IDEAS home Printed from https://ideas.repec.org/a/plo/pcbi00/1006630.html
   My bibliography  Save this article

Identifying (un)controllable dynamical behavior in complex networks

Author

Listed:
  • Jordan C Rozum
  • Réka Albert

Abstract

We present a technique applicable in any dynamical framework to identify control-robust subsets of an interacting system. These robust subsystems, which we call stable modules, are characterized by constraints on the variables that make up the subsystem. They are robust in the sense that if the defining constraints are satisfied at a given time, they remain satisfied for all later times, regardless of what happens in the rest of the system, and can only be broken if the constrained variables are externally manipulated. We identify stable modules as graph structures in an expanded network, which represents causal links between variable constraints. A stable module represents a system “decision point”, or trap subspace. Using the expanded network, small stable modules can be composed sequentially to form larger stable modules that describe dynamics on the system level. Collections of large, mutually exclusive stable modules describe the system’s repertoire of long-term behaviors. We implement this technique in a broad class of dynamical systems and illustrate its practical utility via examples and algorithmic analysis of two published biological network models. In the segment polarity gene network of Drosophila melanogaster, we obtain a state-space visualization that reproduces by novel means the four possible cell fates and predicts the outcome of cell transplant experiments. In the T-cell signaling network, we identify six signaling elements that determine the high-signal response and show that control of an element connected to them cannot disrupt this response.Author summary: We show how to uncover the causal relationships between qualitative statements about the values of variables in ODE systems. We then show how these relationships can be used to identify subsystem behaviors that are robust to outside interventions. This informs potential system control strategies (e.g., in identifying drug targets). Typical analytical properties of biomolecular systems render them particularly amenable to our techniques. Furthermore, due to their often high dimension and large uncertainties, our results are particularly useful in biomolecular systems. We apply our methods to two quantitative biological models: the segment polarity gene network of Drosophila melanogaster and the T-cell signal transduction network.

Suggested Citation

  • Jordan C Rozum & Réka Albert, 2018. "Identifying (un)controllable dynamical behavior in complex networks," PLOS Computational Biology, Public Library of Science, vol. 14(12), pages 1-17, December.
  • Handle: RePEc:plo:pcbi00:1006630
    DOI: 10.1371/journal.pcbi.1006630
    as

    Download full text from publisher

    File URL: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1006630
    Download Restriction: no

    File URL: https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1006630&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pcbi.1006630?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. George von Dassow & Eli Meir & Edwin M. Munro & Garrett M. Odell, 2000. "The segment polarity network is a robust developmental module," Nature, Nature, vol. 406(6792), pages 188-192, July.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Adel Dayarian & Madalena Chaves & Eduardo D Sontag & Anirvan M Sengupta, 2009. "Shape, Size, and Robustness: Feasible Regions in the Parameter Space of Biochemical Networks," PLOS Computational Biology, Public Library of Science, vol. 5(1), pages 1-12, January.
    2. Jasmin Fisher & Nir Piterman & Alex Hajnal & Thomas A Henzinger, 2007. "Predictive Modeling of Signaling Crosstalk during C. elegans Vulval Development," PLOS Computational Biology, Public Library of Science, vol. 3(5), pages 1-12, May.
    3. Daniel Lobo & Michael Levin, 2015. "Inferring Regulatory Networks from Experimental Morphological Phenotypes: A Computational Method Reverse-Engineers Planarian Regeneration," PLOS Computational Biology, Public Library of Science, vol. 11(6), pages 1-28, June.
    4. Sabine Schilling & Maria Willecke & Tinri Aegerter-Wilmsen & Olaf A Cirpka & Konrad Basler & Christian von Mering, 2011. "Cell-Sorting at the A/P Boundary in the Drosophila Wing Primordium: A Computational Model to Consolidate Observed Non-Local Effects of Hh Signaling," PLOS Computational Biology, Public Library of Science, vol. 7(4), pages 1-12, April.
    5. Cummings, F.W, 2004. "A model of morphogenesis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 339(3), pages 531-547.
    6. Ryan N Gutenkunst & Joshua J Waterfall & Fergal P Casey & Kevin S Brown & Christopher R Myers & James P Sethna, 2007. "Universally Sloppy Parameter Sensitivities in Systems Biology Models," PLOS Computational Biology, Public Library of Science, vol. 3(10), pages 1-8, October.
    7. Zeina Shreif & Vipul Periwal, 2014. "A Network Characteristic That Correlates Environmental and Genetic Robustness," PLOS Computational Biology, Public Library of Science, vol. 10(2), pages 1-23, February.
    8. Andreas Wagner, 2015. "Causal Drift, Robust Signaling, and Complex Disease," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-29, March.
    9. Manuel Cambón & Óscar Sánchez, 2022. "Thermodynamic Modelling of Transcriptional Control: A Sensitivity Analysis," Mathematics, MDPI, vol. 10(13), pages 1-18, June.
    10. Debasish Mondal & Edward Dougherty & Abhishek Mukhopadhyay & Adria Carbo & Guang Yao & Jianhua Xing, 2014. "Systematic Reverse Engineering of Network Topologies: A Case Study of Resettable Bistable Cellular Responses," PLOS ONE, Public Library of Science, vol. 9(8), pages 1-12, August.
    11. Guillermo Rodrigo & Santiago F Elena, 2011. "Structural Discrimination of Robustness in Transcriptional Feedforward Loops for Pattern Formation," PLOS ONE, Public Library of Science, vol. 6(2), pages 1-7, February.
    12. Miles Miller & Marc Hafner & Eduardo Sontag & Noah Davidsohn & Sairam Subramanian & Priscilla E M Purnick & Douglas Lauffenburger & Ron Weiss, 2012. "Modular Design of Artificial Tissue Homeostasis: Robust Control through Synthetic Cellular Heterogeneity," PLOS Computational Biology, Public Library of Science, vol. 8(7), pages 1-18, July.
    13. Stefano Ciliberti & Olivier C Martin & Andreas Wagner, 2007. "Robustness Can Evolve Gradually in Complex Regulatory Gene Networks with Varying Topology," PLOS Computational Biology, Public Library of Science, vol. 3(2), pages 1-10, February.
    14. Carl Song & Hilary Phenix & Vida Abedi & Matthew Scott & Brian P Ingalls & Mads Kærn & Theodore J Perkins, 2010. "Estimating the Stochastic Bifurcation Structure of Cellular Networks," PLOS Computational Biology, Public Library of Science, vol. 6(3), pages 1-11, March.
    15. Kazunari Kaizu & Hisao Moriya & Hiroaki Kitano, 2010. "Fragilities Caused by Dosage Imbalance in Regulation of the Budding Yeast Cell Cycle," PLOS Genetics, Public Library of Science, vol. 6(4), pages 1-12, April.
    16. Merav Socolovsky & Michael Murrell & Ying Liu & Ramona Pop & Ermelinda Porpiglia & Andre Levchenko, 2007. "Negative Autoregulation by FAS Mediates Robust Fetal Erythropoiesis," PLOS Biology, Public Library of Science, vol. 5(10), pages 1-16, September.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pcbi00:1006630. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ploscompbiol (email available below). General contact details of provider: https://journals.plos.org/ploscompbiol/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.