Duration Of Cell Cycle Phases Research Articles

Basic Methods of Cell Cycle Analysis.

Cellular growth and the preparation of cells for division between two successive cell divisions is called the cell cycle. The cell cycle is divided into several phases; the length of these particular cell cycle phases is an important characteristic of cell life. The progression of cells through these phases is a highly orchestrated process governed by endogenous and exogenous factors. For the elucidation of the role of these factors, including pathological aspects, various methods have been developed. Among these methods, those focused on the analysis of the duration of distinct cell cycle phases play important role. The main aim of this review is to guide the readers through the basic methods of the determination of cell cycle phases and estimation of their length, with a focus on the effectiveness and reproducibility of the described methods.

Quantifying and mathematical modelling of the influence of soluble adenylate cyclase on cell cycle in human endothelial cells with Bayesian inference.

Adenosine-3', 5'-cyclic monophosphate (cAMP) produced by adenylate cyclases (ADCYs) is an established key regulator of cell homoeostasis. However, its role in cell cycle control is still controversially discussed. This study focussed on the impact of soluble HCO3 - -activated ADCY10 on cell cycle progression. Effects are quantified with Bayesian inference integrating a mathematical model and experimental data. The activity of ADCY10 in human umbilical vein endothelial cells (HUVECs) was either pharmacologically inhibited by KH7 or endogenously activated by HCO3 - . Cell numbers of individual cell cycle phases were assessed over time using flow cytometry. Based on these numbers, cell cycle dynamics were analysed using a mathematical model. This allowed precise quantification of cell cycle dynamics with model parameters that describe the durations of individual cell cycle phases. Endogenous inactivation of ADCY10 resulted in prolongation of mean cell cycle times (38.7 ± 8.3h at 0 mM HCO3 - vs 30.3 ± 2.7h at 24 mM HCO3 - ), while pharmacological inhibition resulted in functional arrest of cell cycle by increasing mean cell cycle time after G0 /G1 synchronization to 221.0 ± 96.3h. All cell cycle phases progressed slower due to ADCY10 inactivation. In particular, the G1 -S transition was quantitatively the most influenced by ADCY10. In conclusion, the data of the present study show that ADCY10 is a key regulator in cell cycle progression linked specifically to the G1 -S transition.

Transcriptional Fluctuations Govern the Serum-Dependent Cell Cycle Duration Heterogeneities in Mammalian Cells.

Mammalian cells exhibit a high degree of intercellular variability in cell cycle period and phase durations. However, the factors orchestrating the cell cycle duration heterogeneities remain unclear. Herein, by combining cell cycle network-based mathematical models with live single-cell imaging studies under varied serum conditions, we demonstrate that fluctuating transcription rates of cell cycle regulatory genes across cell lineages and during cell cycle progression in mammalian cells majorly govern the robust correlation patterns of cell cycle period and phase durations among sister, cousin, and mother-daughter lineage pairs. However, for the overall cellular population, alteration in the serum level modulates the fluctuation and correlation patterns of cell cycle period and phase durations in a correlated manner. These heterogeneities at the population level can be fine-tuned under limited serum conditions by perturbing the cell cycle network using a p38-signaling inhibitor without affecting the robust lineage-level correlations. Overall, our approach identifies transcriptional fluctuations as the key controlling factor for the cell cycle duration heterogeneities and predicts ways to reduce cell-to-cell variabilities by perturbing the cell cycle network regulations.

Resistance of Hypoxic Cells to Ionizing Radiation Is Mediated in Part via Hypoxia-Induced Quiescence.

Double strand breaks (DSBs) are highly toxic to a cell, a property that is exploited in radiation therapy. A critical component for the damage induction is cellular oxygen, making hypoxic tumor areas refractory to the efficacy of radiation treatment. During a fractionated radiation regimen, these hypoxic areas can be re-oxygenated. Nonetheless, hypoxia still constitutes a negative prognostic factor for the patient’s outcome. We hypothesized that this might be attributed to specific hypoxia-induced cellular traits that are maintained upon reoxygenation. Here, we show that reoxygenation of hypoxic non-transformed RPE-1 cells fully restored induction of DSBs but the cells remain radioresistant as a consequence of hypoxia-induced quiescence. With the use of the cell cycle indicators (FUCCI), cell cycle-specific radiation sensitivity, the cell cycle phase duration with live cell imaging, and single cell tracing were assessed. We observed that RPE-1 cells experience a longer G1 phase under hypoxia and retain a large fraction of cells that are non-cycling. Expression of HPV oncoprotein E7 prevents hypoxia-induced quiescence and abolishes the radioprotective effect. In line with this, HPV-negative cancer cell lines retain radioresistance, while HPV-positive cancer cell lines are radiosensitized upon reoxygenation. Quiescence induction in hypoxia and its HPV-driven prevention was observed in 3D multicellular spheroids. Collectively, we identify a new hypoxia-dependent radioprotective phenotype due to hypoxia-induced quiescence that accounts for a global decrease in radiosensitivity that can be retained upon reoxygenation and is absent in cells expressing oncoprotein E7.

An immunocytochemical approach to the analysis of the cell division cycle in the rat cerebellar neuroepithelium

ABSTRACT Cerebellar neurons are generated from the rhombic lip and the neuroepithelium. In this study, we analyze the histogenesis of the cerebellar neuroepithelium in terms of cellular kinetics. The experimental animals are the offspring of pregnant dams injected with 5-bromo-2ʹ-deoxyuridine (BrdU) on embryonic day 13. We infer the fraction of S-phase cells by examining a range of survival times after a single BrdU-exposure and a cumulative BrdU-labeling sequence, which allow for the derivation of cell-cycle parameters and phase durations. The current results indicate that the dose of BrdU employed (35 mg/kg) provides saturation S-phase labeling from at least 1 h after marker delivery. The duration of G2, mitotic phase, and G1 are 1.2, 0.5, and 6.9 h, respectively. The duration for the S-phase, growth fraction, and the whole cycle are obtained on the basis of two proliferative models, steady-state and exponential growth. Both models provided similar results. In conclusion, our results indicate that the steady-state and the cumulative S-phase labeling paradigms can be adopted to analyze cell cycle parameters in the cerebellar neuroepithelium. Current results can help in understanding the regulatory mechanisms of cerebellar histogenesis and the cell biological mechanisms of the proliferative cycle of the neuroepithelium.

Mathematical modelling reveals unexpected inheritance and variability patterns of cell cycle parameters in mammalian cells.

The cell cycle is the fundamental process of cell populations, it is regulated by environmental cues and by intracellular checkpoints. Cell cycle variability in clonal cell population is caused by stochastic processes such as random partitioning of cellular components to progeny cells at division and random interactions among biomolecules in cells. One of the important biological questions is how the dynamics at the cell cycle scale, which is related to family dependencies between the cell and its descendants, affects cell population behavior in the long-run. We address this question using a “mechanistic” model, built based on observations of single cells over several cell generations, and then extrapolated in time. We used cell pedigree observations of NIH 3T3 cells including FUCCI markers, to determine patterns of inheritance of cell-cycle phase durations and single-cell protein dynamics. Based on that information we developed a hybrid mathematical model, involving bifurcating autoregression to describe stochasticity of partitioning and inheritance of cell-cycle-phase times, and an ordinary differential equation system to capture single-cell protein dynamics. Long-term simulations, concordant with in vitro experiments, demonstrated the model reproduced the main features of our data and had homeostatic properties. Moreover, heterogeneity of cell cycle may have important consequences during population development. We discovered an effect similar to genetic drift, amplified by family relationships among cells. In consequence, the progeny of a single cell with a short cell cycle time had a high probability of eventually dominating the population, due to the heritability of cell-cycle phases. Patterns of epigenetic heritability in proliferating cells are important for understanding long-term trends of cell populations which are either required to provide the influx of maturing cells (such as hematopoietic stem cells) or which started proliferating uncontrollably (such as cancer cells).

Time varying causal network reconstruction of a mouse cell cycle

BackgroundBiochemical networks are often described through static or time-averaged measurements of the component macromolecules. Temporal variation in these components plays an important role in both describing the dynamical nature of the network as well as providing insights into causal mechanisms. Few methods exist, specifically for systems with many variables, for analyzing time series data to identify distinct temporal regimes and the corresponding time-varying causal networks and mechanisms.ResultsIn this study, we use well-constructed temporal transcriptional measurements in a mammalian cell during a cell cycle, to identify dynamical networks and mechanisms describing the cell cycle. The methods we have used and developed in part deal with Granger causality, Vector Autoregression, Estimation Stability with Cross Validation and a nonparametric change point detection algorithm that enable estimating temporally evolving directed networks that provide a comprehensive picture of the crosstalk among different molecular components. We applied our approach to RNA-seq time-course data spanning nearly two cell cycles from Mouse Embryonic Fibroblast (MEF) primary cells. The change-point detection algorithm is able to extract precise information on the duration and timing of cell cycle phases. Using Least Absolute Shrinkage and Selection Operator (LASSO) and Estimation Stability with Cross Validation (ES-CV), we were able to, without any prior biological knowledge, extract information on the phase-specific causal interaction of cell cycle genes, as well as temporal interdependencies of biological mechanisms through a complete cell cycle.ConclusionsThe temporal dependence of cellular components we provide in our model goes beyond what is known in the literature. Furthermore, our inference of dynamic interplay of multiple intracellular mechanisms and their temporal dependence on one another can be used to predict time-varying cellular responses, and provide insight on the design of precise experiments for modulating the regulation of the cell cycle.

Eternal sunshine of the spotless cycle.

Understanding the quantitative principles underlying the durations of each of the four cell cycle phases has remained a challenge, despite the extensive knowledge on the molecular components and mechanisms related to cell cycle control. In their recent study, Purvis and colleagues (Chao et al , 2019) quantify cell cycle phase durations in human cells and propose a model whereby cell cycle progression in single cells is a succession of uncoupled, memoryless phases, each composed of a characteristic rate and number of steps.

A transcriptome-wide analysis deciphers distinct roles of G1 cyclins in temporal organization of the yeast cell cycle

Oscillating gene expression is crucial for correct timing and progression through cell cycle. In Saccharomyces cerevisiae, G1 cyclins Cln1–3 are essential drivers of the cell cycle and have an important role for temporal fine-tuning. We measured time-resolved transcriptome-wide gene expression for wild type and cyclin single and double knockouts over cell cycle with and without osmotic stress. Clustering of expression profiles, peak time detection of oscillating genes, integration with transcription factor network dynamics, and assignment to cell cycle phases allowed us to quantify the effect of genetic or stress perturbations on the duration of cell cycle phases. Cln1 and Cln2 showed functional differences, especially affecting later phases. Deletion of Cln3 led to a delay of START followed by normal progression through later phases. Our data and network analysis suggest mutual effects of cyclins with the transcriptional regulators SBF and MBF.

Scaling of G1 Duration with Population Doubling Time by a Cyclin in Saccharomyces cerevisiae.

The longer cells stay in particular phases of the cell cycle, the longer it will take these cell populations to increase. However, the above qualitative description has very little predictive value, unless it can be codified mathematically. A quantitative relation that defines the population doubling time (Td) as a function of the time eukaryotic cells spend in specific cell cycle phases would be instrumental for estimating rates of cell proliferation and for evaluating introduced perturbations. Here, we show that in human cells, the length of the G1 phase (TG1) regressed on Td with a slope of ≈0.75, while in the yeast Saccharomyces cerevisiae, the slope was slightly smaller, at ≈0.60. On the other hand, cell size was not strongly associated with Td or TG1 in cell cultures that were proliferating at different rates. Furthermore, we show that levels of the yeast G1 cyclin Cln3p were positively associated with rates of cell proliferation over a broad range, at least in part through translational control mediated by a short upstream ORF (uORF) in the CLN3 transcript. Cln3p was also necessary for the proper scaling between TG1 and Td In contrast, yeast lacking the Whi5p transcriptional repressor maintained the scaling between TG1 and Td These data reveal fundamental scaling relationships between the duration of eukaryotic cell cycle phases and rates of cell proliferation, point to the necessary role of Cln3p in these relationships in yeast, and provide a mechanistic basis linking Cln3p levels to proliferation rates and the scaling of G1 with doubling time.

Transcriptional timing and noise of yeast cell cycle regulators\u2014a single cell and single molecule approach

Gene expression is a stochastic process and its appropriate regulation is critical for cell cycle progression. Cellular stress response necessitates expression reprogramming and cell cycle arrest. While previous studies are mostly based on bulk experiments influenced by synchronization effects or lack temporal distribution, time-resolved methods on single cells are needed to understand eukaryotic cell cycle in context of noisy gene expression and external perturbations. Using smFISH, microscopy and morphological markers, we monitored mRNA abundances over cell cycle phases and calculated transcriptional noise for SIC1, CLN2, and CLB5, the main G1/S transition regulators in budding yeast. We employed mathematical modeling for in silico synchronization and for derivation of time-courses from single cell data. This approach disclosed detailed quantitative insights into transcriptional regulation with and without stress, not available from bulk experiments before. First, besides the main peak in G1 we found an upshift of CLN2 and CLB5 expression in late mitosis. Second, all three genes showed basal expression throughout cell cycle enlightening that transcription is not divided in on and off but rather in high and low phases. Finally, exposing cells to osmotic stress revealed different periods of transcriptional inhibition for CLN2 and CLB5 and the impact of stress on cell cycle phase duration. Combining experimental and computational approaches allowed us to precisely assess cell cycle progression timing, as well as gene expression dynamics.

Modelling T cell proliferation: Dynamics heterogeneity depending on cell differentiation, age, and genetic background.

Cell proliferation is the common characteristic of all biological systems. The immune system insures the maintenance of body integrity on the basis of a continuous production of diversified T lymphocytes in the thymus. This involves processes of proliferation, differentiation, selection, death and migration of lymphocytes to peripheral tissues, where proliferation also occurs upon antigen recognition. Quantification of cell proliferation dynamics requires specific experimental methods and mathematical modelling. Here, we assess the impact of genetics and aging on the immune system by investigating the dynamics of proliferation of T lymphocytes across their differentiation through thymus and spleen in mice. Our investigation is based on single-cell multicolour flow cytometry analysis revealing the active incorporation of a thymidine analogue during S phase after pulse-chase-pulse experiments in vivo, versus cell DNA content. A generic mathematical model of state transition simulates through Ordinary Differential Equations (ODEs) the evolution of single cell behaviour during various durations of labelling. It allows us to fit our data, to deduce proliferation rates and estimate cell cycle durations in sub-populations. Our model is simple and flexible and is validated with other durations of pulse/chase experiments. Our results reveal that T cell proliferation is highly heterogeneous but with a specific “signature” that depends upon genetic origins, is specific to cell differentiation stages in thymus and spleen and is altered with age. In conclusion, our model allows us to infer proliferation rates and cell cycle phase durations from complex experimental 5-ethynyl-2'-deoxyuridine (EdU) data, revealing T cell proliferation heterogeneity and specific signatures.

Environmental stress speeds up DNA replication in Pseudomonas putida in chemostat cultivations.

Cellular response to different types of stress is the hallmark of the cell's strategy for survival. How organisms adjust their cell cycle dynamics to compensate for changes in environmental conditions is an important unanswered question in bacterial physiology. A cell using binary fission for reproduction passes through three stages during its cell cycle: a stage from cell birth to initiation of replication, a DNA replication phase and a period of cell division. We present a detailed analysis of durations of cell cycle phases, investigating their dynamics under environmental stress conditions. Applying continuous steady state cultivations (chemostats), the DNA content of a Pseudomonas putida KT2440 population was quantified with flow cytometry at distinct growth rates. Data-driven modeling revealed that under stress conditions, such as oxygen deprivation, solvent exposure and decreased iron availability, DNA replication was accelerated correlated to the severity of the imposed stress (up to 1.9-fold). Cells maintained constant growth rates by balancing the shortened replication phase with extended cell cycle phases before and after replication. Transcriptome data underpin the transcriptional upregulation of crucial genes of the replication machinery. Hence adaption of DNA replication speed appears to be an important strategy to withstand environmental stress.

A mathematical model of subpopulation kinetics for the deconvolution of leukaemia heterogeneity.

Acute myeloid leukaemia is characterized by marked inter- and intra-patient heterogeneity, the identification of which is critical for the design of personalized treatments. Heterogeneity of leukaemic cells is determined by mutations which ultimately affect the cell cycle. We have developed and validated a biologically relevant, mathematical model of the cell cycle based on unique cell-cycle signatures, defined by duration of cell-cycle phases and cyclin profiles as determined by flow cytometry, for three leukaemia cell lines. The model was discretized for the different phases in their respective progress variables (cyclins and DNA), resulting in a set of time-dependent ordinary differential equations. Cell-cycle phase distribution and cyclin concentration profiles were validated against population chase experiments. Heterogeneity was simulated in culture by combining the three cell lines in a blinded experimental set-up. Based on individual kinetics, the model was capable of identifying and quantifying cellular heterogeneity. When supplying the initial conditions only, the model predicted future cell population dynamics and estimated the previous heterogeneous composition of cells. Identification of heterogeneous leukaemia clones at diagnosis and post-treatment using such a mathematical platform has the potential to predict multiple future outcomes in response to induction and consolidation chemotherapy as well as relapse kinetics.

S-phase duration is the main target of cell cycle regulation in neural progenitors of developing ferret neocortex.

ABSTRACTThe evolutionary expansion of the neocortex primarily reflects increases in abundance and proliferative capacity of cortical progenitors and in the length of the neurogenic period during development. Cell cycle parameters of neocortical progenitors are an important determinant of cortical development. The ferret (Mustela putorius furo), a gyrencephalic mammal, has gained increasing importance as a model for studying corticogenesis. Here, we have studied the abundance, proliferation, and cell cycle parameters of different neural progenitor types, defined by their differential expression of the transcription factors Pax6 and Tbr2, in the various germinal zones of developing ferret neocortex. We focused our analyses on postnatal day 1, a late stage of cortical neurogenesis when upper‐layer neurons are produced. Based on cumulative 5‐ethynyl‐2′‐deoxyuridine (EdU) labeling as well as Ki67 and proliferating cell nuclear antigen (PCNA) immunofluorescence, we determined the duration of the various cell cycle phases of the different neocortical progenitor subpopulations. Ferret neocortical progenitors were found to exhibit longer cell cycles than those of rodents and little variation in the duration of G1 among distinct progenitor types, also in contrast to rodents. Remarkably, the main difference in cell cycle parameters among the various progenitor types was the duration of S‐phase, which became shorter as progenitors progressively changed transcription factor expression from patterns characteristic of self‐renewal to those of neuron production. Hence, S‐phase duration emerges as major target of cell cycle regulation in cortical progenitors of this gyrencephalic mammal. J. Comp. Neurol. 524:456–470, 2016. © 2015 The Authors The Journal of Comparative Neurology Published by Wiley Periodicals, Inc.

Different rates of DNA replication at early versus late S-phase sections: multiscale modeling of stochastic events related to DNA content/EdU (5-ethynyl-2'deoxyuridine) incorporation distributions.

Mathematical modeling allows relating molecular events to single-cell characteristics assessed by multiparameter cytometry. In the present study we labeled newly synthesized DNA in A549 human lung carcinoma cells with 15-120 min pulses of EdU. All DNA was stained with DAPI and cellular fluorescence was measured by laser scanning cytometry. The frequency of cells in the ascending (left) side of the "horseshoe"-shaped EdU/DAPI bivariate distributions reports the rate of DNA replication at the time of entrance to S phase while their frequency in the descending (right) side is a marker of DNA replication rate at the time of transition from S to G2 phase. To understand the connection between molecular-scale events and scatterplot asymmetry, we developed a multiscale stochastic model, which simulates DNA replication and cell cycle progression of individual cells and produces in silico EdU/DAPI scatterplots. For each S-phase cell the time points at which replication origins are fired are modeled by a non-homogeneous Poisson Process (NHPP). Shifted gamma distributions are assumed for durations of cell cycle phases (G1, S and G2 M), Depending on the rate of DNA synthesis being an increasing or decreasing function, simulated EdU/DAPI bivariate graphs show predominance of cells in left (early-S) or right (late-S) side of the horseshoe distribution. Assuming NHPP rate estimated from independent experiments, simulated EdU/DAPI graphs are nearly indistinguishable from those experimentally observed. This finding proves consistency between the S-phase DNA-replication rate based on molecular-scale analyses, and cell population kinetics ascertained from EdU/DAPI scatterplots and demonstrates that DNA replication rate at entrance to S is relatively slow compared with its rather abrupt termination during S to G2 transition. Our approach opens a possibility of similar modeling to study the effect of anticancer drugs on DNA replication/cell cycle progression and also to quantify other kinetic events that can be measured during S-phase.

Current breast cancer proliferative markers correlate variably based on decoupled duration of cell cycle phases.

Mitotic count, PhH3, and MIB-1 are used as measures of the proportion of proliferating malignant cells in surgical pathology. They highlight different stages of the cell cycle, but little is known about how this affects their counts. This study assesses the strength of their correlations and attempts to determine the relationship between them. Proliferation counts for forty-nine consecutive cases of invasive breast carcinomas were analyzed, with the same tumor area on each stain counted using digital image analysis. The integrated optical density (IOD) of nuclei was measured as an approximation of nuclear DNA content. PhH3 strongly correlated with mitotic count (r = 0.94). Weaker correlations were found between MIB-1 versus PhH3 (r = 0.79) and mitotic count (r = 0.83). Nuclear IOD showed stronger correlation with MIB-1 (r = 0.37) than to mitotic count (r = 0.23) and PhH3 (r = 0.34). With evidence from a literature review, it is suggested that the weaker correlations with MIB-1 are not explained by count imprecision or error, but relies on temporal decorrelation between cell cycle phases. Consequences on correlation between these proliferative markers are illustrated by mathematical models.

Drosophila splicing factor SF2 knock‐down mutant shows altered cell‐cycle in vivo

Pioneering single gene study documents pre-mRNA processing proteins participation in the cell-cycle regulation of multi-cellular animals (Andersen and Tapon, 2008, J Biol Chem 283: 31256-67). Whole-genome RNAi screen in Drosophila tissue-culture cell lines demonstrates that 17 genes involved in RNA-processing are required for G2/M check-point function (Kondo and Perrimon, 2011, Sci Signal 4: rs1). In particular, the silencing of Splicing Factor 2 (SF2) increases the number of G2(M) cells. We have measured the absolute duration of cell-cycle phases in SF2 depleted flies with the use of flow cytometry and growth parameters of GFP marked mosaic clones. For SF2 mutant cells, G1 = 1.89 h, G2(M) = 7.22 h and S = 1.30 h compared with G1 = 2.25 h, G2(M) = 4.86 h and S = 1.28 h for control normal cells. Thus, G2(M) phase appears to be longer in SF2 silenced cells, supporting the evidence that this splicing protein participates in G2-M check-point function.

Haploid unit-ploidy transition of tetraploid and octaploid H1 (ES) cells in long-term culturing

Haploid unit-ploidy transition in tetraploid and octaploid mouse H1 (ES) cells (4H1 and 8H1 cells, respectively) during long-term culturing was observed using flow cytometry. The DNA content of 4H1 cells was elevated from 3.5C to 4.5C, and that of 8H1 cells was degraded from 6.5C to 5.5C, in addition to gradual DNA loss (C: complement). The timing of the transition was not predetermined. Cell cycle parameters, doubling time and phase durations, were essentially the same before and after the transition, suggesting that most cells in a cell population were induced to undergo the ploidy transition at the same time. Cellular morphology was altered before and after the transition, suggesting that the ploidy shift changed cellular characteristics; however, pluripotency was maintained irrespective of DNA content. Cell volume correlated with DNA content during the final stage of culturing. Diploid and hexaploid H1 (ES) cells--2H1 and 6H1 cells, respectively--were used as control cells in which the ploidy was maintained for about 300 days of culturing. The haploid unit-ploidy transition was explained using a hypothesis concerning the DNA structure of polyploid cells: closing homologous chromosomes causes inhomogeneous cell division accompanying a haploid DNA set, suggesting the existence of a coupling apparatus connecting DNA fibers with a single haploid DNA set.

Time warping of evolutionary distant temporal gene expression data based on noise suppression

BackgroundComparative analysis of genome wide temporal gene expression data has a broad potential area of application, including evolutionary biology, developmental biology, and medicine. However, at large evolutionary distances, the construction of global alignments and the consequent comparison of the time-series data are difficult. The main reason is the accumulation of variability in expression profiles of orthologous genes, in the course of evolution.ResultsWe applied Pearson distance matrices, in combination with other noise-suppression techniques and data filtering to improve alignments. This novel framework enhanced the capacity to capture the similarities between the temporal gene expression datasets separated by large evolutionary distances. We aligned and compared the temporal gene expression data in budding (Saccharomyces cerevisiae) and fission (Schizosaccharomyces pombe) yeast, which are separated by more then ~400 myr of evolution. We found that the global alignment (time warping) properly matched the duration of cell cycle phases in these distant organisms, which was measured in prior studies. At the same time, when applied to individual ortholog pairs, this alignment procedure revealed groups of genes with distinct alignments, different from the global alignment.ConclusionOur alignment-based predictions of differences in the cell cycle phases between the two yeast species were in a good agreement with the existing data, thus supporting the computational strategy adopted in this study. We propose that the existence of the alternative alignments, specific to distinct groups of genes, suggests presence of different synchronization modes between the two organisms and possible functional decoupling of particular physiological gene networks in the course of evolution.