scholarly journals Characterizing stochastic cell cycle dynamics in exponential growth

2021 ◽  
Author(s):  
Dean Huang ◽  
Teresa Lo ◽  
Houra Merrikh ◽  
Paul A. Wiggins
Keyword(s):  
Cell Cycle ◽  
Stochastic Model ◽  
Cell Biology ◽  
Deterministic Model ◽  
Time Lapse ◽  
Distribution Functions ◽  
Cell Cycles ◽  
Experimental Approaches ◽  
Deterministic Description ◽  
Cell Cycle Dynamics

Two powerful and complementary experimental approaches are commonly used to study the cell cycle and cell biology: One class of experiments characterizes the statistics (or demographics) of an unsynchronized exponentially-growing population, while the other captures cell cycle dynamics, either by time-lapse imaging of full cell cycles or in bulk experiments on synchronized populations. In this paper, we study the subtle relationship between observations in these two distinct experimental approaches. We begin with an existing model: a single-cell deterministic description of cell cycle dynamics where cell states (i.e. periods or phases) have precise lifetimes. We then generalize this description to a stochastic model in which the states have stochastic lifetimes, as described by arbitrary probability distribution functions. Our analyses of the demographics of an exponential culture reveal a simple and exact correspondence between the deterministic and stochastic models: The corresponding state lifetimes in the deterministic model are equal to the exponential mean of the lifetimes in the stochastic model. An important implication is therefore that the demographics of an exponential culture will be well-fit by a deterministic model even if the state timing is stochastic. Although we explore the implications of the models in the context of the Escherichia coli cell cycle, we expect both the models as well as the significance of the exponential-mean lifetimes to find many applications in the quantitative analysis of cell cycle dynamics in other biological systems.

scholarly journals Analyzing the dynamics of cell cycle processes from fixed samples through ergodic principles

2015 ◽  
Vol 26 (22) ◽  
pp. 3898-3903 ◽  
Author(s):  
Richard John Wheeler
Keyword(s):  
Cell Cycle ◽  
Cell Biology ◽  
Time Lapse ◽  
Mathematical Methods ◽  
Alternative Analysis ◽  
Cell Cycles ◽  
Complementary Method ◽  
Cellular Processes ◽  
Cell Cycle Synchronization ◽  
Multiple Fission

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.

scholarly journals Single-embryo phosphoproteomics reveals the importance of intrinsic disorder in cell cycle dynamics

2021 ◽  
Author(s):  
Juan Manuel Valverde ◽  
Geronimo Dubra ◽  
Henk van den Toorn ◽  
Guido van Mierlo ◽  
Michiel Vermeulen ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Intrinsically Disordered Proteins ◽  
Protein Complexes ◽  
Disordered Proteins ◽  
Cell Cycles ◽  
Intrinsically Disordered ◽  
Intrinsically Disordered Regions ◽  
Egg Extracts ◽  
Cell Cycle Dynamics

Switch-like cyclin-dependent kinase (CDK)-1 activation is thought to underlie the abruptness of mitotic onset, but how CDKs can simultaneously phosphorylate many diverse substrates is unknown, and direct evidence for such phosphorylation dynamics in vivo is lacking. Here, we analysed protein phosphorylation states in single Xenopus embryos throughout synchronous cell cycles. Over a thousand phosphosites were dynamic in vivo, and assignment of cell cycle phases using egg extracts revealed hundreds of S-phase phosphorylations. Targeted phosphoproteomics in single embryos showed switch-like mitotic phosphorylation of diverse protein complexes. The majority of cell cycle-regulated phosphosites occurred in CDK consensus motifs, and 72% located to intrinsically disordered regions. Dynamically phosphorylated proteins, and documented substrates of cell cycle kinases, are significantly more disordered than phosphoproteins in general. Furthermore, 30-50% are components of membraneless organelles. Our results suggest that phosphorylation of intrinsically disordered proteins by cell cycle kinases, particularly CDKs, allows switch-like mitotic cellular reorganisation.

The structure of the human cell cycle

2021 ◽  
Author(s):  
Wayne Stallaert ◽  
Katarzyna M. Kedziora ◽  
Colin D. Taylor ◽  
Tarek M. Zikry ◽  
Holly K. Sobon ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Single Cell ◽  
Human Cell ◽  
Time Lapse ◽  
Underlying Structure ◽  
Cell Cycle Exit ◽  
Multiple Points ◽  
Cell Cycles ◽  
Single Cell Imaging ◽  
Proliferative Cell

ABSTRACTThe human cell cycle is conventionally depicted as a five-phase model consisting of four proliferative phases (G1, S, G2, M) and a single state of arrest (G0). However, recent studies show that individual cells can take different paths through the cell cycle and exit into distinct arrest states, thus necessitating an update to the canonical model. We combined time lapse microscopy, highly multiplexed single cell imaging and manifold learning to determine the underlying “structure” of the human cell cycle under multiple growth and arrest conditions. By visualizing the cell cycle as a complete biological process, we identified multiple points of divergence from the proliferative cell cycle into distinct states of arrest, revealing multiple mechanisms of cell cycle exit and re-entry and the molecular routes to senescence, endoreduplication and polyploidy. These findings enable the visualization and comparison of alternative cell cycles in development and disease.One-sentence summaryA systems-level view of single-cell states reveals the underlying architecture of the human cell cycle

scholarly journals A Stochastic Model Correctly Predicts Changes in Budding Yeast Cell Cycle Dynamics upon Periodic Expression of CLN2

PLoS ONE ◽  
2014 ◽  
Vol 9 (5) ◽  
pp. e96726 ◽  
Author(s):  
Cihan Oguz ◽  
Alida Palmisano ◽  
Teeraphan Laomettachit ◽  
Layne T. Watson ◽  
William T. Baumann ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Stochastic Model ◽  
Yeast Cell ◽  
Budding Yeast ◽  
Yeast Cell Cycle ◽  
Cell Cycle Dynamics ◽  
Budding Yeast Cell Cycle

scholarly journals Lsr2, a nucleoid-associated protein influencing mycobacterial cell cycle

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Marta Kołodziej ◽  
Damian Trojanowski ◽  
Katarzyna Bury ◽  
Joanna Hołówka ◽  
Weronika Matysik ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Cell Biology ◽  
Fluorescent Microscopy ◽  
Regulation Of Gene Expression ◽  
Model Organism ◽  
Time Lapse ◽  
Mycobacterial Cell ◽  
Associated Proteins ◽  
Nucleoprotein Complexes

AbstractNucleoid-associated proteins (NAPs) are responsible for maintaining highly organized and yet dynamic chromosome structure in bacteria. The genus Mycobacterium possesses a unique set of NAPs, including Lsr2, which is a DNA-bridging protein. Importantly, Lsr2 is essential for the M. tuberculosis during infection exhibiting pleiotropic activities including regulation of gene expression (mainly as a repressor). Here, we report that deletion of lsr2 gene profoundly impacts the cell morphology of M. smegmatis, which is a model organism for studying the cell biology of M. tuberculosis and other mycobacterial pathogens. Cells lacking Lsr2 are shorter, wider, and more rigid than the wild-type cells. Using time-lapse fluorescent microscopy, we showed that fluorescently tagged Lsr2 forms large and dynamic nucleoprotein complexes, and that the N-terminal oligomerization domain of Lsr2 is indispensable for the formation of nucleoprotein complexes in vivo. Moreover, lsr2 deletion exerts a significant effect on the replication time and replisome dynamics. Thus, we propose that the Lsr2 nucleoprotein complexes may contribute to maintaining the proper organization of the newly synthesized DNA and therefore influencing mycobacterial cell cycle.

Time-lapse imaging of cell cycle dynamics during development in living cardiomyocyte

2014 ◽  
Vol 72 ◽  
pp. 241-249 ◽  
Author(s):  
Hisayuki Hashimoto ◽  
Shinsuke Yuasa ◽  
Hidenori Tabata ◽  
Shugo Tohyama ◽  
Nozomi Hayashiji ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Time Lapse ◽  
Cell Cycle Dynamics ◽  
Time Lapse Imaging

scholarly journals Stochastic models of cell invasion with fluorescent cell cycle indicators

2018 ◽  
Author(s):  
Matthew J Simpson ◽  
Wang Jin ◽  
Sean T Vittadello ◽  
Tamara A Tambyah ◽  
Jacob M Ryan ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Stochastic Model ◽  
Cell Biology ◽  
Exclusion Process ◽  
Reaction Diffusion ◽  
Reaction Diffusion Equations ◽  
Relevant Parameter ◽  
Scratch Assay ◽  
Fluorescent Cell ◽  
Limit Model

AbstractFluorescent cell cycle labelling in cell biology experiments provides real time information about the location of individual cells, as well as the phase of the cell cycle of individual cells. We develop a stochastic, lattice-based random walk model of a two-dimensional scratch assay where the total population is composed of three distinct subpopulations which we visualise as red, yellow and green subpopulations. Our model mimics FUCCI technology in which cells in the G1 phase of the cell cycle fluoresce red, cells in the early S phase fluoresce yellow, and cells in the S/G2/M phase fluoresce green. The model is an exclusion process so that any potential motility or proliferation event that would place an agent on an occupied lattice site is aborted. Using experimental images and previous experimental measurements, we explain how to apply the stochastic model to simulate a scratch assay initialised with a low to moderate density monolayer of human melanoma cell line. We obtain additional mathematical insight by deriving an approximate partial differential equation (PDE) description of the stochastic model, leading to a novel system of three coupled nonlinear reaction diffusion equations. Comparing averaged simulation data with the solution of the continuum limit model confirms that the PDE description is accurate for biologically-relevant parameter combinations.

350 COMPARATIVE TIME-LAPSE STUDIES ON PORCINE EMBRYOS FERTILIZED IN VITRO WITH FLOW CYTOMETRICALLY SEX SORTED SPERMATOZOA

2006 ◽  
Vol 18 (2) ◽  
pp. 282
Author(s):  
D. Rath ◽  
S. Schulze
Keyword(s):  
Cell Cycle ◽  
In Vitro Fertilization ◽  
Time Lapse ◽  
Culture Period ◽  
Similar Data ◽  
Vitro Fertilization ◽  
Cell Cycles ◽  
Cell Numbers

The objective of the present time-lapse studies was to compare developmental characteristics of porcine embryos after in vitro fertilization with flow cytometrically sexed spermatozoa during an in vitro culture period. Immature oocytes were matured (n = 851) and fertilized in vitro using 50 spermatozoa of either sex per oocyte. Additionally, in vivo-matured oocytes (n = 700) were derived from hormonally stimulated (eCG/hCG) prepuberal gilts, which were slaughtered 38 h after hCG treatment. Potential zygotes were introduced into the time-lapse system (50 µL-microdrops, NCSU-23) 18 h after the onset of in vitro fertilization. The onset and duration of cell cycles and blastomere rotation as well as collapses and re-expansion of cytoplasm were investigated. At the end of the culture period (168 h), embryos were labeled with Hoechst 33342 to identify the number of cell nuclei. The time-lapse system consisted of an incubation chamber installed on an inverted phase-contrast microscope and gassed with 5% CO2 in maximally humidified air. Computer controlled positioning of the chamber allowed the capture and storage of digital images of individual embryos every 30 min over a 7-day period. Converted time values were tested by ANOVA or ANOVA on ranks. In total, 700 in vivo-matured oocytes were fertilized in vitro (Y-sperm: 342; X-sperm: 358). Cleavage rates were 45.6% for male and 40.2% for female embryos. Out of these, 45.5% developed to male and 62.5% to female blastocysts, respectively. The onset and duration of cell cycles differed significantly at the 2-cell and morula stages (P < 0.01). The onset and number of rotations as well as collapses and re-expansion of cytoplasm were not different. Mean cell numbers of blastocysts were equal for both sexes (male: 35.2; female: 38.8). In parallel, 851 in vitro-matured oocytes were fertilized in vitro (Y-sperm: 431; X-sperm: 422). Cleavage rates were 45.5% for male and 49.3% for female embryos. Out of these, 54.1% developed to male and 56.7% to female blastocysts, respectively. The onset and duration of cell cycles and rotations as well as collapses were not significantly different between sexes. Mean cell numbers of blastocysts were equal for both sexes (male: 29.5; female: 27.7). Comparison between male embryos derived from in vivo (VV) or in vitro (VT)-matured oocytes showed a delay of the onset of the second cell cycle for VV (P < 0.001) and at the blastocyst stage for VT (P < 0.001). Accordingly, in these stages the duration of the cell cycle was shortened (P < 0.001). The onset and duration of rotations as well as collapses and re-expansion of cytoplasm were not different. Similar data were obtained for the female embryos. The data suggest slight sex related differences (onset of cell cycles), but these might be masked in embryos produced from in vitro-matured oocytes due to their higher individual variability. The experiment also shows the advantages of a time-lapse system to identify dynamic cell processes. It might also be a useful tool to precisely correlate cell cycle events to activation of certain marker genes.

NONLINEAR DYNAMICS OF CELL CYCLES WITH STOCHASTIC MATHEMATICAL MODELS

2009 ◽  
Vol 17 (03) ◽  
pp. 425-460 ◽  
Author(s):  
RODERICK V. N. MELNIK ◽  
XILIN WEI ◽  
GABRIEL MORENO–HAGELSIEB
Keyword(s):  
Nonlinear Dynamics ◽  
Cell Cycle ◽  
Regulatory Networks ◽  
Deterministic Model ◽  
Phase Portraits ◽  
Nonlinear Phenomena ◽  
Cellular Dynamics ◽  
Cell Cycles ◽  
Special Events ◽  
Wide Range

Cell cycles are fundamental components of all living organisms and their systematic studies extend our knowledge about the interconnection between regulatory, metabolic, and signaling networks, and therefore open new opportunities for our ultimate efficient control of cellular processes for disease treatments, as well as for a wide variety of biomedical and biotechnological applications. In the study of cell cycles, nonlinear phenomena play a paramount role, in particular in those cases where the cellular dynamics is in the focus of attention. Quantification of this dynamics is a challenging task due to a wide range of parameters that require estimations and the presence of many stochastic effects. Based on the originally deterministic model, in this paper we develop a hierarchy of models that allow us to describe the nonlinear dynamics accounting for special events of cell cycles. First, we develop a model that takes into account fluctuations of relative concentrations of proteins during special events of cell cycles. Such fluctuations are induced by varying rates of relative concentrations of proteins and/or by relative concentrations of proteins themselves. As such fluctuations may be responsible for qualitative changes in the cell, we develop a new model that accounts for the effect of cellular dynamics on the cell cycle. Finally, we analyze numerically nonlinear effects in the cell cycle by constructing phase portraits based on the newly developed model and carry out a parametric sensitivity analysis in order to identify parameters for an efficient cell cycle control. The results of computational experiments demonstrate that the metabolic events in gene regulatory networks can qualitatively influence the dynamics of the cell cycle.

A Dynamical Stochastic Model of Yeast Translation Across the Cell Cycle

2020 ◽  
Author(s):  
Martin Seeger ◽  
Max Flöttmann ◽  
Edda Klipp
Keyword(s):  
Cell Cycle ◽  
Stochastic Model
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