A quantitative analysis of the kinetics of the G(2) DNA damage checkpoint system

Curating models from BioModels: Developing a workflow for creating OMEX files

The reproducibility of computational biology models can be greatly facilitated by widely adopted standards and public repositories. We examined 50 models from the BioModels Database and attempted to validate the original curation and correct some of them if necessary. For each model, we reproduced these published results using Tellurium. Once reproduced we manually created a new set of files, with the model information stored by the Systems Biology Markup Language (SBML), and simulation instructions stored by the Simulation Experiment Description Markup Language (SED-ML), and everything included in an Open Modeling EXchange (OMEX) file, which could be used with a variety of simulators to reproduce the same results. On the one hand, the validation procedure of 50 models developed a manual workflow that we would use to build an automatic platform to help users more easily curate and verify models in the future. On the other hand, these exercises allowed us to find the limitations and possible enhancement of the current curation and tooling to verify and curate models.

Proteins as fuzzy controllers: Auto tuning a biological fuzzy inference system to predict protein dynamics in complex biological networks

Biological systems such as mammalian cell cycle are complex systems consisting of a large number of molecular species interacting in ways that produce complex nonlinear systems dynamics. Discrete models such as Boolean models and continuous models such as Ordinary Differential Equations (ODEs) have been widely used to study these systems. Boolean models are simple and can capture qualitative systems behaviour, but they cannot capture the continuous trends of protein concentrations, while ODE models capture continuous trends but require kinetics parameters that are limited. Further, as systems get larger, complexity of these models becomes an issue for parameterization, analysis and interpretation. Also, molecular systems operate under the conditions of uncertainty and noise and our understanding of molecular processes in general is more at a qualitative level characterised by vagueness, imprecision and ambiguity. Hence, as more data are generated, there is a greater need for simpler data driven methods that can approximate continuous system behaviour while representing vagueness and ambiguity without requiring kinetic parameters. Fuzzy inferencing is one such promising method with the ability to work with qualitative vague/imprecise biological knowledge. In this study, we propose a fuzzy inference system for representing continuous behaviour of proteins and apply to some key proteins in the mammalian cell cycle system. The methods we introduced here is novel to protein interaction systems and cell cycle proteins. Our study proposes a three-stage approach to develop fuzzy protein controllers. In stage one, protein system is studied for interactions. We studied some significant core controllers of mammalian cell cycle and their producers and degraders as presented in a published ODE model. Based on the observations from a dataset generated from it, we developed Fuzzy inference systems (FIS) in the second stage, that involved deriving fuzzy IF-THEN rules and their processing, and manually tuned the FIS to predict the dynamics of individual proteins. In stage three, we employed Particle Swarm Optimisation (PSO) for optimising the FIS to further enhance prediction accuracy. Systems dynamics simulation results of the optimised FIS models were in close agreement with the benchmark ODE model results. The results show that the FIS models provide a close approximation to the comprehensive benchmark model in robustly representing continuous protein dynamics while representing the control of protein behavior in an intuitive and transparent format without requiring kinetic parameters. Therefore, FIS models can be an alternative to ODEs in network modelling. Further, FIS models can be assembled to develop large complex systems without losing information or accuracy.

Semisynthetic aurones A14 protects against T-cell acute lymphoblastic leukemia via suppressing proliferation and inducing cell cycle arrest with apoptosis

BACKGROUND

Acute lymphoblastic leukemia is an aggressive neoplasm and seriously threatens human health. A14 is one kind of semisynthetic aurone that exhibits the capability to inhibit prostate cancer, but little is known about the role of A14 on T-cell acute lymphoblastic leukemia.

METHODS

Firstly, the effects of A14 on the ability of leukemia cells to proliferate were measured by Vi-cell counter. Then, we detected the cell cycle and apoptosis by flow cytometry and characterized the related protein expression using immunoblotting. In addition, we constructed stable luciferase expressing cell lines for use in a cell derived xenograft mouse model to measure the effect of A14 on T-cell acute lymphoblastic leukemia.

RESULTS

Results exhibited that A14 markedly suppressed cell proliferation and induced G2/M phase arrest along with cell cycles regulating proteins changes. A14 led to apoptosis in leukemia cells, at least partly, through the cytochrome c signaling pathway. Experiments in cell derived xenograft mouse model also showed that A14 markedly ameliorated the survival rate.

CONCLUSIONS

The present study revealed that semisynthetic aurones A14 can effectively protect against T-cell acute lymphoblastic leukemia progression both in vitro and in vivo, indicating the capability of A14 as a promising drug for the treatment of T-cell acute lymphoblastic leukemia.

A Model for the Proliferation–Quiescence Transition in Human Cells

The process of revitalising quiescent cells in order for them to proliferate plays a pivotal role in the repair of worn-out tissues as well as for tissue homeostasis. This process is also crucial in the growth, development and well-being of higher multi-cellular organisms such as mammals. Deregulation of proliferation-quiescence transition is related to many diseases, such as cancer. Recent studies have revealed that this proliferation–quiescence process is regulated tightly by the Rb−E2F bistable switch mechanism. Based on experimental observations, in this study, we formulate a mathematical model to examine the effect of the growth factor concentration on the proliferation–quiescence transition in human cells. Working with a non-dimensionalised model, we prove the positivity, boundedness and uniqueness of solutions. To understand model solution behaviour close to bifurcation points, we carry out bifurcation analysis, which is further illustrated by the use of numerical bifurcation analysis, sensitivity analysis and numerical simulations. Indeed, bifurcation and numerical analysis of the model predicted a transition between bistable and stable states, which are dependent on the growth factor concentration parameter (GF). The derived predictions confirm experimental observations.

The optimal strategy balancing risk and speed predicts DNA damage checkpoint override times

Checkpoints arrest biological processes allowing time for error correction. The phenomenon of checkpoint override (also known as checkpoint adaptation, slippage, or leakage), during cellular self-replication is biologically critical but currently lacks a quantitative, functional, or system-level understanding. To uncover fundamental laws governing error-correction systems, we derived a general theory of optimal checkpoint strategies, balancing the trade-off between risk and self-replication speed. Mathematically, the problem maps onto the optimization of an absorbing boundary for a random walk. We applied the theory to the DNA damage checkpoint (DDC) in budding yeast, an intensively researched model checkpoint. Using novel reporters for double-strand DNA breaks (DSBs), we first quantified the probability distribution of DSB repair in time including rare events and, secondly, the survival probability after override. With these inputs, the optimal theory predicted remarkably accurately override times as a function of DSB numbers, which we measured precisely for the first time. Thus, a first-principles calculation revealed undiscovered patterns underlying highly noisy override processes. Our multi-DSB measurements revise well-known past results and show that override is more general than previously thought.

Application of Lie Symmetry to a Mathematical Model that Describes a Cancer Sub-Network

In this paper, a mathematical model of a cancer sub-network is analysed from the view point of Lie symmetry methods. This model discusses a human cancer cell which is developed due to the dysfunction of some genes at the R-checkpoint during the cell cycle. The primary purpose of this paper is to apply the techniques of Lie symmetry to the model and present some approximated solutions for the three-dimensional system of first-order ordinary differential equations describing a cancer sub-network. The result shows that the phosphatase gene (Cdc25A) regulates the cyclin-dependent kinases inhibitor (P27Kip1). Furthermore, this research discovered that the activity that reverses the inhibitory effects on cell cycle progression at the R-checkpoint initiates a pathway.

Modeling Long ncRNA-Mediated Regulation in the Mammalian Cell Cycle

Long noncoding RNAs (lncRNAs) are transcripts longer than 200 nucleotides that are not translated into proteins. They have recently gained widespread attention due to the finding that tens of thousands of lncRNAs reside in the human genome, and due to an increasing number of lncRNAs that are found to be associated with disease. Some lncRNAs, including disease-associated ones, play different roles in regulating the cell cycle. Mathematical models of the cell cycle have been useful in better understanding this biological system, such as how it could be robust to some perturbations and how the cell cycle checkpoints could act as a switch. Here, we discuss mathematical modeling techniques for studying lncRNA regulation of the mammalian cell cycle. We present examples on how modeling via network analysis and differential equations can provide novel predictions toward understanding cell cycle regulation in response to perturbations such as DNA damage.

CYP enzymes, expressed within live human suspension cells, are superior to widely-used microsomal enzymes in identifying potent CYP1A1/CYP1B1 inhibitors: Identification of quinazolinones as CYP1A1/CYP1B1 inhibitors that efficiently reverse B[a]P toxicity and cisplatin resistance

Microsomal cytochrome P450 (CYP) enzymes, isolated from recombinant bacterial/insect/yeast cells, are extensively used for drug metabolism studies. However, they may not always portray how a developmental drug would behave in human cells with intact intracellular transport mechanisms. This study emphasizes the usefulness of human HEK293 kidney cells, grown in 'suspension' for expression of CYPs, in finding potent CYP1A1/CYP1B1 inhibitors, as possible anticancer agents. With live cell-based assays, quinazolinones 9i/9b were found to be selective CYP1A1/CYP1B1 inhibitors with IC₅₀ values of 30/21 nM, and > 150-fold selectivity over CYP2/3 enzymes, whereas they were far less active using commercially-available CYP1A1/CYP1B1 microsomal enzymes (IC₅₀, >10/1.3-1.7 μM). Compound 9i prevented CYP1A1-mediated benzo[a]pyrene-toxicity in normal fibroblasts whereas 9b completely reversed cisplatin resistance in PC-3/prostate, COR-L23/lung, MIAPaCa-2/pancreatic and LS174T/colon cancer cells, underlining the human-cell-assays' potential. Our results indicate that the most potent CYP1A1/CYP1B1 inhibitors would not have been identified if one had relied merely on microsomal enzymes.

Quantitative imaging with Fucci and mathematics to uncover temporal dynamics of cell cycle progression

Cell cycle progression is strictly coordinated to ensure proper tissue growth, development, and regeneration of multicellular organisms. Spatiotemporal visualization of cell cycle phases directly helps us to obtain a deeper understanding of controlled, multicellular, cell cycle progression. The fluorescent ubiquitination-based cell cycle indicator (Fucci) system allows us to monitor, in living cells, the G1 and the S/G2/M phases of the cell cycle in red and green fluorescent colors, respectively. Since the discovery of Fucci technology, it has found numerous applications in the characterization of the timing of cell cycle phase transitions under diverse conditions and various biological processes. However, due to the complexity of cell cycle dynamics, understanding of specific patterns of cell cycle progression is still far from complete. In order to tackle this issue, quantitative approaches combined with mathematical modeling seem to be essential. Here, we review several studies that attempted to integrate Fucci technology and mathematical models to obtain quantitative information regarding cell cycle regulatory patterns. Focusing on the technological development of utilizing mathematics to retrieve meaningful information from the Fucci producing data, we discuss how the combined methods advance a quantitative understanding of cell cycle regulation.

Dynamics of the mammalian cell cycle in physiological and pathological conditions

A network of cyclin-dependent kinases (Cdks) controls progression along the successive phases G1, S, G2, and M of the mammalian cell cycle. Deregulations in the expression of molecular components in this network often lead to abusive cell proliferation and cancer. Given the complex nature of the Cdk network, it is fruitful to resort to computational models to grasp its dynamical properties. Investigated by means of bifurcation diagrams, a detailed computational model for the Cdk network shows how the balance between quiescence and proliferation is affected by activators (oncogenes) and inhibitors (tumor suppressors) of cell cycle progression, as well as by growth factors and other external factors such as the extracellular matrix (ECM) and cell contact inhibition. Suprathreshold changes in all these factors can trigger a switch in the dynamical behavior of the network corresponding to a bifurcation between a stable steady state, associated with cell cycle arrest, and sustained oscillations of the various cyclin/Cdk complexes, corresponding to cell proliferation. The model for the Cdk network accounts for the dependence or independence of cell proliferation on serum and/or cell anchorage to the ECM. Such computational approach provides an integrated view of the control of cell proliferation in physiological or pathological conditions. Whether the balance is tilted toward cell cycle arrest or cell proliferation depends on the direction in which the threshold associated with the bifurcation is passed once the cell integrates the multiple signals, internal or external to the Cdk network, that promote or impede progression in the cell cycle.

Bifurcation in Cell Cycle Dynamics Regulated by p53

We study the regulating mechanism of p53 on the properties of cell cycle dynamics in the light of the proposed model of interacting p53 and cell cycle networks via p53. Irradiation (IR) introduce to p53 compel p53 dynamics to suffer different phases, namely oscillating and oscillation death (stabilized) phases. The IR induced p53 dynamics undergo collapse of oscillation with collapse time Δt which depends on IR strength. The stress p53 via IR drive cell cycle molecular species MPF and cyclin dynamics to different states, namely, oscillation death, oscillations of periods, chaotic and sustain oscillation in their bifurcation diagram. We predict that there could be a critical Δtc induced by p53 via IRc, where, if Δt〈Δtc the cell cycle may come back to normal state, otherwise it will go to cell cycle arrest (apoptosis).

Bistability, oscillations, and traveling waves in frog egg extracts

Mathematical modeling is a powerful tool for unraveling the complexities of the molecular regulatory networks underlying all aspects of cell physiology. To support this claim, we review our experiences modeling the cyclin-dependent kinase (CDK) network that controls events of the eukaryotic cell cycle. The model was derived from classic experiments on the biochemistry and molecular genetics of CDKs and their partner proteins. Because the dynamical properties of CDK activity depend in large part on positive and negative feedback loops in the regulatory network, it is difficult to predict its behavior by intuitive reasoning alone. Mathematical modeling is the correct tool for reliably determining the properties of the network in comparison with observed properties of dividing cells and for predicting the behavior of the control system under novel conditions. In this review, we describe six unexpected predictions of our 1993 model of the CDK control system in frog egg extracts and the remarkable experiments, performed much later, that verified all six predictions. The dynamical properties of the CDK network are consequences of feedback signals and ultrasensitive responses of regulatory proteins to CDK activity, and we describe the experimental evidence for the predicted ultrasensitivity. This case study illustrates the novel insights that mathematical modeling, analysis, and simulation can provide cell physiologists, and it points the way to a new "dynamical perspective" on molecular cell biology.

A data-driven, mathematical model of mammalian cell cycle regulation

Few of >150 published cell cycle modeling efforts use significant levels of data for tuning and validation. This reflects the difficultly to generate correlated quantitative data, and it points out a critical uncertainty in modeling efforts. To develop a data-driven model of cell cycle regulation, we used contiguous, dynamic measurements over two time scales (minutes and hours) calculated from static multiparametric cytometry data. The approach provided expression profiles of cyclin A2, cyclin B1, and phospho-S10-histone H3. The model was built by integrating and modifying two previously published models such that the model outputs for cyclins A and B fit cyclin expression measurements and the activation of B cyclin/Cdk1 coincided with phosphorylation of histone H3. The model depends on Cdh1-regulated cyclin degradation during G1, regulation of B cyclin/Cdk1 activity by cyclin A/Cdk via Wee1, and transcriptional control of the mitotic cyclins that reflects some of the current literature. We introduced autocatalytic transcription of E2F, E2F regulated transcription of cyclin B, Cdc20/Cdh1 mediated E2F degradation, enhanced transcription of mitotic cyclins during late S/early G2 phase, and the sustained synthesis of cyclin B during mitosis. These features produced a model with good correlation between state variable output and real measurements. Since the method of data generation is extensible, this model can be continually modified based on new correlated, quantitative data.

Minimal models for cell-cycle control based on competitive inhibition and multisite phosphorylations of Cdk substrates

The eukaryotic cell cycle is characterized by alternating oscillations in the activities of cyclin-dependent kinase (Cdk) and the anaphase-promoting complex (APC). Successful completion of the cell cycle is dependent on the precise, temporally ordered appearance of these activities. A modest level of Cdk activity is sufficient to initiate DNA replication, but mitosis and APC activation require an elevated Cdk activity. In present-day eukaryotes, this temporal order is provided by a complex network of regulatory proteins that control both Cdk and APC activities via sharp thresholds, bistability, and time delays. Using simple computational models, we show here that these dynamical features of cell-cycle organization could emerge in a control system driven by a single Cdk/cyclin complex and APC wired in a negative-feedback loop. We show that ordered phosphorylation of cellular proteins could be explained by multisite phosphorylation/dephosphorylation and competition of substrates for interconverting kinase (Cdk) and phosphatase. In addition, the competition of APC substrates for ubiquitylation can create and maintain sustained oscillations in cyclin levels. We propose a sequence of models that gets closer and closer to a realistic model of cell-cycle control in yeast. Since these models lack the elaborate control mechanisms characteristic of modern eukaryotes, they suggest that bistability and time delay may have characterized eukaryotic cell divisions before the current cell-cycle control network evolved in all its complexity.

A predictive mathematical model of the DNA damage G2 checkpoint

A predictive mathematical model of the transition from the G2 phase in the cell cycle to mitosis (M) was constructed from the known interactions of the proteins that are thought to play significant roles in the G2 to M transition as well as the DNA damage- induced G2 checkpoint. The model simulates the accumulation of active cyclin B1/Cdk1 (MPF) complexes in the nucleus to activate mitosis, the inhibition of this process by DNA damage, and transport of component proteins between cytoplasm and nucleus. Interactions in the model are based on activities of individual phospho-epitopes and binding sites of proteins involved in G2/M. Because tracking phosphoforms leads to combinatorial explosion, we employ a rule-based approach using the BioNetGen software. The model was used to determine the effects of depletion or over-expression of selected proteins involved in the regulation of the G2 to M transition in the presence and absence of DNA damage. Depletion of Plk1 delayed mitotic entry and recovery from the DNA damage-induced G2 arrest and over-expression of MPF attenuated the DNA damage-induced G2 delay. The model recapitulates the G2 delay observed in the biological response to varying levels of a DNA damage signal. The model produced the novel prediction that depletion of pkMyt1 results in an abnormal biological state in which G2 cells with DNA damage accumulate inactive nuclear MPF. Such a detailed model may prove useful for predicting DNA damage G2 checkpoint function in cancer and, therefore, sensitivity to cancer therapy.

Pharmacodynamic evaluation of irinotecan therapy by FDG and FLT PET/CT imaging in a colorectal cancer xenograft model

PURPOSE

Longitudinal changes of 3'-[(18) F]fluoro-3'-deoxythymidine (FLT) and 2-deoxy-2-[(18) F]fluoro-D-glucose (FDG) in response to irinotecan therapy in an animal model of colorectal cancer were compared.

PROCEDURES

SCID/CB-17 mice with HCT116 tumors were treated with 50 mg/kg irinotecan by intraperitoneal injection weekly for 3 weeks. FLT and FDG-positron emission tomography (PET) were performed at baseline, the day after each treatment, and 5 days after the first treatment. Proliferation and apoptosis were evaluated by immunohistochemistry (IHC) after day 15 of imaging.

RESULTS

Irinotecan treatment resulted in a suppression of tumor growth. Tumor FLT uptake was decreased the day after each treatment but to a lesser extent 5 days after the first treatment. FDG uptake increased the day after each treatment with a continuous increase throughout the experiment. IHC analysis of phospho-H3 and Ki67 confirmed FLT-PET results, indicating a decrease in proliferation the day after the final irinotecan treatment. Increased apoptosis monitored by caspase-3 was observed after day 15 with irinotecan treatment.

CONCLUSIONS

FLT-PET may be a better method than FDG-PET for assessing treatment response to irinotecan. Changes in imaging occur before changes in tumor volume.

Mathematical modeling of fission yeast Schizosaccharomyces pombe cell cycle: exploring the role of multiple phosphatases

Cell cycle is the central process that regulates growth and division in all eukaryotes. Based on the environmental condition sensed, the cell lies in a resting phase G0 or proceeds through the cyclic cell division process (G1→S→G2→M). These series of events and phase transitions are governed mainly by the highly conserved Cyclin dependent kinases (Cdks) and its positive and negative regulators. The cell cycle regulation of fission yeast Schizosaccharomyces pombe is modeled in this study. The study exploits a detailed molecular interaction map compiled based on the published model and experimental data. There are accumulating evidences about the prominent regulatory role of specific phosphatases in cell cycle regulations. The current study emphasizes the possible role of multiple phosphatases that governs the cell cycle regulation in fission yeast S. pombe. The ability of the model to reproduce the reported regulatory profile for the wild-type and various mutants was verified though simulations.

ELECTRONIC SUPPLEMENTARY MATERIAL

The online version of this article (doi:10.1007/s11693-011-9090-7) contains supplementary material, which is available to authorized users.

From quiescence to proliferation: Cdk oscillations drive the mammalian cell cycle

We recently proposed a detailed model describing the dynamics of the network of cyclin-dependent kinases (Cdks) driving the mammalian cell cycle (Gérard and Goldbeter, 2009). The model contains four modules, each centered around one cyclin/Cdk complex. Cyclin D/Cdk4–6 and cyclin E/Cdk2 promote progression in G1 and elicit the G1/S transition, respectively; cyclin A/Cdk2 ensures progression in S and the transition S/G2, while the activity of cyclin B/Cdk1 brings about the G2/M transition. This model shows that in the presence of sufficient amounts of growth factor the Cdk network is capable of temporal self-organization in the form of sustained oscillations, which correspond to the ordered, sequential activation of the various cyclin/Cdk complexes that control the successive phases of the cell cycle. The results suggest that the switch from cellular quiescence to cell proliferation corresponds to the transition from a stable steady state to sustained oscillations in the Cdk network. The transition depends on a finely tuned balance between factors that promote or hinder progression in the cell cycle. We show that the transition from quiescence to proliferation can occur in multiple ways that alter this balance. By resorting to bifurcation diagrams, we analyze the mechanism of oscillations in the Cdk network. Finally, we show that the complexity of the detailed model can be greatly reduced, without losing its key dynamical properties, by considering a skeleton model for the Cdk network. Using such a skeleton model for the mammalian cell cycle we show that positive feedback (PF) loops enhance the amplitude and the robustness of Cdk oscillations with respect to molecular noise. We compare the relative merits of the detailed and skeleton versions of the model for the Cdk network driving the mammalian cell cycle.

An investigation of spatial signal transduction in cellular networks

Background

Spatial signal transduction plays a vital role in many intracellular processes such as eukaryotic chemotaxis, polarity generation and cell division. Furthermore it is being increasingly realized that the spatial dimension to signalling may play an important role in other apparently purely temporal signal transduction processes. It is increasingly being recognized that a conceptual basis for studying spatial signal transduction in signalling networks is necessary.

Results

In this work we examine spatial signal transduction in a series of standard motifs/networks. These networks include coherent and incoherent feedforward, positive and negative feedback, cyclic motifs, monostable switches, bistable switches and negative feedback oscillators. In all these cases, the driving signal has spatial variation. For each network we consider two cases, one where all elements are essentially non-diffusible, and the other where one of the network elements may be highly diffusible. A careful analysis of steady state signal transduction provides many insights into the behaviour of all these modules. While in the non-diffusible case for the most part, spatial signalling reflects the temporal signalling behaviour, in the diffusible cases, we see significant differences between spatial and temporal signalling characteristics. Our results demonstrate that the presence of diffusible elements in the networks provides important constraints and capabilities for signalling.

Conclusions

Our results provide a systematic basis for understanding spatial signalling in networks and the role of diffusible elements therein. This provides many insights into the signal transduction capabilities and constraints in such networks and suggests ways in which cellular signalling and information processing is organized to conform to or bypass those constraints. It also provides a framework for starting to understand the organization and regulation of spatial signal transduction in individual processes.

Quercetin-3-methyl ether inhibits lapatinib-sensitive and -resistant breast cancer cell growth by inducing G(2)/M arrest and apoptosis

Lapatinib, an oral, small-molecule, reversible inhibitor of both EGFR and HER2, is highly active in HER2 positive breast cancer as a single agent and in combination with other therapeutics. However, resistance against lapatinib is an unresolved problem in clinical oncology. Recently, interest in the use of natural compounds to prevent or treat cancers has gained increasing interest because of presumed low toxicity. Quercetin-3-methyl ether, a naturally occurring compound present in various plants, has potent anticancer activity. Here, we found that quercetin-3-methyl ether caused a significant growth inhibition of lapatinib-sensitive and -resistant breast cancer cells. Western blot data showed that quercetin-3-methyl ether had no effect on Akt or ERKs signaling in resistant cells. However, quercetin-3-methyl ether caused a pronounced G(2)/M block mainly through the Chk1-Cdc25c-cyclin B1/Cdk1 pathway in lapatinib-sensitive and -resistant cells. In contrast, lapatinib produced an accumulation of cells in the G(1) phase mediated through cyclin D1, but only in lapatinib-sensitive cells. Moreover, quercetin-3-methyl ether induced significant apoptosis, accompanied with increased levels of cleaved caspase 3, caspase 7, and poly(ADP-ribose) polymerase (PARP) in both cell lines. Overall, these results suggested that quercetin-3-methyl ether might be a novel and promising therapeutic agent in lapatinib-sensitive or -resistant breast cancer patients.

Computational modeling of the cell cycle

This Teaching Resource provides lecture notes, slides, and a problem set for introducing graduate-level students to computational biology through a simple mathematical model of the cell cycle. The model simulates interactions between cyclin B and cyclin-dependent kinase 1, proteins that together form the mitosis-promoting factor (MPF), which initiates the processes leading to mitosis. The lecture begins with a biological background describing the importance of MPF for mitosis, the components of MPF, and the changes in cellular MPF observed during different phases of the cell cycle. The model is compared with newer, more mechanistically detailed models of the same process, which allows for a discussion of the insights that can be gained even from simplified models. The lecture concludes with a demonstration of how this model can be implemented in the scientific programming language MATLAB and includes a problem set.

Interoperability of time series cytometric data: a cross platform approach for modeling tumor heterogeneity

The cell cycle, with its highly conserved features, is a fundamental driver for the temporal control of cell proliferation-while abnormal control and modulation of the cell cycle are characteristic of tumor cells. The principal aim in cancer biology is to seek an understanding of the origin and nature of innate and acquired heterogeneity at the cellular level, driven principally by temporal and functional asynchrony. A major bottleneck when mathematically modeling these biological systems is the lack of interlinked structured experimental data. This often results in the in silico models failing to translate the specific hypothesis into parameterized terms that enable robust validation and hence would produce suitable prediction tools rather than just simulation tools. The focus has been on linking data originating from different cytometric platforms and cell-based event analysis to inform and constrain the input parameters of a compartmental cell cycle model, hence partly measuring and deconvolving cell cycle heterogeneity within a tumor population. Our work has addressed the concept that the interoperability of cytometric data, derived from different cytometry platforms, can complement as well as enhance cellular parameters space, thus providing a more broader and in-depth view of the cellular systems. The initial aim was to enable the cell cycle model to deliver an improved integrated simulation of the well-defined and constrained biological system. From a modeling perspective, such a cross platform approach has provided a paradigm shift from conventional cross-validation approaches, and from a bioinformatics perspective, novel computational methodology has been introduced for integrating and mapping continuous data with cross-sectional data. This establishes the foundation for developing predictive models and in silico tracking and prediction of tumor progression

A skeleton model for the network of cyclin-dependent kinases driving the mammalian cell cycle

We previously proposed a detailed, 39-variable model for the network of cyclin-dependent kinases (Cdks) that controls progression along the successive phases of the mammalian cell cycle. Here, we propose a skeleton, 5-variable model for the Cdk network that can be seen as the backbone of the more detailed model for the mammalian cell cycle. In the presence of sufficient amounts of growth factor, the skeleton model also passes from a stable steady state to sustained oscillations of the various cyclin/Cdk complexes. This transition corresponds to the switch from quiescence to cell proliferation. Sequential activation of the cyclin/Cdk complexes allows the ordered progression along the G1, S, G2 and M phases of the cell cycle. The 5-variable model can also account for the existence of a restriction point in G1, and for endoreplication. Like the detailed model, it contains multiple oscillatory circuits and can display complex oscillatory behaviour such as quasi-periodic oscillations and chaos. We compare the dynamical properties of the skeleton model with those of the more detailed model for the mammalian cell cycle.

XJW20, a novel oxoindole derivative, induces G2/M arrest and apoptosis selectively in K562 leukemia cell line

In comparison with four tumor cell lines and three non transformed cell types, chronic myeloid leukemia K562 cells were selectively sensitive to proliferation inhibition by the oxoindole derivative XJW20, as determined by the MTT assay. Further investigation revealed that XJW20 selectively induced G2/M arrest and apoptosis in K562 cells. At the molecular level, XJW20-induced G2/M arrest was accompanied by up-regulation of cyclin B1 and phospho (p)-Cdc25C (Ser216) and down-regulation of CDK1. There is no change in the expression of CDK2. The increased apoptotic activity by XJW20 was characterized by an increase in reactive oxygen species (ROS) generation, the mitochondrial transmembrane potential (DeltaPsi(m)) dissipation, cytochrome C releasing, apoptotic nuclei (AO/EB double staining) and nuclei condensation (DAPI-staining). The down-regulation of phosphorylated ERK was also found in XJW20-treated K562 cells. These molecular events induced by XJW20 may provide insight into the mechanism of action that led to growth arrest and apoptosis.

Computational systems biology of the cell cycle

One of the early success stories of computational systems biology was the work done on cell-cycle regulation. The earliest mathematical descriptions of cell-cycle control evolved into very complex, detailed computational models that describe the regulation of cell division in many different cell types. On the way these models predicted several dynamical properties and unknown components of the system that were later experimentally verified/identified. Still, research on this field is far from over. We need to understand how the core cell-cycle machinery is controlled by internal and external signals, also in yeast cells and in the more complex regulatory networks of higher eukaryotes. Furthermore, there are many computational challenges what we face as new types of data appear thanks to continuing advances in experimental techniques. We have to deal with cell-to-cell variations, revealed by single cell measurements, as well as the tremendous amount of data flowing from high throughput machines. We need new computational concepts and tools to handle these data and develop more detailed, more precise models of cell-cycle regulation in various organisms. Here we review past and present of computational modeling of cell-cycle regulation, and discuss possible future directions of the field.

HIV-1 expression induces tubular cell G2/M arrest and apoptosis

Human renal biopsy studies suggest the presence of HIV-1 and associated signs of injury in renal tubular epithelial cells. Because renal epithelial cells lack conventional HIV-1 receptors, the modus operandi of HIV-1 in the induction of tubular cell injury remains a mystery. In the present study, we evaluated the role of HIV-1 gene expression in human proximal tubular cell apoptosis and cell cycle progression. HIV-1- or vector-transduced cells were assayed for cellular injury and cell cycle defect. HIV-1-transduced cells showed the progressive loss of viability in a time-dependent manner. Similarly, HIV-1-transduced cells showed greater apoptosis when compared with vector-transduced cells. A higher number of HIV-1 expressing cells showed cell cycle arrest at G2/M phase and enhanced tubular cell expression of phospho-p53(ser15), phospho-cdc-2(Tyr 15), and phospho-chk-2 (Thr 68). These findings suggest that in addition to the activation of apoptotic pathway, HIV-1-induced G2/M arrest may also contribute to tubular cell injury.

A test of highly optimized tolerance reveals fragile cell-cycle mechanisms are molecular targets in clinical cancer trials

Robustness, a long-recognized property of living systems, allows function in the face of uncertainty while fragility, i.e., extreme sensitivity, can potentially lead to catastrophic failure following seemingly innocuous perturbations. Carlson and Doyle hypothesized that highly-evolved networks, e.g., those involved in cell-cycle regulation, can be resistant to some perturbations while highly sensitive to others. The “robust yet fragile” duality of networks has been termed Highly Optimized Tolerance (HOT) and has been the basis of new lines of inquiry in computational and experimental biology. In this study, we tested the working hypothesis that cell-cycle control architectures obey the HOT paradigm. Three cell-cycle models were analyzed using monte-carlo sensitivity analysis. Overall state sensitivity coefficients, which quantify the robustness or fragility of a given mechanism, were calculated using a monte-carlo strategy with three different numerical techniques along with multiple parameter perturbation strategies to control for possible numerical and sampling artifacts. Approximately 65% of the mechanisms in the G1/S restriction point were responsible for 95% of the sensitivity, conversely, the G2-DNA damage checkpoint showed a much stronger dependence on a few mechanisms; ∼32% or 13 of 40 mechanisms accounted for 95% of the sensitivity. Our analysis predicted that CDC25 and cyclin E mechanisms were strongly implicated in G1/S malfunctions, while fragility in the G2/M checkpoint was predicted to be associated with the regulation of the cyclin B-CDK1 complex. Analysis of a third model containing both G1/S and G2/M checkpoint logic, predicted in addition to mechanisms already mentioned, that translation and programmed proteolysis were also key fragile subsystems. Comparison of the predicted fragile mechanisms with literature and current preclinical and clinical trials suggested a strong correlation between efficacy and fragility. Thus, when taken together, these results support the working hypothesis that cell-cycle control architectures are HOT networks and establish the mathematical estimation and subsequent therapeutic exploitation of fragile mechanisms as a novel strategy for anti-cancer lead generation.

Computer-aided drug design: the next 20 years

This perspectives article has been taken from a talk the author gave at the symposium in honor of Yvonne C. Martin's retirement, held at the American Chemical Society spring meeting in Chicago on March 25, 2007. The talk was intended as a somewhat lighthearted attempt to gaze into the future; inevitably, in print, things will come across more seriously than was intended. As we all know-the past is rarely predictive of the future.

DNA-damage response network at the crossroads of cell-cycle checkpoints, cellular senescence and apoptosis

Tissue homeostasis requires a carefully-orchestrated balance between cell proliferation, cellular senescence and cell death. Cells proliferate through a cell cycle that is tightly regulated by cyclin-dependent kinase activities. Cellular senescence is a safeguard program limiting the proliferative competence of cells in living organisms. Apoptosis eliminates unwanted cells by the coordinated activity of gene products that regulate and effect cell death. The intimate link between the cell cycle, cellular senescence, apoptosis regulation, cancer development and tumor responses to cancer treatment has become eminently apparent. Extensive research on tumor suppressor genes, oncogenes, the cell cycle and apoptosis regulatory genes has revealed how the DNA damage-sensing and -signaling pathways, referred to as the DNA-damage response network, are tied to cell proliferation, cell-cycle arrest, cellular senescence and apoptosis. DNA-damage responses are complex, involving "sensor" proteins that sense the damage, and transmit signals to "transducer" proteins, which, in turn, convey the signals to numerous "effector" proteins implicated in specific cellular pathways, including DNA repair mechanisms, cell-cycle checkpoints, cellular senescence and apoptosis. The Bcl-2 family of proteins stands among the most crucial regulators of apoptosis and performs vital functions in deciding whether a cell will live or die after cancer chemotherapy and irradiation. In addition, several studies have now revealed that members of the Bcl-2 family also interface with the cell cycle, DNA repair/recombination and cellular senescence, effects that are generally distinct from their function in apoptosis. In this review, we report progress in understanding the molecular networks that regulate cell-cycle checkpoints, cellular senescence and apoptosis after DNA damage, and discuss the influence of some Bcl-2 family members on cell-cycle checkpoint regulation.

Mathematical modeling as a tool for investigating cell cycle control networks

Although not a traditional experimental "method," mathematical modeling can provide a powerful approach for investigating complex cell signaling networks, such as those that regulate the eukaryotic cell division cycle. We describe here one modeling approach based on expressing the rates of biochemical reactions in terms of nonlinear ordinary differential equations. We discuss the steps and challenges in assigning numerical values to model parameters and the importance of experimental testing of a mathematical model. We illustrate this approach throughout with the simple and well-characterized example of mitotic cell cycles in frog egg extracts. To facilitate new modeling efforts, we describe several publicly available modeling environments, each with a collection of integrated programs for mathematical modeling. This review is intended to justify the place of mathematical modeling as a standard method for studying molecular regulatory networks and to guide the non-expert to initiate modeling projects in order to gain a systems-level perspective for complex control systems.

Dynamics of biological systems: role of systems biology in medical research

Cellular systems are networks of interacting components that change with time in response to external and internal events. Studying the dynamic behavior of these networks is the basis for an understanding of cellular functions and disease mechanisms. Quantitative time-series data leading to meaningful models can improve our knowledge of human physiology in health and disease, and aid the search for earlier diagnoses, better therapies and a healthier life. The advent of systems biology is about to take the leap into clinical research and medical applications. This review emphasizes the importance of a dynamic view and understanding of cell function. We discuss the potential for computer-aided mathematical modeling of biological systems in medical research with examples from some of the major therapeutic areas: cancer, cardiovascular, diabetic and neurodegenerative medicine.

Analysis of a generic model of eukaryotic cell-cycle regulation

We propose a protein interaction network for the regulation of DNA synthesis and mitosis that emphasizes the universality of the regulatory system among eukaryotic cells. The idiosyncrasies of cell cycle regulation in particular organisms can be attributed, we claim, to specific settings of rate constants in the dynamic network of chemical reactions. The values of these rate constants are determined ultimately by the genetic makeup of an organism. To support these claims, we convert the reaction mechanism into a set of governing kinetic equations and provide parameter values (specific to budding yeast, fission yeast, frog eggs, and mammalian cells) that account for many curious features of cell cycle regulation in these organisms. Using one-parameter bifurcation diagrams, we show how overall cell growth drives progression through the cell cycle, how cell-size homeostasis can be achieved by two different strategies, and how mutations remodel bifurcation diagrams and create unusual cell-division phenotypes. The relation between gene dosage and phenotype can be summarized compactly in two-parameter bifurcation diagrams. Our approach provides a theoretical framework in which to understand both the universality and particularity of cell cycle regulation, and to construct, in modular fashion, increasingly complex models of the networks controlling cell growth and division.

Linking cell division to cell growth in a spatiotemporal model of the cell cycle

Cell division must be tightly coupled to cell growth in order to maintain cell size, yet the mechanisms linking these two processes are unclear. It is known that almost all proteins involved in cell division shuttle between cytoplasm and nucleus during the cell cycle; however, the implications of this process for cell cycle dynamics and its coupling to cell growth remains to be elucidated. We developed mathematical models of the cell cycle which incorporate protein translocation between cytoplasm and nucleus. We show that protein translocation between cytoplasm and nucleus not only modulates temporal cell cycle dynamics, but also provides a natural mechanism coupling cell division to cell growth. This coupling is mediated by the effect of cytoplasmic-to-nuclear size ratio on the activation threshold of critical cell cycle proteins, leading to the size-sensing checkpoint (sizer) and the size-independent clock (timer) observed in many cell cycle experiments.

Gene copy number and cell cycle arrest

The cell cycle is an orderly sequence of events which ultimately lead to the division of a single cell into two daughter cells. In the case of DNA damage by radiation or chemicals, the damage checkpoints in the G1 and G2 phases of the cell cycle are activated. This results in an arrest of the cell cycle so that the DNA damage can be repaired. Once this is done, the cell continues with its usual cycle of activity. We study a mathematical model of the DNA damage checkpoint in the G2 phase which arrests the transition from the G2 to the M (mitotic) phase of the cell cycle. The tumor suppressor protein p53 plays a key role in activating the pathways leading to cell cycle arrest in mammalian systems. If the DNA damage is severe, the p53 proteins activate other pathways which bring about apoptosis, i.e., programmed cell death. Loss of the p53 gene results in the proliferation of cells containing damaged DNA, i.e., in the growth of tumors which may ultimately become cancerous. There is some recent experimental evidence which suggests that the mutation of a single copy of the p53 gene (in the normal cell each gene has two identical copies) is sufficient to trigger the formation of tumors. We study the effect of reducing the gene copy number of the p53 and two other genes on cell cycle arrest and obtain results consistent with experimental observations.

Inactivated MGMT by O6-benzylguanine is associated with prolonged G2/M arrest in cancer cells treated with BCNU

HCT116 and HCT15 cells that highly express O(6)-methylguanine-DNA-methyltransferase (MGMT) displayed a transient cell cycle G2/M arrest in response to exposure to 1,3-bis(2-chloroethyl)-1-nitrosourea (BCNU) alone; however, 70-80% of cells were arrested in G2/M after treatment with O(6)-benzylguanine (BG) and BCNU. Cells accumulated in G2/M showed elevated levels of an inactive form of cyclin B1/p-Cdc2 (Tyr15) complex that was not associated with activation of Chk1/p-Cdc25C and was independent of p53/p21 status. The most prominent feature of cell death was the appearance of enlarged and multinucleated cells that was related to the inhibition of mitotic entry. In contrast, BG-resistant cell lines, HCT116 BBR and HCT15 BBR cells that contain mutations K165E and K165N of MGMT, respectively, displayed a normal cell cycle progression with a slight and transient increase in G2/M arrest at 24 h after treatments with either BCNU alone or BG combined with BCNU. The differences in the ability to progress toward G2/M after treatment with BG and BCNU between cells expressing wild-type MGMT and mutated MGMT were confirmed in CHO cells transfected with human wild type and K165E mutant MGMT cDNA, respectively. Thus, our findings suggest that BG-inactivated MGMT may be linked to cell signaling events, forcing cells into a permanent G2/M arrest in response to the DNA damages induced by BCNU.

Bifurcation analysis of a model of the budding yeast cell cycle

We study the bifurcations of a set of nine nonlinear ordinary differential equations that describe regulation of the cyclin-dependent kinase that triggers DNA synthesis and mitosis in the budding yeast, Saccharomyces cerevisiae. We show that Clb2-dependent kinase exhibits bistability (stable steady states of high or low kinase activity). The transition from low to high Clb2-dependent kinase activity is driven by transient activation of Cln2-dependent kinase, and the reverse transition is driven by transient activation of the Clb2 degradation machinery. We show that a four-variable model retains the main features of the nine-variable model. In a three-variable model exhibiting birhythmicity (two stable oscillatory states), we explore possible effects of extrinsic fluctuations on cell cycle progression.

Dynamics of the cell cycle: checkpoints, sizers, and timers

We have developed a generic mathematical model of a cell cycle signaling network in higher eukaryotes that can be used to simulate both the G1/S and G2/M transitions. In our model, the positive feedback facilitated by CDC25 and wee1 causes bistability in cyclin-dependent kinase activity, whereas the negative feedback facilitated by SKP2 or anaphase-promoting-complex turns this bistable behavior into limit cycle behavior. The cell cycle checkpoint is a Hopf bifurcation point. These behaviors are coordinated by growth and division to maintain normal cell cycle and size homeostasis. This model successfully reproduces sizer, timer, and the restriction point features of the eukaryotic cell cycle, in addition to other experimental findings.

Cell cycle-dependent potentiation of cisplatin by UCN-01 in non-small-cell lung carcinoma

PURPOSE

We evaluated the combination of UCN-01 plus cisplatin and sought to determine how the cell cycle effects of each agent affected the combined response. Cisplatin-induced DNA damage results in cell cycle arrest, primarily at the S and G(2) checkpoints, providing the opportunity for DNA damage repair prior to mitosis. Thus, strategies to enhance cisplatin cytotoxicity include attenuation of DNA damage-induced checkpoints. The cyclin-dependent kinase inhibitor 7-hydroxystaurosporine (UCN-01) can potentiate cisplatin activity, likely via abrogation of the S and G(2) checkpoints. UCN-01 has additional effects on cell cycling, including induction of an RB-associated G(1) arrest.

METHODS

NSCLC cell lines A549 (wt p53, wt RB), Calu1 ( p53-null, wt RB) and H596 (mt p53, RB-null) were treated with UCN-01 and/or cisplatin with two-drug treatments delivered in alternate sequences. Effects of drug treatment on cell growth, cell cycling, apoptosis and levels and phosphorylation of cell cycle-associated proteins were evaluated. The interaction between the two drugs was assessed using median effect analysis.

RESULTS

When UCN-01 preceded cisplatin, growth inhibition was additive or less than additive, as assessed by median effect analysis. In contrast, when NSCLC cells were treated with cisplatin followed by UCN-01, the combination was synergistic. In this treatment sequence, a decrease in the proportion of cells at the G(2) checkpoint was confirmed by reduced expression of cyclins A and B and activation of Cdk1. Abrogation of the G(2) DNA damage checkpoint and apoptosis were prevalent only in cell populations treated with cisplatin followed by UCN-01 and was markedly enhanced in the cell lines with disrupted p53.

CONCLUSIONS

These studies indicate that timing of drug administration strongly influences response to cisplatin plus UCN-01 in NSCLC cells, and this is related to the cell cycle-modulatory effects of these agents. Furthermore, this sequence combination was more effective in cell lines with dysfunctional p53. These findings support the hypothesis that checkpoint abrogation is the major mechanism of UCN-01-mediated potentiation of cisplatin cytotoxicity.

Hysteresis drives cell-cycle transitions in Xenopus laevis egg extracts

Cells progressing through the cell cycle must commit irreversibly to mitosis without slipping back to interphase before properly segregating their chromosomes. A mathematical model of cell-cycle progression in cell-free egg extracts from frog predicts that irreversible transitions into and out of mitosis are driven by hysteresis in the molecular control system. Hysteresis refers to toggle-like switching behavior in a dynamical system. In the mathematical model, the toggle switch is created by positive feedback in the phosphorylation reactions controlling the activity of Cdc2, a protein kinase bound to its regulatory subunit, cyclin B. To determine whether hysteresis underlies entry into and exit from mitosis in cell-free egg extracts, we tested three predictions of the Novak-Tyson model. (i) The minimal concentration of cyclin B necessary to drive an interphase extract into mitosis is distinctly higher than the minimal concentration necessary to hold a mitotic extract in mitosis, evidence for hysteresis. (ii) Unreplicated DNA elevates the cyclin threshold for Cdc2 activation, indication that checkpoints operate by enlarging the hysteresis loop. (iii) A dramatic "slowing down" in the rate of Cdc2 activation is detected at concentrations of cyclin B marginally above the activation threshold. All three predictions were validated. These observations confirm hysteresis as the driving force for cell-cycle transitions into and out of mitosis.

Regulation of the mammalian cell cycle: a model of the G1-to-S transition

We have formulated a mathematical model for regulation of the G(1)-to-S transition of the mammalian cell cycle. This mathematical model incorporates the key molecules and interactions that have been identified experimentally. By subdividing these critical molecules into modules, we have been able to systematically analyze the contribution of each to dynamics of the G(1)-to-S transition. The primary module, which includes the interactions between cyclin E (CycE), cyclin-dependent kinase 2 (CDK2), and protein phosphatase CDC25A, exhibits dynamics such as limit cycle, bistability, and excitable transient. The positive feedback between CycE and transcription factor E2F causes bistability, provided that the total E2F is constant and the retinoblastoma protein (Rb) can be hyperphosphorylated. The positive feedback between active CDK2 and cyclin-dependent kinase inhibitor (CKI) generates a limit cycle. When combined with the primary module, the E2F/Rb and CKI modules potentiate or attenuate the dynamics generated by the primary module. In addition, we found that multisite phosphorylation of CDC25A, Rb, and CKI was critical for the generation of dynamics required for cell cycle progression.

Understanding complex signaling networks through models and metaphors

Signaling networks are complex both in terms of the chemical and biophysical events that underlie them, and in the sheer number of interactions. Computer models are powerful tools to deal with both aspects of complexity, but their utility goes beyond simply replicating signaling events in silicon. Their great advantage is as a tool to understanding. The completeness of the description demanded by computer models highlights gaps in knowledge. The quantitative description in models facilitates a mapping between different kinds of analysis methods for complex systems. Systems analysis methods can highlight stable states of signaling networks and describe the transitions between them. Modeling also reveals functional similarities between signaling network properties and other well-understood systems such as electronic devices and neural networks. These suggest various metaphors as a tool to understanding. Based on such descriptions, it is possible to regard signaling networks as systems that decode complex inputs in time, space and chemistry into combinatorial output patterns of signaling activity. This would provide a natural interface to the combinatorial input patterns required by genetic circuits. Thus, a combination of computer modeling methods to capture the complexity and details, and useful abstractions revealed by these models, is necessary to achieve both rigorous description as well as human understanding.

Nonequilibrium capillary electrophoresis of equilibrium mixtures--a single experiment reveals equilibrium and kinetic parameters of protein-DNA interactions

We introduce a novel electrophoretic method, nonequilibrium capillary electrophoresis of equilibrium mixtures (NECEEM), and demonstrate its use for studying protein-DNA interactions. The equilibrium mixture of protein and DNA contains three components: free protein, free DNA, and the protein-DNA complex. A short plug of such a mixture is injected into the capillary, and the three components are separated under nonequilibrium conditions. The resulting electropherograms are composed of characteristic peaks and exponential curves. An easy nonnumerical analysis of a single electropherogram reveals two parameters: the equilibrium binding constant and the monomolecular rate constant of complex decay. The bimolecular rate constant of complex formation can then be calculated as the product of the two experimentally determined constants. NECEEM was applied to study the interaction between single-stranded DNA binding protein and a fluorescently labeled 15-mer oligonucleotide. It allowed us to measure for the first time the rate constant of complex decay for this important protein-DNA pair, k-1 = 0.03 s-1. The value of the equilibrium binding constant, Kb = 3.6 x 10-6 M-1, was in good agreement with those measured by other methods. As low as 10-18 mol of the protein was sufficient for the measurements. Thus, the new method is simple, informative, and highly sensitive. Moreover, it can be equally applied to other noncovalent protein-ligand complexes. These features of NECEEM make this method an indispensable tool in studies of macromolecular interactions. They also emphasize the potential role of NECEEM in the development of extremely sensitive protein assays using nucleotide aptamers.

Fish oil slows S phase progression and may cause upstream shift of DHFR replication origin ori-beta in CHO cells

Fish oils (FOs) have been noted to reduce growth and proliferation of certain tumor cells, effects usually attributed to the content of polyunsaturated fatty acids of the n-3 family, which are thought to modulate cellular signaling pathways. We investigated the influence of FO on cell cycle kinetics of cultured Chinese hamster ovary cells. Exponentially growing cells were labeled with 5-bromo-2'-deoxyuridine (BrdU) and analyzed by flow cytometry after 5-day treatment with exogenous fat. Bivariate BrdU-DNA analysis indicated slower progression through S phase and thus longer S phase duration time in FO- but not corn oil-treated or control cells. We hypothesize that FO treatment might interfere with spatial/temporal organization of replication origins. Therefore, we mapped the well-characterized replication origin ori-beta downstream of the dihydrofolate reductase gene with the nascent strand length assay. Three DNA marker segments with known positions relative to this origin were amplified by PCR. By quantitatively assessing DNA length of the fragments in all fractions containing these markers, the location of ori-beta was established. In control or corn oil-treated cells, the location of ori-beta was consistent with previous studies. However, in FO-treated cells, DNA replication appears to start from a new site located farther upstream from ori-beta, suggesting a different replication initiation pattern. This study suggests novel mechanism(s) by which fats affect cell proliferation and DNA replication in mammalian cells.