Dataset Information

Jaiswal2017 - Cell cycle arrest

ABSTRACT: Jaiswal2017 - Cell cycle arrest This model is described in the article: ATM/Wip1 activities at chromatin control Plk1 re-activation to determine G2 checkpoint duration. Jaiswal H, Benada J, Müllers E, Akopyan K, Burdova K, Koolmeister T, Helleday T, Medema RH, Macurek L, Lindqvist A. EMBO J. 2017 Jul; 36(14): 2161-2176 Abstract: After DNA damage, the cell cycle is arrested to avoid propagation of mutations. Arrest in G2 phase is initiated by ATM-/ATR-dependent signaling that inhibits mitosis-promoting kinases such as Plk1. At the same time, Plk1 can counteract ATR-dependent signaling and is required for eventual resumption of the cell cycle. However, what determines when Plk1 activity can resume remains unclear. Here, we use FRET-based reporters to show that a global spread of ATM activity on chromatin and phosphorylation of ATM targets including KAP1 control Plk1 re-activation. These phosphorylations are rapidly counteracted by the chromatin-bound phosphatase Wip1, allowing cell cycle restart despite persistent ATM activity present at DNA lesions. Combining experimental data and mathematical modeling, we propose a model for how the minimal duration of cell cycle arrest is controlled. Our model shows how cell cycle restart can occur before completion of DNA repair and suggests a mechanism for checkpoint adaptation in human cells. This model is hosted on BioModels Database and identified by: BIOMD0000000641. To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

SUBMITTER: Karen Akopyan

PROVIDER: BIOMD0000000641 | BioModels | 2024-09-02

REPOSITORIES: BioModels

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			Action	DRS
	BIOMD0000000641?filename=BIOMD0000000641-biopax2.owl	Owl
	BIOMD0000000641?filename=BIOMD0000000641-biopax3.owl	Owl
	BIOMD0000000641?filename=BIOMD0000000641-matlab.m	Other
	BIOMD0000000641?filename=BIOMD0000000641-octave.m	Other
	BIOMD0000000641?filename=BIOMD0000000641.m	Other

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ATM/Wip1 activities at chromatin control Plk1 re-activation to determine G2 checkpoint duration.

Jaiswal Himjyot H Benada Jan J Müllers Erik E Akopyan Karen K Burdova Kamila K Koolmeister Tobias T Helleday Thomas T Medema René H RH Macurek Libor L Lindqvist Arne A

The EMBO journal 20170612 14

After DNA damage, the cell cycle is arrested to avoid propagation of mutations. Arrest in G2 phase is initiated by ATM-/ATR-dependent signaling that inhibits mitosis-promoting kinases such as Plk1. At the same time, Plk1 can counteract ATR-dependent signaling and is required for eventual resumption of the cell cycle. However, what determines when Plk1 activity can resume remains unclear. Here, we use FRET-based reporters to show that a global spread of ATM activity on chromatin and phosphorylati ...[more]

PMID: 28607002

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Project description:Not many models of mammalian cell cycle system exist due to its complexity. Some models are too complex and hard to understand, while some others are too simple and not comprehensive enough. Moreover, some essential aspects, such as the response of G1-S and G2-M checkpoints to DNA damage as well as the growth factor signalling, have not been investigated from a systems point of view in current mammalian cell cycle models. To address these issues, we bring a holistic perspective to cell cycle by mathematically modelling it as a complex system consisting of important sub-systems that interact with each other. This retains the functionality of the system and provides a clearer interpretation to the processes within it while reducing the complexity in comprehending these processes. To achieve this, we first update a published ODE mathematical model of cell cycle with current knowledge. Then the part of the mathematical model relevant to each sub-system is shown separately in conjunction with a diagram of the sub-system as part of this representation. The model sub-systems are Growth Factor, DNA damage, G1-S, and G2-M checkpoint signalling. To further simplify the model and better explore the function of sub-systems, they are further divided into modules. Here we also add important new modules of: chk-related rapid cell cycle arrest, p53 modules expanded to seamlessly integrate with the rapid arrest module, Tyrosine phosphatase modules that activate Cyc_Cdk complexes and play a crucial role in rapid and delay arrest at both G1-S and G2-M, Tyrosine Kinase module that is important for inactivating nuclear transport of CycB_cdk1 through Wee1 to resist M phase entry, Plk1-Related module that is crucial in activating Tyrosine phosphatases and inactivating Tyrosine kinase, and APC-Related module to show steps in CycB degradation. This multi-level systems approach incorporating all known aspects of cell cycle allowed us to (i) study, through dynamic simulation of an ODE model, comprehensive details of cell cycle dynamics under normal and DNA damage conditions revealing the role and value of the added new modules and elements, (ii) assess, through a global sensitivity analysis, the most influential sub-systems, modules and parameters on system response, such as G1-S and G2-M transitions, and (iii) probe deeply into the relationship between DNA damage and cell cycle progression and test the biological evidence that G1-S is relatively inefficient in arresting damaged cells compared to G2-M checkpoint. To perform sensitivity analysis, Self-Organizing Map with Correlation Coefficient Analysis (SOMCCA) is developed which shows that Growth Factor and G1-S Checkpoint sub-systems and 13 parameters in the modules within them are crucial for G1-S and G2-M transitions. To study the relative efficiency of DNA damage checkpoints, a Checkpoint Efficiency Evaluator (CEE) is developed based on perturbation studies and statistical Type II error. Accordingly, cell cycle is about 96% efficient in arresting damaged cells with G2-M checkpoint being more efficient than G1-S. Further, both checkpoint systems are near perfect (98.6%) in passing healthy cells. Thus this study has shown the efficacy of the proposed systems approach to gain a better understanding of different aspects of mammalian cell cycle system separately and as an integrated system that will also be useful in investigating targeted therapy in future cancer treatments.