Model Of Cell Cycle Progression Research Articles

Live-cell imaging defines a threshold in CDK activity at the G2/M transition

Cyclin-dependent kinase (CDK) determines the temporal ordering of the cell cycle phases. However, despite significant progress in studying regulators of CDK and phosphorylation patterns of CDK substrates at the population level, it remains elusive how CDK regulators coordinately affect CDK activity at the single-cell level and how CDK controls the temporal order of cell cycle events. Here, we elucidate the dynamics of CDK activity in fission yeast and mammalian cells by developing a CDK activity biosensor, Eevee-spCDK. We find that although CDK activity does not necessarily correlate with cyclin levels, it converges to the same level around mitotic onset in several mutant backgrounds, including pom1Δ cells and wee1 or cdc25 overexpressing cells. These data provide direct evidence that cells enter the M phase when CDK activity reaches a high threshold, consistent with the quantitative model of cell cycle progression in fission yeast.

Cell cycle progression score and PTEN as prognostic factors for metastasis in intermediate and high-risk prostate cancer.

e16575 Background: To evaluate the cell cycle progression (CCP) score and PTEN as prognostic markers for risk of metastasis in a radical prostatectomy (RP) cohort of NCCN intermediate and high risk prostate cancer. Methods: This IRB-approved case-cohort study included men treated with RP at Johns Hopkins from 2007-2015. Paraffin-embedded RP tissue was analyzed blind to study outcome at Myriad Genetics, for CCP score using qRT-PCR, and PTEN by immunohistochemistry. Metastasis-free survival (MFS) was analyzed with the Cox proportional hazards model, weighted for case-cohort design. CCP and PTEN were analyzed independently, and adjusted for CAPRA-S. The CCR score, which combines CCP and CAPRA-S, was also analyzed. Data were analyzed independently by Johns Hopkins and Myriad Genetics. Results: There were 209 patients, of whom 42 were cases (metastasis). Median age was 59 years, 47% had Gleason 4+3 or higher, 48% had non-organ confined tumor, and 18% had seminal vesicle or lymph node involvement. NCCN risk was intermediate in 77% and high in 23%. Median follow-up was 4 years. Both CCP and PTEN, as well as CCR score, were statistically significant in univariate analyses, but only CCP retained statistical significance in a multivariable model of CCP, PTEN, and CAPRA-S (Table). Conclusions: This is the first comparison of the CCP score and PTEN as risk factors for metastasis in a recent RP cohort of patients at NCCN intermediate or high risk. Both CCP score and PTEN were strongly associated with MFS in univariate analyses, but only CCP score retained significance in a multivariable analysis with both biomarkers adjusted for CAPRA-S. [Table: see text]

Stochastic multi-scale models of competition within heterogeneous cellular populations: Simulation methods and mean-field analysis

We propose a modelling framework to analyse the stochastic behaviour of heterogeneous, multi-scale cellular populations. We illustrate our methodology with a particular example in which we study a population with an oxygen-regulated proliferation rate. Our formulation is based on an age-dependent stochastic process. Cells within the population are characterised by their age (i.e. time elapsed since they were born). The age-dependent (oxygen-regulated) birth rate is given by a stochastic model of oxygen-dependent cell cycle progression. Once the birth rate is determined, we formulate an age-dependent birth-and-death process, which dictates the time evolution of the cell population. The population is under a feedback loop which controls its steady state size (carrying capacity): cells consume oxygen which in turn fuels cell proliferation. We show that our stochastic model of cell cycle progression allows for heterogeneity within the cell population induced by stochastic effects. Such heterogeneous behaviour is reflected in variations in the proliferation rate. Within this set-up, we have established three main results. First, we have shown that the age to the G1/S transition, which essentially determines the birth rate, exhibits a remarkably simple scaling behaviour. Besides the fact that this simple behaviour emerges from a rather complex model, this allows for a huge simplification of our numerical methodology. A further result is the observation that heterogeneous populations undergo an internal process of quasi-neutral competition. Finally, we investigated the effects of cell-cycle-phase dependent therapies (such as radiation therapy) on heterogeneous populations. In particular, we have studied the case in which the population contains a quiescent sub-population. Our mean-field analysis and numerical simulations confirm that, if the survival fraction of the therapy is too high, rescue of the quiescent population occurs. This gives rise to emergence of resistance to therapy since the rescued population is less sensitive to therapy.

From START to FINISH: computational analysis of cell cycle control in budding yeast.

In the cell division cycle of budding yeast, START refers to a set of tightly linked events that prepare a cell for budding and DNA replication, and FINISH denotes the interrelated events by which the cell exits from mitosis and divides into mother and daughter cells. On the basis of recent progress made by molecular biologists in characterizing the genes and proteins that control START and FINISH, we crafted a new mathematical model of cell cycle progression in yeast. Our model exploits a natural separation of time scales in the cell cycle control network to construct a system of differential-algebraic equations for protein synthesis and degradation, post-translational modifications, and rapid formation and dissociation of multimeric complexes. The model provides a unified account of the observed phenotypes of 257 mutant yeast strains (98% of the 263 strains in the data set used to constrain the model). We then use the model to predict the phenotypes of 30 novel combinations of mutant alleles. Our comprehensive model of the molecular events controlling cell cycle progression in budding yeast has both explanatory and predictive power. Future experimental tests of the model’s predictions will be useful to refine the underlying molecular mechanism, to constrain the adjustable parameters of the model, and to provide new insights into how the cell division cycle is regulated in budding yeast.

A novel quantitative model of cell cycle progression based on cyclin-dependent kinases activity and population balances

Cell cycle regulates proliferative cell capacity under normal or pathologic conditions, and in general it governs all in vivo/in vitro cell growth and proliferation processes. Mathematical simulation by means of reliable and predictive models represents an important tool to interpret experiment results, to facilitate the definition of the optimal operating conditions for in vitro cultivation, or to predict the effect of a specific drug in normal/pathologic mammalian cells. Along these lines, a novel model of cell cycle progression is proposed in this work. Specifically, it is based on a population balance (PB) approach that allows one to quantitatively describe cell cycle progression through the different phases experienced by each cell of the entire population during its own life. The transition between two consecutive cell cycle phases is simulated by taking advantage of the biochemical kinetic model developed by Gérard and Goldbeter (2009) which involves cyclin-dependent kinases (CDKs) whose regulation is achieved through a variety of mechanisms that include association with cyclins and protein inhibitors, phosphorylation–dephosphorylation, and cyclin synthesis or degradation. This biochemical model properly describes the entire cell cycle of mammalian cells by maintaining a sufficient level of detail useful to identify check point for transition and to estimate phase duration required by PB. Specific examples are discussed to illustrate the ability of the proposed model to simulate the effect of drugs for in vitro trials of interest in oncology, regenerative medicine and tissue engineering.

A Mathematical Model of Cell Cycle Progression Applied to the MCF-7 Breast Cancer Cell Line

In this paper, we present a model of cell cycle progression and apply it to cells of the MCF-7 breast cancer cell line. We consider cells existing in the three typical cell cycle phases determined using flow cytometry: the G1, S, and G2/M phases. We further break each phase up into model phases in order to capture certain features such as cells remaining in phases for a minimum amount of time. The model is also able to capture the environmentally responsive part of the G1 phase, allowing for quantification of the number of environmentally responsive cells at each point in time. The model parameters are carefully chosen using data from various sources in the biological literature. The model is then validated against a variety of experiments, and the excellent fit with experimental results allows for insight into the mechanisms that influence observed biological phenomena. In particular, the model is used to question the common assumption that a 'slow cycling population' is necessary to explain some results. Finally, an extension is proposed, where cell death is included in order to accurately model the effects of tamoxifen, a common first line anticancer drug in breast cancer patients. We conclude that the model has strong potential to be used as an aid in future experiments to gain further insight into cell cycle progression and cell death.

A Model for the Adjustment of the Mitotic Clock by Cyclin and MPF Levels

A mathematical model of cell cycle progression is presented, which integrates recent biochemical information on the interaction of the maturation promotion factor (MPF) and cyclin. The model retrieves the dynamics observed in early embryos and explains how multiple cycles of MPF activity can be produced and how the internal clock that determines durations and number of cycles can be adjusted by modulating the rate of change in MPF or cyclin concentrations. Experiments are suggested for verifying the role of MPF activity in determining the length of the somatic cell cycle.