An Extended, Boolean Model of the Septation Initiation Network in S.Pombe Provides Insights into Its Regulation

Chasapi, Anastasia; Wachowicz, Paulina; Niknejad, Anne; Collin, Philippe; Krapp, Andrea; Cano, Elena; Xénarios, Ioannis

doi:10.1371/journal.pone.0134214

Cited by 8 publications

(5 citation statements)

References 97 publications

(121 reference statements)

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“…The entire collection of these rules, encoded as regulatory logic functions, comprise a Boolean model. Although these models lack temporal units, they can be used to explore properties of biological systems such as the presence of attractors (states of convergence, often phenotypes such as healthy or disease states) and the alterations or perturbations that drive transitions between them ( 24 ).…”

Section: Constructing a Pkn Of The Formation Of Atherosclerotic Plaqumentioning

confidence: 99%

Expert curation for building network-based dynamical models: a case study on atherosclerotic plaque formation

et al. 2018

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Knowledgebases play an increasingly important role in scientific research, where the expert curation of biological knowledge in forms that are amenable to computational analysis (using ontologies for example)–provides a significant added value and enables new types of computational analyses for high throughput datasets. In this work, we demonstrate how expert curation can also play a more direct role in research, by supporting the use of network-based dynamical models to study a specific biological process. This curation effort is focused on the regulatory interactions between biological entities, such as genes or proteins and compounds, which may interact with each other in a complex manner, including regulatory complexes and conditional dependencies between co-regulators. This critical information has to be captured and encoded in a computable manner, which is currently far beyond the current capabilities of automatically constructed network. As a case study, we report here the prior knowledge network constructed by the sysVASC consortium to model the biological events leading to the formation of atherosclerotic plaques, during the onset of cardiovascular disease and discuss some specific examples to illustrate the main pitfalls and added value provided by the expert curation during this endeavor. Database URL: http://biomodels.caltech.edu

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Section: Constructing a Pkn Of The Formation Of Atherosclerotic Plaqumentioning

confidence: 99%

Expert curation for building network-based dynamical models: a case study on atherosclerotic plaque formation

et al. 2018

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“…An example is given by the work of Chasapi et al , where the back and forth between models and experiments lead to a prediction of a counterintuitive mutant phenotype in the fission yeast Schizosaccharomyces pombe which was then validated experimentally (Chasapi et al. 2015). …”

Section: Logical Modeling Of the Budding Yeast Cell Cyclementioning

confidence: 99%

Advances and challenges in logical modeling of cell cycle regulation: perspective for multi-scale, integrative yeast cell models

Barberis

Todd

Zee

2016

FEMS Yeast Research

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The eukaryotic cell cycle is robustly designed, with interacting molecules organized within a definite topology that ensures temporal precision of its phase transitions. Its underlying dynamics are regulated by molecular switches, for which remarkable insights have been provided by genetic and molecular biology efforts. In a number of cases, this information has been made predictive, through computational models. These models have allowed for the identification of novel molecular mechanisms, later validated experimentally. Logical modeling represents one of the youngest approaches to address cell cycle regulation. We summarize the advances that this type of modeling has achieved to reproduce and predict cell cycle dynamics. Furthermore, we present the challenge that this type of modeling is now ready to tackle: its integration with intracellular networks, and its formalisms, to understand crosstalks underlying systems level properties, ultimate aim of multi-scale models. Specifically, we discuss and illustrate how such an integration may be realized, by integrating a minimal logical model of the cell cycle with a metabolic network.

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“…Logical models have been used widely to explore cell cycle regulation in yeast, starting with Li and colleagues [26], and some large scale models have included coarse-grained representations of exit from mitosis [27,28]. A logical model was used to explore regulation of the Septation Initiation Network (SIN) [29], the MEN's homologous https://doi.org/10.1371/journal.pbio.3000917.g001…”

Section: Introductionmentioning

confidence: 99%

“…Logical models have been used widely to explore cell cycle regulation in yeast, starting with Li and colleagues [ 26 ], and some large scale models have included coarse-grained representations of exit from mitosis [ 27 , 28 ]. A logical model was used to explore regulation of the Septation Initiation Network (SIN) [ 29 ], the MEN’s homologous pathway in Schizosaccharomyces pombe [ 30 ]. Münzner and colleagues [ 31 ] recently published the most comprehensive logical yeast cell cycle model yet.…”

Section: Introductionmentioning

confidence: 99%

Unifying the mechanism of mitotic exit control in a spatiotemporal logical model

et al. 2020

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The transition from mitosis into the first gap phase of the cell cycle in budding yeast is controlled by the Mitotic Exit Network (MEN). The network interprets spatiotemporal cues about the progression of mitosis and ensures that release of Cdc14 phosphatase occurs only after completion of key mitotic events. The MEN has been studied intensively; however, a unified understanding of how localisation and protein activity function together as a system is lacking. In this paper, we present a compartmental, logical model of the MEN that is capable of representing spatial aspects of regulation in parallel to control of enzymatic activity. We show that our model is capable of correctly predicting the phenotype of the majority of mutants we tested, including mutants that cause proteins to mislocalise. We use a continuous time implementation of the model to demonstrate that Cdc14 Early Anaphase Release (FEAR) ensures robust timing of anaphase, and we verify our findings in living cells. Furthermore, we show that our model can represent measured cell–cell variation in Spindle Position Checkpoint (SPoC) mutants. This work suggests a general approach to incorporate spatial effects into logical models. We anticipate that the model itself will be an important resource to experimental researchers, providing a rigorous platform to test hypotheses about regulation of mitotic exit.

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An Extended, Boolean Model of the Septation Initiation Network in S.Pombe Provides Insights into Its Regulation

Cited by 8 publications

References 97 publications

Expert curation for building network-based dynamical models: a case study on atherosclerotic plaque formation

Expert curation for building network-based dynamical models: a case study on atherosclerotic plaque formation

Advances and challenges in logical modeling of cell cycle regulation: perspective for multi-scale, integrative yeast cell models

Unifying the mechanism of mitotic exit control in a spatiotemporal logical model

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