Transcriptional Regulation Is a Major Controller of Cell Cycle Transition Dynamics

Romanel, Alessandro; Jensen, Lars Juhl; Cardelli, Luca; Csikász‐Nagy, Attila

doi:10.1371/journal.pone.0029716

Cited by 29 publications

(18 citation statements)

References 68 publications

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“…In Fig. 8 we show that, with respect to the basal models (with unit rates), the same level of parameter variation 49 50 leads to smaller deviations in the more complex networks 49 50 . To simulate extrinsic noise, the reaction rates are randomly sampled from Gaussian distributions with mean 1 and standard deviation 0.5.…”

Section: Resultsmentioning

confidence: 87%

Noise Reduction in Complex Biological Switches

Cardelli

Csikász‐Nagy

Dalchau

et al. 2016

Sci Rep

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Cells operate in noisy molecular environments via complex regulatory networks. It is possible to understand how molecular counts are related to noise in specific networks, but it is not generally clear how noise relates to network complexity, because different levels of complexity also imply different overall number of molecules. For a fixed function, does increased network complexity reduce noise, beyond the mere increase of overall molecular counts? If so, complexity could provide an advantage counteracting the costs involved in maintaining larger networks. For that purpose, we investigate how noise affects multistable systems, where a small amount of noise could lead to very different outcomes; thus we turn to biochemical switches. Our method for comparing networks of different structure and complexity is to place them in conditions where they produce exactly the same deterministic function. We are then in a good position to compare their noise characteristics relatively to their identical deterministic traces. We show that more complex networks are better at coping with both intrinsic and extrinsic noise. Intrinsic noise tends to decrease with complexity, and extrinsic noise tends to have less impact. Our findings suggest a new role for increased complexity in biological networks, at parity of function.

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Section: Resultsmentioning

confidence: 87%

Noise Reduction in Complex Biological Switches

Cardelli

Csikász‐Nagy

Dalchau

et al. 2016

Sci Rep

Self Cite

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“…For this purpose, we present an algorithmic model that describes the intracellular metabolic network that involves gemcitabine and its main metabolites. We use continuous time discrete stochastic simulations, which provide an account of the random fluctuations due to the variability of the conditions in the biochemical environment [23]–[25]. This allows us to give an algorithmic interpretation [26] of the experimental data mechanistically with respect to the time series concentrations [7] and efficacy measurements [27].…”

Section: Introductionmentioning

confidence: 99%

“…The algorithmic representations of biological systems [26] are then amenable to computer execution, and the state changes of computer programs can be directly mapped to dynamic state changes of biological networks [24], [25], [29]. Language technology and compiler theory provide the means to build high-level abstractions, comprehensible to non expert modelers, to be mapped onto executable languages [30]–[32].…”

Section: Introductionmentioning

confidence: 99%

Algorithmic Modeling Quantifies the Complementary Contribution of Metabolic Inhibitions to Gemcitabine Efficacy

et al. 2012

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Gemcitabine (2,2-difluorodeoxycytidine, dFdC) is a prodrug widely used for treating various carcinomas. Gemcitabine exerts its clinical effect by depleting the deoxyribonucleotide pools, and incorporating its triphosphate metabolite (dFdC-TP) into DNA, thereby inhibiting DNA synthesis. This process blocks the cell cycle in the early S phase, eventually resulting in apoptosis. The incorporation of gemcitabine into DNA takes place in competition with the natural nucleoside dCTP. The mechanisms of indirect competition between these cascades for common resources are given with the race for DNA incorporation; in clinical studies dedicated to singling out mechanisms of resistance, ribonucleotide reductase (RR) and deoxycytidine kinase (dCK) and human equilibrative nucleoside transporter1 (hENT1) have been associated to efficacy of gemcitabine with respect to their roles in the synthesis cascades of dFdC-TP and dCTP. However, the direct competition, which manifests itself in terms of inhibitions between these cascades, remains to be quantified. We propose an algorithmic model of gemcitabine mechanism of action, verified with respect to independent experimental data. We performed in silico experiments in different virtual conditions, otherwise difficult in vivo, to evaluate the contribution of the inhibitory mechanisms to gemcitabine efficacy. In agreement with the experimental data, our model indicates that the inhibitions due to the association of dCTP with dCK and the association of gemcitabine diphosphate metabolite (dFdC-DP) with RR play a key role in adjusting the efficacy. While the former tunes the catalysis of the rate-limiting first phosphorylation of dFdC, the latter is responsible for depletion of dCTP pools, thereby contributing to gemcitabine efficacy with a dependency on nucleoside transport efficiency. Our simulations predict the existence of a continuum of non-efficacy to high-efficacy regimes, where the levels of dFdC-TP and dCTP are coupled in a complementary manner, which can explain the resistance to this drug in some patients.

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“…Cdc2 is a cyclin dependent kinase (Cdk) and can be active only in a complex with cyclins as, e.g., Cig1, Cig2, Puc1, or Cdc13. Such post translational control forms a central part of the fission yeast network [40].…”

Section: Methodsmentioning

confidence: 99%

Boolean Network Model Predicts Knockout Mutant Phenotypes of Fission Yeast

Davidich

Bornholdt

2013

PLoS ONE

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Boolean networks (or: networks of switches) are extremely simple mathematical models of biochemical signaling networks. Under certain circumstances, Boolean networks, despite their simplicity, are capable of predicting dynamical activation patterns of gene regulatory networks in living cells. For example, the temporal sequence of cell cycle activation patterns in yeasts S. pombe and S. cerevisiae are faithfully reproduced by Boolean network models. An interesting question is whether this simple model class could also predict a more complex cellular phenomenology as, for example, the cell cycle dynamics under various knockout mutants instead of the wild type dynamics, only. Here we show that a Boolean network model for the cell cycle control network of yeast S. pombe correctly predicts viability of a large number of known mutants. So far this had been left to the more detailed differential equation models of the biochemical kinetics of the yeast cell cycle network and was commonly thought to be out of reach for models as simplistic as Boolean networks. The new results support our vision that Boolean networks may complement other mathematical models in systems biology to a larger extent than expected so far, and may fill a gap where simplicity of the model and a preference for an overall dynamical blueprint of cellular regulation, instead of biochemical details, are in the focus.

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Transcriptional Regulation Is a Major Controller of Cell Cycle Transition Dynamics

Cited by 29 publications

References 68 publications

Noise Reduction in Complex Biological Switches

Noise Reduction in Complex Biological Switches

Algorithmic Modeling Quantifies the Complementary Contribution of Metabolic Inhibitions to Gemcitabine Efficacy

Boolean Network Model Predicts Knockout Mutant Phenotypes of Fission Yeast

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