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Dynamic modelling of oestrogen signalling and cell fate in breast cancer cells

Nature Reviews Cancer volume 11, pages 523532 (2011) | Download Citation

Abstract

Cancers of the breast and other tissues arise from aberrant decision-making by cells regarding their survival or death, proliferation or quiescence, damage repair or bypass. These decisions are made by molecular signalling networks that process information from outside and from within the breast cancer cell and initiate responses that determine the cell's survival and reproduction. Because the molecular logic of these circuits is difficult to comprehend by intuitive reasoning alone, we present some preliminary mathematical models of the basic decision circuits in breast cancer cells that may aid our understanding of their susceptibility or resistance to endocrine therapy.

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Acknowledgements

This work was supported in part by US National Institutes of Health grants U54-CA149147 (to R.C.) and R01-GM078989 (to J.J.T. and W.B.), by US National Science Foundation grants DMS-0342283 (to J.J.T. and P. Brazhnik) and DBI-0904340 (to A.V.), and by fellowships to C.C. and I.T. provided by the Virginia Polytechnic Institute and State University graduate program in Genetics, Bioinformatics and Computational Biology.

Author information

Affiliations

  1. John J. Tyson and Anael Verdugo are at the Department of Biological Sciences, Virginia Polytechnic Institute and State University, Blacksburg, Virginia 24061, USA.

    • John J. Tyson
    •  & Anael Verdugo
  2. William T. Baumann and Yue Wang are at the Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Blacksburg, Virginia 24061, USA.

    • William T. Baumann
    •  & Yue Wang
  3. Chun Chen and Iman Tavassoly are at the Graduate Program in Genetics, Bioinformatics and Computational Biology, Virginia Polytechnic Institute and State University, Blacksburg, Virginia 24061, USA.

    • Chun Chen
    •  & Iman Tavassoly
  4. Louis M. Weiner and Robert Clarke are at the Lombardi Comprehensive Cancer Center, Georgetown University School of Medicine, Washington, District of Columbia 20057, USA.

    • Louis M. Weiner
    •  & Robert Clarke

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Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to John J. Tyson.

Supplementary information

PDF files

  1. 1.

    Supplementary information S1 (text)

    RB-E2F-CYCE Bistable Switch

  2. 2.

    Supplementary information S2 (text)

    BH3-BCL-BAX Bistable Switch

  3. 3.

    Supplementary information S3 (text)

    Beclin-1 Rheostat

  4. 4.

    Supplementary information S4 (text)

    Unfolded Protein Response in Mammalian Cells

  5. 5.

    Supplementary information S5 (text)

    ER-GFR Survival Signaling Switch

Glossary

Autophagy

Degradation of a cell's own components, using its lysosomal machinery, to remove damaged organelles and/or to provide energy and raw materials for adaptation and survival under stressful conditions.

Bistable switch

A regulatory network that can persist, under identical external conditions, in either of two stable states ('ON' or 'OFF') depending on its recent history.

Crosstalk

Interactions among modules that alter the behaviour of the modules in isolation.

Dynamic behaviour

The characteristic change over time of a molecular regulatory network in response to a specific pattern of input signals.

Modules

A set of molecular interactions that accomplishes a specific task in a cell, such as committing a cell to a new round of DNA replication.

Molecular interaction graph

A representation of a set of biochemical reactions involving co-regulated genes and proteins; for example, a signal transduction pathway or a transcription factor network. Also referred to as a 'wiring diagram'.

Plasticity

The ability of a regulatory network, in the face of interference or damage, to adapt and maintain something akin to its normal function.

Rheostat

A variable resistor, used to provide continuous control over the current through a circuit (for example, the dimmer knob on a light fixture).

Signal–response curve

The functional dependence of the output of a molecular regulatory network (for example, the activity of a transcription factor) on changing values of its input (for example, concentration of a growth factor in the extracellular medium).

Stochastic fluctuations

Random variations in the numbers of molecules of mRNAs and proteins due to the unpredictable nature of chemical reactions at the molecular level.

Unfolded protein response

The cellular response to the accumulation of misfolded proteins in the endoplasmic reticulum.

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DOI

https://doi.org/10.1038/nrc3081

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