Regulation of modular Cyclin and CDK feedback loops by an E2F transcription oscillator in the mammalian cell cycle

  • Received: 01 April 2010 Accepted: 29 June 2018 Published: 01 April 2011
  • MSC : Primary: 58F15, 58F17; Secondary: 53C35.

  • The cell cycle is regulated by a large number of enzymes and transcription factors. We have developed a modular description of the cell cycle, based on a set of interleaved modular feedback loops, each leading to a cyclic behavior. The slowest loop is the E2F transcription and ubiquitination, which determines the cycling frequency of the entire cell cycle. Faster feedback loops describe the dynamics of each Cyclin by itself. Our model shows that the cell cycle progression as well as the checkpoints of the cell cycle can be understood through the interactions between the main E2F feedback loop and the driven Cyclin feedback loops. Multiple models were proposed for the cell cycle dynamics; each with differing basic mechanisms. We here propose a new generic formalism. In contrast with existing models, the proposed formalism allows a straightforward analysis and understanding of the dynamics, neglecting the details of each interaction. This model is not sensitive to small changes in the parameters used and it reproduces the observed behavior of the transcription factor E2F and different Cyclins in continuous or regulated cycling conditions. The modular description of the cell cycle resolves the gap between cyclic models, solely based on protein-protein reactions and transcription reactions based models. Beyond the explanation of existing observations, this model suggests the existence of unknown interactions, such as the need for a functional interaction between Cyclin B and retinoblastoma protein (Rb) de-phosphorylation.

    Citation: Orit Lavi, Doron Ginsberg, Yoram Louzoun. Regulation of modular Cyclin and CDK feedback loops by an E2F transcriptionoscillator in the mammalian cell cycle[J]. Mathematical Biosciences and Engineering, 2011, 8(2): 445-461. doi: 10.3934/mbe.2011.8.445

    Related Papers:

    [1] Yanqin Wang, Xin Ni, Jie Yan, Ling Yang . Modeling transcriptional co-regulation of mammalian circadian clock. Mathematical Biosciences and Engineering, 2017, 14(5&6): 1447-1462. doi: 10.3934/mbe.2017075
    [2] LanJiang Luo, Haihong Liu, Fang Yan . Dynamic behavior of P53-Mdm2-Wip1 gene regulatory network under the influence of time delay and noise. Mathematical Biosciences and Engineering, 2023, 20(2): 2321-2347. doi: 10.3934/mbe.2023109
    [3] Yue Liu, Wing-Cheong Lo . Analysis of spontaneous emergence of cell polarity with delayed negative feedback. Mathematical Biosciences and Engineering, 2019, 16(3): 1392-1413. doi: 10.3934/mbe.2019068
    [4] Maria Conceição A. Leite, Yunjiao Wang . Multistability, oscillations and bifurcations in feedback loops. Mathematical Biosciences and Engineering, 2010, 7(1): 83-97. doi: 10.3934/mbe.2010.7.83
    [5] Cicely K. Macnamara, Mark A. J. Chaplain . Spatio-temporal models of synthetic genetic oscillators. Mathematical Biosciences and Engineering, 2017, 14(1): 249-262. doi: 10.3934/mbe.2017016
    [6] Jian Zhang, Xingchen Liang, Feng Zhou, Bo Li, Yanling Li . TYLER, a fast method that accurately predicts cyclin-dependent proteins by using computation-based motifs and sequence-derived features. Mathematical Biosciences and Engineering, 2021, 18(5): 6410-6429. doi: 10.3934/mbe.2021318
    [7] Jifa Jiang, Qiang Liu, Lei Niu . Theoretical investigation on models of circadian rhythms based on dimerization and proteolysis of PER and TIM. Mathematical Biosciences and Engineering, 2017, 14(5&6): 1247-1259. doi: 10.3934/mbe.2017064
    [8] Gheorghe Craciun, Baltazar Aguda, Avner Friedman . Mathematical Analysis Of A Modular Network Coordinating The Cell Cycle And Apoptosis. Mathematical Biosciences and Engineering, 2005, 2(3): 473-485. doi: 10.3934/mbe.2005.2.473
    [9] Baltazar D. Aguda, Ricardo C.H. del Rosario, Michael W.Y. Chan . Oncogene-tumor suppressor gene feedback interactions and their control. Mathematical Biosciences and Engineering, 2015, 12(6): 1277-1288. doi: 10.3934/mbe.2015.12.1277
    [10] Changgui Gu, Ping Wang, Tongfeng Weng, Huijie Yang, Jos Rohling . Heterogeneity of neuronal properties determines the collective behavior of the neurons in the suprachiasmatic nucleus. Mathematical Biosciences and Engineering, 2019, 16(4): 1893-1913. doi: 10.3934/mbe.2019092
  • The cell cycle is regulated by a large number of enzymes and transcription factors. We have developed a modular description of the cell cycle, based on a set of interleaved modular feedback loops, each leading to a cyclic behavior. The slowest loop is the E2F transcription and ubiquitination, which determines the cycling frequency of the entire cell cycle. Faster feedback loops describe the dynamics of each Cyclin by itself. Our model shows that the cell cycle progression as well as the checkpoints of the cell cycle can be understood through the interactions between the main E2F feedback loop and the driven Cyclin feedback loops. Multiple models were proposed for the cell cycle dynamics; each with differing basic mechanisms. We here propose a new generic formalism. In contrast with existing models, the proposed formalism allows a straightforward analysis and understanding of the dynamics, neglecting the details of each interaction. This model is not sensitive to small changes in the parameters used and it reproduces the observed behavior of the transcription factor E2F and different Cyclins in continuous or regulated cycling conditions. The modular description of the cell cycle resolves the gap between cyclic models, solely based on protein-protein reactions and transcription reactions based models. Beyond the explanation of existing observations, this model suggests the existence of unknown interactions, such as the need for a functional interaction between Cyclin B and retinoblastoma protein (Rb) de-phosphorylation.


  • This article has been cited by:

    1. Shanshan Wu, Tingting Wei, Wenjuan Fan, Yanli Wang, Chaojie Li, Jinbo Deng, Cell cycle during neuronal migration and neocortical lamination, 2021, 0736-5748, 10.1002/jdn.10091
    2. Orit Lavi, Redundancy: A Critical Obstacle to Improving Cancer Therapy, 2015, 75, 0008-5472, 808, 10.1158/0008-5472.CAN-14-3256
    3. Xiaodan Li, Xiaolei Yao, Haiqiang Xie, Mingtian Deng, Xiaoxiao Gao, Kaiping Deng, Yongjin Bao, Qi Wang, Feng Wang, Effects of SPATA6 on proliferation, apoptosis and steroidogenesis of Hu sheep Leydig cells in vitro, 2021, 166, 0093691X, 9, 10.1016/j.theriogenology.2021.02.011
    4. Yongjin Bao, Xiaodan Li, M.A. El-Samahy, Hua Yang, Zhibo Wang, Fan Yang, Xiaolei Yao, Feng Wang, Exploration the role of INHBA in Hu sheep granulosa cells using RNA-Seq, 2023, 197, 0093691X, 198, 10.1016/j.theriogenology.2022.12.006
    5. Jinqian Wang, Xiang Chen, Wei Sun, Wen Tang, Jiajing Chen, Yuan Zhang, Ruiyang Li, Yanfei Wang, Expression of GLOD4 in the Testis of the Qianbei Ma Goat and Its Effect on Leydig Cells, 2024, 14, 2076-2615, 2611, 10.3390/ani14172611
  • Reader Comments
  • © 2011 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(3001) PDF downloads(501) Cited by(5)

Article outline

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog