Explicit kinetic heterogeneity: mathematical models for interpretation of deuterium labeling of heterogeneous cell populations

• Published in: PLoS computational biology 2010-02, Vol.6 (2), p.e1000666-e1000666
• Main Authors: Ganusov, Vitaly V, Borghans, José A M, De Boer, Rob J
• Format: Article
• Language: English
• Subjects: Affinity labeling; Algorithms; Apoptosis; Cell differentiation; Cell division; Cell Growth Processes - physiology; Cloning; Computational Biology; Computational Biology - methods; Computer Simulation; Confidence intervals; Deoxyribonucleic acid; Deuterium - metabolism; DNA; Estimates; Experiments; Genetic aspects; Glucose; Humans; Immunology/Immune Response; Isotope Labeling - methods; Kinetics; Labeling; Life sciences; Lymphocytes; Lymphocytes - cytology; Lymphocytes - metabolism; Mathematical models; Mathematics/Statistics; Methods; Models, Biological; Population; Reproducibility of Results; Rodents; Studies
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1000666

Estimation of division and death rates of lymphocytes in different conditions is vital for quantitative understanding of the immune system. Deuterium, in the form of deuterated glucose or heavy water, can be used to measure rates of proliferation and death of lymphocytes in vivo. Inferring these rates from labeling and delabeling curves has been subject to considerable debate with different groups suggesting different mathematical models for that purpose. We show that the three most common models, which are based on quite different biological assumptions, actually predict mathematically identical labeling curves with one parameter for the exponential up and down slope, and one parameter defining the maximum labeling level. By extending these previous models, we here propose a novel approach for the analysis of data from deuterium labeling experiments. We construct a model of "kinetic heterogeneity" in which the total cell population consists of many sub-populations with different rates of cell turnover. In this model, for a given distribution of the rates of turnover, the predicted fraction of labeled DNA accumulated and lost can be calculated. Our model reproduces several previously made experimental observations, such as a negative correlation between the length of the labeling period and the rate at which labeled DNA is lost after label cessation. We demonstrate the reliability of the new explicit kinetic heterogeneity model by applying it to artificially generated datasets, and illustrate its usefulness by fitting experimental data. In contrast to previous models, the explicit kinetic heterogeneity model 1) provides a novel way of interpreting labeling data; 2) allows for a non-exponential loss of labeled cells during delabeling, and 3) can be used to describe data with variable labeling length.