, J.J. and Novak, B. (2001) Regulation of the eukaryotic cell cycle: molecular antagonism, hysteresis and irreversible transitions. J.Theor. Biol. 210, 249–263.
Obeyesekere, M.N., Tecarro, E. and Lozano, G. (2004) Model predictions of MDM2 mediated cell regulation. Cell Cycle 3, 655–661.
Bai, S., Goodrich, D., Thron, C.D., Tecarro, E. and Obeyesekere, M. (2003) Theoretical and experimental evidence for hysteresis in cell proliferation. Cell Cycle 2, 46–52.
Qu, Z., Weiss, J.N. and MacLellan, W.R. (2003) Regulation of the mammalian cell: a model of the G1-to-S transition. Amer. J. Physiol. Cell Physiol. 284, C349–C367.
Novak, B. and Tyson, J.J. (2004) A model for restriction point control of the mammalian cell cycle. J. Theor. Biol. 230, 563–579.
Swat, M., Kel, A. and Herzel, H. (2004) Bifurcation analysis of the regulation modules of the G(1)/S transition. Bioinformatics 20, 1506–1511.
Haberichter, T., Madge, B., Christopher, R.A., Yoshioka, N., Dhiman, A., Miller, R., Gendelman, R., Aksenov, S.V., Khalil, I.G. and Dowdy, S.F. (2007) A systems biology dynamical model of the mammalian G1 cell cycle progression. Mol. Syst. Biol. 3, 84.
Csikasz-Nagy, A., Battogtokh, D., Chen, K.C., Novak, B. and Tyson, J.J. (2006) Analysis of a generic model of eukaryotic cell-cycle regulation. Biophys. J. 90, 4361–4379.
Chen, K.C., Csikasz-Nagy, A., Gyorffy, B., Val, J., Novak, B. and Tyson, J.J. (2000) Kinetic analysis of a molecular model of the budding yeast cell cycle. Mol. Biol. Cell 11, 369–391.
Sveiczer, A., Csikasz-Nagy, A., Gyorffy, B., Tyson, J.J. and Novak, B. (2000) Modeling the fission yeast cell cycle: quantized cycle times in Wee1/Cdc25delta mutant cells. Proc. Natl. Acad. Sci. U.S.A. 97, 7865–7870.
Novak, B., Pataki, Z., Ciliberto, A. and Tyson, J.J. (2001) Mathematical model of the cell division cycle of fission yeast. Chaos 11, 277–286.
Ciliberto, A., Novak, B. and Tyson, J.J. (2003) Mathematical model of the morphogenesis checkpoint in budding yeast. J. Cell Biol. 163, 1243–1254.
Chen, K.C., Calzone, L., Csikasz-Nagy, A., Cross, F.R., Novak, B. and Tyson, J.J. (2004) Integrative analysis of cell cycle control in budding yeast. Mol. Biol. Cell 15, 3841–3862.
Sveiczer, A., Tyson, J.J. and Novak, B. (2004) Modelling the fission yeast cell cycle. Brief. Funct. Genomic Proteomic 2, 298–307.
Qu, Z., MacLellan, W.R. and Weiss,J.N. (2003) Dynamics of the cell cycle:checkpoints, sizers, and timers. Biophys. J. 85, 3600–3611.
Qu, Z., Weiss, J.N. and MacLellan, W.R. (2004) Coordination of cell growth and cell division: a mathematical modeling study. J. Cell Sci. 117, 4199–4207.
Steuer, R. (2004). Effects of stochasticity in model of the cell cycle: from quantized cell cycle times to noise-induced oscillations. J. Theor. Biol. 228, 293–301.
Srividhya, J. and Gopinathan, M.S. (2006) A simple time delay model for eukaryotic cell cycle. J. Theor. Biol. 241, 617–627.
Yang, L., Han, Z., MacLellan, W.R., Weiss, J.N. and Qu, Z. (2006) Linking cell division to cell growth in a spatiotemporal model of the cell cycle. J. Theor. Biol. 241, 120–133.
Faure, A., Naldi, A., Chaouiya, C. and Thieffry, D. (2006) Dynamical analysis of a generic boolean model of the control of the mammalian cell cycle. Bioinformatics 22, e124–e131.
Melamed, M.R., Lindmo, T. and Mendelsohn, M. L. (1990) Flow Cytometry and Sorting, second edition. Wiley-Liss, New York, pp. 1–824.
Shapiro, H.M. (2003) Practical Flow Cytometry, fourth edition. Wiley-Liss, New York, pp. 1–681 (online at http://www.coulterflow.com).
Watson, J.V. (1991) Introduction to Flow Cytometry. Cambridge Press, Cambridge,pp. 1–443.
Gray, J.W. and Darzynkiewicz, Z. (1987) Techniques in Cell Cycle Analysis. Humana Press, Totowa, pp. 1–407.
Darzynkiewicz, Z., Robinson, J.P. and Crissman, H.A. (2001) Methods in Cell Biology: Cytometry, third edition. Academic Press, New York, vols. 63 & 64.
Jacobberger, J.W. (1991) Intracellular antigen staining: quantitative immunofluorescence. Methods 2, 207–218.
Jacobberger, J.W. (2000) Flow cytometric analysis of intracellular protein epitopes. In Immunophenotyping, Edited by Stewart, C., Nicholson, K. Wiley-Liss, New York, pp. 361–405.
Jacobberger, J.W. (2001) Stoichiometry of Immunocytochemical Staining Reactions. Methods in Cell Biology: Cytometry, third edition. Academic Press, New York, vol. 63, pp. 271–298.
Jacobberger, J.W., Sramkoski, R.M. and T. Stefan (2011) Multiparameter cell cycle analysis. In Methods in Molecular Biology: Flow Cytometry Protocols, third edition, Edited by Hawley, T.S. and Hawley, R.G., Humana Press, Totowa., vol. 699, pp. 229–249.
Jacobberger, J.W., Sramkoski, R.M., Wormsley, S.B., and Bolton, W.E. (1999) Estimation of kinetic cell cycle-related gene expression in G1 and G2 phases from immunofluorescence flow cytometry data. Cytometry 35, 284–289.
Frisa, P.S. and Jacobberger, J.W. (2009) Cell cycle-related cyclin B1 quantification. PLoS One 4 (9), e7064.
Jacobberger, J.W., Frisa, P.S., Sramkoski, R.M., Stefan, T., Shults, K.E. and Soni, D.V. (2008) A new biomarker for mitotic cells. Cytometry A 73, 5–15.
Goldbeter, A. (1991) A minimal cascade model for the mitotic oscillator involving cyclin and Cdc2 kinase. Proc. Natl. Acad. Sci. U.S.A. 88, 9107–9111.
Lozzio, B.B. and Lozzio, C.B. (1979) Properties and usefulness of the original K-562 human myelogenous leukemia cell line. Leuk. Res. 3, 363–370.
Frisa, P.S. and Jacobberger, J.W. (2010) Cytometry of chromatin bound Mcm6 and PCNA identifies two states in G1 that are separated functionally by the G1 restriction point. BMC Cell Biol. 11, 26.
Flow Cytometry Principles: Biology, University of California, Berkeley (Last accessed February 27, 2010, at http://biology.berkeley.edu/crl/flow_cytometry_basic.html).
Sigal, A., Milo, R., Cohen, A., Geva-Zatorsky, N., Klein, Y., Liron, Y., et al (2006) Variability and memory of protein levels in human cell. Nature 444, 643–646.
Ezyfit Toolbox – A Free Curve-Fitting Toolbox for Matlab (Last accessed March 16, 2010, at http://www.mathworks.com/matlabcentral/linkexchange/links/1234-ezyfit-toolbox-a-free-curve-fitting-toolbox-for-matlab).
Lozzio, B.B., Lozzio, C.B., Bamberger, E.G. and Feliu, A.S. (1981) A multipotential leukemia cell line (K-562) of human orgin. Proc. Soc. Exper. Biol. Med. 166, 546–550.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer Science+Business Media, LLC
About this protocol
Cite this protocol
Avva, J., Weis, M.C., Soebiyanto, R.P., Jacobberger, J.W., Sreenath, S.N. (2011). CytoSys: A Tool for Extracting Cell-Cycle-Related Expression Dynamics from Static Data. In: Kalyuzhny, A. (eds) Signal Transduction Immunohistochemistry. Methods in Molecular Biology, vol 717. Humana Press. https://doi.org/10.1007/978-1-61779-024-9_10
Download citation
DOI: https://doi.org/10.1007/978-1-61779-024-9_10
Published:
Publisher Name: Humana Press
Print ISBN: 978-1-61779-023-2
Online ISBN: 978-1-61779-024-9
eBook Packages: Springer Protocols