title: Analyzing the dynamics of cell cycle processes from fixed samples through ergodic principles.
abstract: Tools to analyze cyclical cellular processes, particularly the cell cycle, are of broad value for cell biology. Cell cycle synchronization and live-cell time-lapse observation are widely used to analyze these processes but are not available for many systems. Simple mathematical methods built on the ergodic principle are a well-established, widely applicable, and powerful alternative analysis approach, although they are less widely used. These methods extract data about the dynamics of a cyclical process from a single time-point "snapshot" of a population of cells progressing through the cycle asynchronously. Here, I demonstrate application of these simple mathematical methods to analysis of basic cyclical processes--cycles including a division event, cell populations undergoing unicellular aging, and cell cycles with multiple fission (schizogony)--as well as recent advances that allow detailed mapping of the cell cycle from continuously changing properties of the cell such as size and DNA content. This includes examples using existing data from mammalian, yeast, and unicellular eukaryotic parasite cell biology. Through the ongoing advances in high-throughput cell analysis by light microscopy, electron microscopy, and flow cytometry, these mathematical methods are becoming ever more important and are a powerful complementary method to traditional synchronization and time-lapse cell cycle analysis methods.
authors.affiliation: Sir William Dunn School of Pathology, University of Oxford, Oxford OX1 3RE, United Kingdom, and Max Planck Institute of Molecular Cell Biology and Genetics, 01307 Dresden, Germany richard.wheeler@path.ox.ac.uk.
authors.forename: Richard John
authors.initials: RJ
authors.lastname: Wheeler
mesh_terms: D003585:Cell Biology; D002453:Cell Cycle / Q000502:physiology*; D002455:Cell Division / Q000502:physiology; D003584:Cytological Techniques / Q000379:methods*; D006801:Humans; D008954:Models, Biological; D013223:Statistics as Topic / Q000379:methods