IDEAS home Printed from https://ideas.repec.org/a/plo/pcbi00/1004295.html
   My bibliography  Save this article

Inferring Regulatory Networks from Experimental Morphological Phenotypes: A Computational Method Reverse-Engineers Planarian Regeneration

Author

Listed:
  • Daniel Lobo
  • Michael Levin

Abstract

Transformative applications in biomedicine require the discovery of complex regulatory networks that explain the development and regeneration of anatomical structures, and reveal what external signals will trigger desired changes of large-scale pattern. Despite recent advances in bioinformatics, extracting mechanistic pathway models from experimental morphological data is a key open challenge that has resisted automation. The fundamental difficulty of manually predicting emergent behavior of even simple networks has limited the models invented by human scientists to pathway diagrams that show necessary subunit interactions but do not reveal the dynamics that are sufficient for complex, self-regulating pattern to emerge. To finally bridge the gap between high-resolution genetic data and the ability to understand and control patterning, it is critical to develop computational tools to efficiently extract regulatory pathways from the resultant experimental shape phenotypes. For example, planarian regeneration has been studied for over a century, but despite increasing insight into the pathways that control its stem cells, no constructive, mechanistic model has yet been found by human scientists that explains more than one or two key features of its remarkable ability to regenerate its correct anatomical pattern after drastic perturbations. We present a method to infer the molecular products, topology, and spatial and temporal non-linear dynamics of regulatory networks recapitulating in silico the rich dataset of morphological phenotypes resulting from genetic, surgical, and pharmacological experiments. We demonstrated our approach by inferring complete regulatory networks explaining the outcomes of the main functional regeneration experiments in the planarian literature; By analyzing all the datasets together, our system inferred the first systems-biology comprehensive dynamical model explaining patterning in planarian regeneration. This method provides an automated, highly generalizable framework for identifying the underlying control mechanisms responsible for the dynamic regulation of growth and form.Author Summary: Developmental and regenerative biology experiments are producing a huge number of morphological phenotypes from functional perturbation experiments. However, existing pathway models do not generally explain the dynamic regulation of anatomical shape due to the difficulty of inferring and testing non-linear regulatory networks responsible for appropriate form, shape, and pattern. We present a method that automates the discovery and testing of regulatory networks explaining morphological outcomes directly from the resultant phenotypes, producing network models as testable hypotheses explaining regeneration data. Our system integrates a formalization of the published results in planarian regeneration, an in silico simulator in which the patterning properties of regulatory networks can be quantitatively tested in a regeneration assay, and a machine learning module that evolves networks whose behavior in this assay optimally matches the database of planarian results. We applied our method to explain the key experiments in planarian regeneration, and discovered the first comprehensive model of anterior-posterior patterning in planaria under surgical, pharmacological, and genetic manipulations. Beyond the planarian data, our approach is readily generalizable to facilitate the discovery of testable regulatory networks in developmental biology and biomedicine, and represents the first developmental model discovered de novo from morphological outcomes by an automated system.

Suggested Citation

  • 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.
  • Handle: RePEc:plo:pcbi00:1004295
    DOI: 10.1371/journal.pcbi.1004295
    as

    Download full text from publisher

    File URL: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004295
    Download Restriction: no

    File URL: https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1004295&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pcbi.1004295?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. Kolja Becker & Eva Balsa-Canto & Damjan Cicin-Sain & Astrid Hoermann & Hilde Janssens & Julio R Banga & Johannes Jaeger, 2013. "Reverse-Engineering Post-Transcriptional Regulation of Gap Genes in Drosophila melanogaster," PLOS Computational Biology, Public Library of Science, vol. 9(10), pages 1-16, October.
    2. Jeremiah J Faith & Boris Hayete & Joshua T Thaden & Ilaria Mogno & Jamey Wierzbowski & Guillaume Cottarel & Simon Kasif & James J Collins & Timothy S Gardner, 2007. "Large-Scale Mapping and Validation of Escherichia coli Transcriptional Regulation from a Compendium of Expression Profiles," PLOS Biology, Public Library of Science, vol. 5(1), pages 1-13, January.
    3. 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.
    4. 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.
    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. David Henriques & Alejandro F Villaverde & Miguel Rocha & Julio Saez-Rodriguez & Julio R Banga, 2017. "Data-driven reverse engineering of signaling pathways using ensembles of dynamic models," PLOS Computational Biology, Public Library of Science, vol. 13(2), pages 1-25, February.
    2. Lulu Shang & Jennifer A Smith & Xiang Zhou, 2020. "Leveraging gene co-expression patterns to infer trait-relevant tissues in genome-wide association studies," PLOS Genetics, Public Library of Science, vol. 16(4), pages 1-30, April.
    3. 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.
    4. Hossein Zare & Mostafa Kaveh & Arkady Khodursky, 2011. "Inferring a Transcriptional Regulatory Network from Gene Expression Data Using Nonlinear Manifold Embedding," PLOS ONE, Public Library of Science, vol. 6(8), pages 1-7, August.
    5. Karan Mangla & David L Dill & Mark A Horowitz, 2010. "Timing Robustness in the Budding and Fission Yeast Cell Cycles," PLOS ONE, Public Library of Science, vol. 5(2), pages 1-7, February.
    6. Diambra, L., 2011. "Coarse-grain reconstruction of genetic networks from expression levels," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(11), pages 2198-2207.
    7. Marco Grimaldi & Roberto Visintainer & Giuseppe Jurman, 2011. "RegnANN: Reverse Engineering Gene Networks Using Artificial Neural Networks," PLOS ONE, Public Library of Science, vol. 6(12), pages 1-19, December.
    8. 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.
    9. Ruonan Wu & Michelle R. Davison & William C. Nelson & Montana L. Smith & Mary S. Lipton & Janet K. Jansson & Ryan S. McClure & Jason E. McDermott & Kirsten S. Hofmockel, 2023. "Hi-C metagenome sequencing reveals soil phage–host interactions," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    10. repec:jss:jstsof:37:i01 is not listed on IDEAS
    11. 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.
    12. Cummings, F.W, 2004. "A model of morphogenesis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 339(3), pages 531-547.
    13. Joeri Ruyssinck & Vân Anh Huynh-Thu & Pierre Geurts & Tom Dhaene & Piet Demeester & Yvan Saeys, 2014. "NIMEFI: Gene Regulatory Network Inference using Multiple Ensemble Feature Importance Algorithms," PLOS ONE, Public Library of Science, vol. 9(3), pages 1-13, March.
    14. 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.
    15. Tom Wilderjans & Dirk Depril & Iven Van Mechelen, 2013. "Additive Biclustering: A Comparison of One New and Two Existing ALS Algorithms," Journal of Classification, Springer;The Classification Society, vol. 30(1), pages 56-74, April.
    16. Shuhei Kimura & Masanao Sato & Mariko Okada-Hatakeyama, 2013. "Inference of Vohradský's Models of Genetic Networks by Solving Two-Dimensional Function Optimization Problems," PLOS ONE, Public Library of Science, vol. 8(12), pages 1-11, December.
    17. Xiaomeng Zhang & Bin Shao & Yangle Wu & Ouyang Qi, 2013. "A Reverse Engineering Approach to Optimize Experiments for the Construction of Biological Regulatory Networks," PLOS ONE, Public Library of Science, vol. 8(9), pages 1-9, September.
    18. 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.
    19. Takanori Hasegawa & Rui Yamaguchi & Masao Nagasaki & Satoru Miyano & Seiya Imoto, 2014. "Inference of Gene Regulatory Networks Incorporating Multi-Source Biological Knowledge via a State Space Model with L1 Regularization," PLOS ONE, Public Library of Science, vol. 9(8), pages 1-19, August.
    20. Kannan Venkateshan & Tegner Jesper, 2016. "Adaptive input data transformation for improved network reconstruction with information theoretic algorithms," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 15(6), pages 507-520, December.
    21. 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.

    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:pcbi00:1004295. 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.