IDEAS home Printed from https://ideas.repec.org/a/plo/pcbi00/1005376.html
   My bibliography  Save this article

Elucidation of molecular kinetic schemes from macroscopic traces using system identification

Author

Listed:
  • Miguel Fribourg
  • Diomedes E Logothetis
  • Javier González-Maeso
  • Stuart C Sealfon
  • Belén Galocha-Iragüen
  • Fernando Las-Heras Andrés
  • Vladimir Brezina

Abstract

Overall cellular responses to biologically-relevant stimuli are mediated by networks of simpler lower-level processes. Although information about some of these processes can now be obtained by visualizing and recording events at the molecular level, this is still possible only in especially favorable cases. Therefore the development of methods to extract the dynamics and relationships between the different lower-level (microscopic) processes from the overall (macroscopic) response remains a crucial challenge in the understanding of many aspects of physiology. Here we have devised a hybrid computational-analytical method to accomplish this task, the SYStems-based MOLecular kinetic scheme Extractor (SYSMOLE). SYSMOLE utilizes system-identification input-output analysis to obtain a transfer function between the stimulus and the overall cellular response in the Laplace-transformed domain. It then derives a Markov-chain state molecular kinetic scheme uniquely associated with the transfer function by means of a classification procedure and an analytical step that imposes general biological constraints. We first tested SYSMOLE with synthetic data and evaluated its performance in terms of its rate of convergence to the correct molecular kinetic scheme and its robustness to noise. We then examined its performance on real experimental traces by analyzing macroscopic calcium-current traces elicited by membrane depolarization. SYSMOLE derived the correct, previously known molecular kinetic scheme describing the activation and inactivation of the underlying calcium channels and correctly identified the accepted mechanism of action of nifedipine, a calcium-channel blocker clinically used in patients with cardiovascular disease. Finally, we applied SYSMOLE to study the pharmacology of a new class of glutamate antipsychotic drugs and their crosstalk mechanism through a heteromeric complex of G protein-coupled receptors. Our results indicate that our methodology can be successfully applied to accurately derive molecular kinetic schemes from experimental macroscopic traces, and we anticipate that it may be useful in the study of a wide variety of biological systems.Author summary: Unraveling the lower-level (microscopic) processes underlying the overall (macroscopic) cell response to a given stimulus is a challenging problem in cell physiology. This has been a classic problem in biophysics, where the ability to record the activity of single ion channels that generate a macroscopic ion current has allowed a measure of direct access to the underlying microscopic processes. These classic studies have demonstrated that very different groupings of the microscopic processes can yield extremely similar macroscopic responses. Biologists in fields other than biophysics are frequently confronted with the same macroscopic-to-microscopic problem, usually, however, without any direct access to the microscopic processes. Thus, the development of computational methods to deduce from the available macroscopic measurements the nature of the underlying microscopic processes can be expected to substantially advance the study of many areas of cell physiology. Toward that aim, here we have derived and tested a hybrid computational-analytical method to extract information about the microscopic processes that is hidden in macroscopic experimental traces. Our method is independent of the particular system under study, and thus can be applied to new as well as previously-recorded macroscopic traces obtained in a wide variety of biological systems.

Suggested Citation

  • Miguel Fribourg & Diomedes E Logothetis & Javier González-Maeso & Stuart C Sealfon & Belén Galocha-Iragüen & Fernando Las-Heras Andrés & Vladimir Brezina, 2017. "Elucidation of molecular kinetic schemes from macroscopic traces using system identification," PLOS Computational Biology, Public Library of Science, vol. 13(2), pages 1-34, February.
  • Handle: RePEc:plo:pcbi00:1005376
    DOI: 10.1371/journal.pcbi.1005376
    as

    Download full text from publisher

    File URL: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005376
    Download Restriction: no

    File URL: https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1005376&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pcbi.1005376?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. Daniel M. Rosenbaum & Søren G. F. Rasmussen & Brian K. Kobilka, 2009. "The structure and function of G-protein-coupled receptors," Nature, Nature, vol. 459(7245), pages 356-363, May.
    2. Leland H. Hartwell & John J. Hopfield & Stanislas Leibler & Andrew W. Murray, 1999. "From molecular to modular cell biology," Nature, Nature, vol. 402(6761), pages 47-52, December.
    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. Nicola Bellomo & Richard Bingham & Mark A.J. Chaplain & Giovanni Dosi & Guido Forni & Damian A. Knopoff & John Lowengrub & Reidun Twarock & Maria Enrica Virgillito, 2020. "A multi-scale model of virus pandemic: Heterogeneous interactive entities in a globally connected world," LEM Papers Series 2020/16, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    2. Lazaros K Gallos & Fabricio Q Potiguar & José S Andrade Jr & Hernan A Makse, 2013. "IMDB Network Revisited: Unveiling Fractal and Modular Properties from a Typical Small-World Network," PLOS ONE, Public Library of Science, vol. 8(6), pages 1-8, June.
    3. T. Ochiai & J. C. Nacher, 2007. "Stochastic analysis of autoregulatory gene expression dynamics," Mathematical and Computer Modelling of Dynamical Systems, Taylor & Francis Journals, vol. 14(4), pages 377-388, November.
    4. Qing-Ju Jiao & Yan-Kai Zhang & Lu-Ning Li & Hong-Bin Shen, 2011. "BinTree Seeking: A Novel Approach to Mine Both Bi-Sparse and Cohesive Modules in Protein Interaction Networks," PLOS ONE, Public Library of Science, vol. 6(11), pages 1-12, November.
    5. Manfred Füllsack, 2011. "Firstness - As seen from the perspective of Complexity Research," E-LOGOS, Prague University of Economics and Business, vol. 2011(1), pages 1-19.
    6. Kaihua Zhang & Hao Wu & Nicholas Hoppe & Aashish Manglik & Yifan Cheng, 2022. "Fusion protein strategies for cryo-EM study of G protein-coupled receptors," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    7. Simeon D. Castle & Michiel Stock & Thomas E. Gorochowski, 2024. "Engineering is evolution: a perspective on design processes to engineer biology," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
    8. Romualdo Pastor-Satorras & Eric Smith & Ricard V. Solé, 2002. "Evolving Protein Interaction Networks through Gene Duplication," Working Papers 02-02-008, Santa Fe Institute.
    9. Frederic Li Mow Chee & Bruno Beernaert & Billie G. C. Griffith & Alexander E. P. Loftus & Yatendra Kumar & Jimi C. Wills & Martin Lee & Jessica Valli & Ann P. Wheeler & J. Douglas Armstrong & Maddy Pa, 2023. "Mena regulates nesprin-2 to control actin–nuclear lamina associations, trans-nuclear membrane signalling and gene expression," Nature Communications, Nature, vol. 14(1), pages 1-19, December.
    10. Joshua S Weitz & Philip N Benfey & Ned S Wingreen, 2007. "Evolution, Interactions, and Biological Networks," PLOS Biology, Public Library of Science, vol. 5(1), pages 1-3, January.
    11. Bo Xu & Hongfei Lin & Yang Chen & Zhihao Yang & Hongfang Liu, 2013. "Protein Complex Identification by Integrating Protein-Protein Interaction Evidence from Multiple Sources," PLOS ONE, Public Library of Science, vol. 8(12), pages 1-12, December.
    12. Scholtens, Denise & Miron, Alexander & M. Merchant, Faisal & Miller, Arden & L. Miron, Penelope & Dirk Iglehart, J. & Gentleman, Robert, 2004. "Analyzing factorial designed microarray experiments," Journal of Multivariate Analysis, Elsevier, vol. 90(1), pages 19-43, July.
    13. Robrecht Cannoodt & Joeri Ruyssinck & Jan Ramon & Katleen De Preter & Yvan Saeys, 2018. "IncGraph: Incremental graphlet counting for topology optimisation," PLOS ONE, Public Library of Science, vol. 13(4), pages 1-11, April.
    14. Margaritis Voliotis & Philipp Thomas & Ramon Grima & Clive G Bowsher, 2016. "Stochastic Simulation of Biomolecular Networks in Dynamic Environments," PLOS Computational Biology, Public Library of Science, vol. 12(6), pages 1-18, June.
    15. Mark J. Wall & Emily Hill & Robert Huckstepp & Kerry Barkan & Giuseppe Deganutti & Michele Leuenberger & Barbara Preti & Ian Winfield & Sabrina Carvalho & Anna Suchankova & Haifeng Wei & Dewi Safitri , 2022. "Selective activation of Gαob by an adenosine A1 receptor agonist elicits analgesia without cardiorespiratory depression," Nature Communications, Nature, vol. 13(1), pages 1-22, December.
    16. Pilar García-Peñarrubia & Juan J Gálvez & Jesús Gálvez, 2011. "Spatio-Temporal Dependence of the Signaling Response in Immune-Receptor Trafficking Networks Regulated by Cell Density: A Theoretical Model," PLOS ONE, Public Library of Science, vol. 6(7), pages 1-12, July.
    17. Eyal Rozenfeld & Merav Tauber & Yair Ben-Chaim & Moshe Parnas, 2021. "GPCR voltage dependence controls neuronal plasticity and behavior," Nature Communications, Nature, vol. 12(1), pages 1-11, December.
    18. Emre Guney & Baldo Oliva, 2014. "Analysis of the Robustness of Network-Based Disease-Gene Prioritization Methods Reveals Redundancy in the Human Interactome and Functional Diversity of Disease-Genes," PLOS ONE, Public Library of Science, vol. 9(4), pages 1-11, April.
    19. Cui, Xue-Mei & Yoon, Chang No & Youn, Hyejin & Lee, Sang Hoon & Jung, Jean S. & Han, Seung Kee, 2017. "Dynamic burstiness of word-occurrence and network modularity in textbook systems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 487(C), pages 103-110.
    20. Zheng Hong, 2019. "A novel individualized drug repositioning approach for predicting personalized candidate drugs for type 1 diabetes mellitus," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 18(5), pages 1-9, October.

    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:pcbi00:1005376. 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.