A simple time delay model for eukaryotic cell cycle

Srividhya, Jeyaraman; Gopinathan, M. S.

doi:10.1016/j.jtbi.2005.12.020

Cited by 34 publications

(19 citation statements)

References 44 publications

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“…The large majority use ordinary differential equations (ODEs) based on chemical mass action assumptions. However, stochastic [40], time delay [41], partial differential equation models of spatiotemporal dynamics [42], Boolean logic [43], and hybrid models [44] have also been reported.…”

Section: Introductionmentioning

confidence: 99%

A Data-Driven, Mathematical Model of Mammalian Cell Cycle Regulation

et al. 2014

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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.

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Section: Introductionmentioning

confidence: 99%

A Data-Driven, Mathematical Model of Mammalian Cell Cycle Regulation

et al. 2014

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“…DDEs are used to reduce the number of variables involved and offer a mechanistic insight into regulatory mechanisms, when the entire process details are unknown. Using phosphorylation-dephosphorylation cycles, it has been shown that DDEs can capture observed system behaviour using fewer variables [27,28]. DDEs have been used to mechanistically model transcriptional effects in the feedback regulation of the NF-kB pathway [29] and the effects of pharmacological agents on this pathway [30].…”

Section: Delay Modelsmentioning

confidence: 99%

Modelling cellular signalling systems

Rangamani

Iyengar

2008

Essays in Biochemistry

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Cell signalling pathways and networks are complex and often non-linear. Signalling pathways can be represented as systems of biochemical reactions that can be modelled using differential equations. Computational modelling of cell signalling pathways is emerging as a tool that facilitates mechanistic understanding of complex biological systems. Mathematical models are also used to generate predictions that may be tested experimentally. In the present chapter, the various steps involved in building models of cell signalling pathways are discussed. Depending on the nature of the process being modelled and the scale of the model, different mathematical formulations, ranging from stochastic representations to ordinary and partial differential equations are discussed. This is followed by a brief summary of some recent modelling successes and the state of future models.

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“…These attempts to elucidate the general principles underlying the eukaryotic cell cycle have been reviewed in Ingolia and Murray (2004). Recent contributions include Csikász-Nagy et al (2006), Srividhya and Gopinathan (2006) and Yang et al (2006), the latter of which describes a spatiotemporal model. The data used to inform these models is collected from specific organisms, each of which employs unique elements in cell cycle regulation.…”

Section: Analysis and Interpretation Through Dynamic Modelingmentioning

confidence: 99%

Systems Level Modeling of the Cell Cycle Using Budding Yeast

et al. 2007

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Proteins involved in the regulation of the cell cycle are highly conserved across all eukaryotes, and so a relatively simple eukaryote such as yeast can provide insight into a variety of cell cycle perturbations including those that occur in human cancer. To date, the budding yeast Saccharomyces cerevisiae has provided the largest amount of experimental and modeling data on the progression of the cell cycle, making it a logical choice for in-depth studies of this process. Moreover, the advent of methods for collection of high-throughput genome, transcriptome, and proteome data has provided a means to collect and precisely quantify simultaneous cell cycle gene transcript and protein levels, permitting modeling of the cell cycle on the systems level. With the appropriate mathematical framework and sufficient and accurate data on cell cycle components, it should be possible to create a model of the cell cycle that not only effectively describes its operation, but can also predict responses to perturbations such as variation in protein levels and responses to external stimuli including targeted inhibition by drugs. In this review, we summarize existing data on the yeast cell cycle, proteomics technologies for quantifying cell cycle proteins, and the mathematical frameworks that can integrate this data into representative and effective models. Systems level modeling of the cell cycle will require the integration of high-quality data with the appropriate mathematical framework, which can currently be attained through the combination of dynamic modeling based on proteomics data and using yeast as a model organism.

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A simple time delay model for eukaryotic cell cycle

Cited by 34 publications

References 44 publications

A Data-Driven, Mathematical Model of Mammalian Cell Cycle Regulation

A Data-Driven, Mathematical Model of Mammalian Cell Cycle Regulation

Modelling cellular signalling systems

Systems Level Modeling of the Cell Cycle Using Budding Yeast

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