Subcellular Localization of the Cyclin Dependent Kinase Inhibitor Sic1 is Modulated by the Carbon Source in Budding Yeast

Rossi, Riccardo L.; Zinzalla,; A, Mastriani; Vanoni, Marco; Alberghina, Lilia

doi:10.4161/cc.4.12.2189

Cited by 29 publications

(46 citation statements)

References 52 publications

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“…One possible candidate was the Cln2-Cdc28 inhibitor Far1, since the nuclear import of Clb5 cyclin depends on binding to its CDK inhibitor Sic1. 46 However, the nuclear localization of Cln2 was not altered in the far1 mutant strain (Fig. S6).…”

Section: Resultsmentioning

confidence: 99%

Molecular basis of the functional distinction between Cln1 and Cln2 cyclins

Quilis

Igual²

2012

Cell Cycle

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Section: Resultsmentioning

confidence: 99%

Molecular basis of the functional distinction between Cln1 and Cln2 cyclins

Quilis

Igual²

2012

Cell Cycle

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“…Similarly, nuclear localization was favored for the Cdk1-Cln1,2 binary complex. According to experimental data indicating that Sic1 promotes Clb5 nuclear localization [17], nuclear import of the ternary complex Cdk1-Clb5,6-Sic1 (k 47 ) was favored over transport of the cognate binary complex (k 48 ). Rate constants for nuclear export of CLN1,2 and CLB5,6 mRNAs (k 50 and k 51 , respectively) were set larger than those of corresponding proteins.…”

Section: Methodsmentioning

confidence: 99%

“…Sic1 is produced in the cytoplasm, where it binds to Cdk1-Clb5,6, favoring its nuclear import [17]. In G 1 cells growing in glucose-supplemented media, Sic1 is mostly nuclear, while in ethanol-grown cells a large amount of Sic1 remains cytoplasmic [17]. Sic1 parameters have been set to account for this differential localization (Tables 2-5) applying the known binding affinity of Sic1 to the cyclin-dependent kinase Cdk-cyclin complex ''in vitro'' [40,41] to simulation of glucose-grown cells, and estimating that for ethanol-grown cells as reported in Materials and Methods.…”

Section: Data Mining and Wiring For Molecular Events Of The G 1 To S mentioning

confidence: 99%

“…To this end, we present here a mathematical model of the G 1 to S transition that integrates the different regulatory links proposed so far. The model takes into consideration data from the literature and various models (not necessarily mathematical models) that were proposed for the G 1 to S transition [2,3,16], together with our own recent experimental results [15,17]. The model was implemented by a set of ordinary differential equations (ODEs) [18], an approach successfully used to describe the cell cycle control in budding yeast [19,20] and the cellular response of yeast to hyperosmotic shock [21].…”

Section: Introductionmentioning

confidence: 99%

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Cell Size at S Phase Initiation: an Emergent Propery of the G1/S Network

Barberis¹,

Klipp²,

Vanoni³

et al. 2005

PLoS Comput Biol

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The eukaryotic cell cycle is the repeated sequence of events that enable the division of a cell into two daughter cells. It is divided into four phases: G 1 , S, G 2 , and M. Passage through the cell cycle is strictly regulated by a molecular interaction network, which involves the periodic synthesis and destruction of cyclins that bind and activate cyclindependent kinases that are present in nonlimiting amounts. Cyclin-dependent kinase inhibitors contribute to cell cycle control. Budding yeast is an established model organism for cell cycle studies, and several mathematical models have been proposed for its cell cycle. An area of major relevance in cell cycle control is the G 1 to S transition. In any given growth condition, it is characterized by the requirement of a specific, critical cell size, P S , to enter S phase. The molecular basis of this control is still under discussion. The authors report a mathematical model of the G 1 to S network that newly takes into account nucleo/cytoplasmic localization, the role of the cyclin-dependent kinase Sic1 in facilitating nuclear import of its cognate Cdk1-Clb5, Whi5 control, and carbon source regulation of Sic1 and Sic1-containing complexes. The model was implemented by a set of ordinary differential equations that describe the temporal change of the concentration of the involved proteins and protein complexes. The model was tested by simulation in several genetic and nutritional setups and was found to be neatly consistent with experimental data. To estimate P S , the authors developed a hybrid model including a probabilistic component for firing of DNA replication origins. Sensitivity analysis of P S provides a novel relevant conclusion: P S is an emergent property of the G 1 to S network that strongly depends on growth rate.

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“…The third step has focused more on the role of Sic1, another Cki, in the control of the entrance into S phase, focusing on the modulation by carbon source of the subcellular localization of Sic1 [18]. Sic1 and the cyclins Clb5 and Clb6 are the major regulators of the Cdk1 kinase activity required for the onset of DNA replication [19].…”

Section: The Modular Systems Biology Approach For Cell Cycle Studiesmentioning

confidence: 99%

The modular systems biology approach to investigate the control of apoptosis in Alzheimer's disease neurodegeneration

Alberghina

Colangelo

2006

BMC Neurosci

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Apoptosis is a programmed cell death that plays a critical role during the development of the nervous system and in many chronic neurodegenerative diseases, including Alzheimer's disease (AD). This pathology, characterized by a progressive degeneration of cholinergic function resulting in a remarkable cognitive decline, is the most common form of dementia with high social and economic impact. Current therapies of AD are only symptomatic, therefore the need to elucidate the mechanisms underlying the onset and progression of the disease is surely needed in order to develop effective pharmacological therapies. Because of its pivotal role in neuronal cell death, apoptosis has been considered one of the most appealing therapeutic targets, however, due to the complexity of the molecular mechanisms involving the various triggering events and the many signaling cascades leading to cell death, a comprehensive understanding of this process is still lacking.Modular systems biology is a very effective strategy in organizing information about complex biological processes and deriving modular and mathematical models that greatly simplify the identification of key steps of a given process.This review aims at describing the main steps underlying the strategy of modular systems biology and briefly summarizes how this approach has been successfully applied for cell cycle studies. Moreover, after giving an overview of the many molecular mechanisms underlying apoptosis in AD, we present both a modular and a molecular model of neuronal apoptosis that suggest new insights on neuroprotection for this disease.

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Subcellular Localization of the Cyclin Dependent Kinase Inhibitor Sic1 is Modulated by the Carbon Source in Budding Yeast

Cited by 29 publications

References 52 publications

Molecular basis of the functional distinction between Cln1 and Cln2 cyclins

Molecular basis of the functional distinction between Cln1 and Cln2 cyclins

Cell Size at S Phase Initiation: an Emergent Propery of the G1/S Network

The modular systems biology approach to investigate the control of apoptosis in Alzheimer's disease neurodegeneration

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