Taoma2024 - The SSA-based Boolean model of cell cycle in budding yeast
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ABSTRACT:
SUBMITTER: 
Kittisak taoma
PROVIDER: MODEL2405030001 | BioModels | 2024-08-22
REPOSITORIES: BioModels
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| MODEL2405030001?filename=model.py | Other | |||
| MODEL2405030001?filename=model_parameters.py | Other |
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Project description:BackgroundCell cycle process of budding yeast (Saccharomyces cerevisiae) consists of four phases: G1, S, G2 and M. Initiated by stimulation of the G1 phase, cell cycle returns to the G1 stationary phase through a sequence of the S, G2 and M phases. During the cell cycle, a cell verifies whether necessary conditions are satisfied at the end of each phase (i.e., checkpoint) since damages of any phase can cause severe cell cycle defect. The cell cycle can proceed to the next phase properly only if checkpoint conditions are met. Over the last decade, there have been several studies to construct Boolean models that capture checkpoint conditions. However, they mostly focused on robustness to network perturbations, and the timing robustness has not been much addressed. Only recently, some studies suggested extension of such models towards timing-robust models, but they have not considered checkpoint conditions.ResultsTo construct a timing-robust Boolean model that preserves checkpoint conditions of the budding yeast cell cycle, we used a model verification technique, 'model checking'. By utilizing automatic and exhaustive verification of model checking, we found that previous models cannot properly capture essential checkpoint conditions in the presence of timing variations. In particular, such models violate the M phase checkpoint condition so that it allows a division of a budding yeast cell into two before the completion of its full DNA replication and synthesis. In this paper, we present a timing-robust model that preserves all the essential checkpoint conditions properly against timing variations. Our simulation results show that the proposed timing-robust model is more robust even against network perturbations and can better represent the nature of cell cycle than previous models.ConclusionsTo our knowledge this is the first work that rigorously examined the timing robustness of the cell cycle process of budding yeast with respect to checkpoint conditions using Boolean models. The proposed timing-robust model is the complete state-of-the-art model that guarantees no violation in terms of checkpoints known to date.
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Project description:The cell division cycle is regulated by a complex network of interacting genes and proteins. The control system has been modeled in many ways, from qualitative Boolean switching-networks to quantitative differential equations and highly detailed stochastic simulations. Here we develop a continuous-time stochastic model using seven Boolean variables to represent the activities of major regulators of the budding yeast cell cycle plus one continuous variable representing cell growth. The Boolean variables are updated asynchronously by logical rules based on known biochemistry of the cell-cycle control system using Gillespie’s stochastic simulation algorithm. Time and cell size are updated continuously. By simulating a population of yeast cells, we calculate statistical properties of cell cycle progression that can be compared directly to experimental measurements. Perturbations of the normal sequence of events indicate that the cell cycle is 91% robust to random ‘flips’ of the Boolean variables, but 9% of the perturbations induce lethal mistakes in cell cycle progression. This simple, hybrid Boolean model gives a good account of the growth and division of budding yeast cells, suggesting that this modeling approach may be as accurate as detailed reaction-kinetic modeling with considerably less demands on estimating rate constants.
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Project description:The cell cycle of budding yeast is governed by an intricate protein regulatory network whose dysregulation can lead to lethal mistakes or aberrant cell division cycles. In this work, we model this network in a Boolean framework for stochastic simulations. Our model is sufficiently detailed to account for the phenotypes of 40 mutant yeast strains (83% of the experimentally characterized strains that we simulated) and also to simulate an endoreplicating strain (multiple rounds of DNA synthesis without mitosis) and a strain that exhibits 'Cdc14 endocycles' (periodic transitions between metaphase and anaphase). Because our model successfully replicates the observed properties of both wild-type yeast cells and many mutant strains, it provides a reasonable, validated starting point for more comprehensive stochastic-Boolean models of cell cycle controls. Such models may provide a better understanding of cell cycle anomalies in budding yeast and ultimately in mammalian cells.
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Project description:Understanding the structure and interplay of cellular signalling pathways is one of the great challenges in molecular biology. Boolean Networks can infer signalling networks from observations of protein activation. In situations where it is difficult to assess protein activation directly, Nested Effect Models are an alternative. They derive the network structure indirectly from downstream effects of pathway perturbations. To date, Nested Effect Models cannot resolve signalling details like the formation of signalling complexes or the activation of proteins by multiple alternative input signals. Here we introduce Boolean Nested Effect Models (B-NEM). B-NEMs combine the use of downstream effects with the higher resolution of signalling pathway structures in Boolean Networks. We show that B-NEMs accurately reconstruct signal flows in simulated data. Using B-NEM we then resolve BCR signalling via PI3K and TAK1 kinases in BL2 lymphoma cell lines. 84 BL2 cell-line samples were hybridized to HGU133+2 Affymetrix GeneChips.
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Project description:During cell division, aberrant DNA structures are detected by regulators called checkpoints that slow division to allow error correction. In addition to checkpoint-induced delay, it is widely assumed, though rarely shown, that merely slowing the cell cycle might allow more time for error detection and correction, thus resulting in a more stable genome. Fidelity by a slowed cell cycle might be independent of checkpoints. Here we tested the hypothesis that a slowed cell cycle stabilizes the genome, independent of checkpoints, in the budding yeast Saccharomyces cerevisiae We were led to this hypothesis when we identified a gene (ERV14, an ER cargo membrane protein) that when mutated, unexpectedly stabilized the genome, as measured by three different chromosome assays. After extensive studies of pathways rendered dysfunctional in erv14 mutant cells, we are led to the inference that no particular pathway is involved in stabilization, but rather the slowed cell cycle induced by erv14 stabilized the genome. We then demonstrated that, in genetic mutations and chemical treatments unrelated to ERV14, a slowed cell cycle indeed correlates with a more stable genome, even in checkpoint-proficient cells. Data suggest a delay in G2/M may commonly stabilize the genome. We conclude that chromosome errors are more rarely made or are more readily corrected when the cell cycle is slowed (even ∼15 min longer in an ∼100-min cell cycle). And, some chromosome errors may not signal checkpoint-mediated responses, or do not sufficiently signal to allow correction, and their correction benefits from this "time checkpoint."
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