IDEAS home Printed from https://ideas.repec.org/a/plo/pcbi00/1007889.html
   My bibliography  Save this article

Disease evolution in reaction networks: Implications for a diagnostic problem

Author

Listed:
  • Abolfazl Ramezanpour
  • Alireza Mashaghi

Abstract

We study the time evolution of symptoms (signs) with some defects in the dynamics of a reaction network as a (microscopic) model for the progress of disease phenotypes. To this end, we take a large population of reaction networks and follow the stochastic dynamics of the system to see how the development of defects affects the macroscopic states of the signs probability distribution. We start from some plausible definitions for the healthy and disease states along with a dynamical model for the emergence of diseases by a reverse simulated annealing algorithm. The healthy state is defined as a state of maximum objective function, which here is the sum of mutual information between a subset of signal variables and the subset of assigned response variables. A disease phenotype is defined with two parameters controlling the rate of mutations in reactions and the rate of accepting mutations that reduce the objective function. The model can provide the time dependence of the sign probabilities given a disease phenotype. This allows us to obtain the accuracy of diagnosis as a function of time by using a probabilistic model of signs and diseases. The trade-off between the diagnosis accuracy (increasing in time) and the objective function (decreasing in time) can be used to suggest an optimal time for medical intervention. Our model would be useful in particular for a dynamical (history-based) diagnostic problem, to estimate the likelihood of a disease hypothesis given the temporal evolution of the signs.Author summary: Here, we use concepts from statistical physics and reaction network dynamics to introduce a measure to quantify the tradeoff between the accuracy of diagnosis and an early diagnosis. This measure is used to suggest an optimal time for medical intervention depending on the number of observed signs (medical tests). We present a stochastic model using a reverse simulated annealing algorithm for numerical simulation of disease evolution. The model can provide the time dependence of the sign probabilities given a disease phenotype. This in turn allows us to anticipate the accuracy of diagnosis as a function of time by using a probabilistic model of signs and diseases.

Suggested Citation

  • Abolfazl Ramezanpour & Alireza Mashaghi, 2020. "Disease evolution in reaction networks: Implications for a diagnostic problem," PLOS Computational Biology, Public Library of Science, vol. 16(6), pages 1-17, June.
    • Handle: RePEc:plo:pcbi00:1007889
    DOI: 10.1371/journal.pcbi.1007889
    as

    Download full text from publisher

    File URL: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1007889
    Download Restriction: no
    File URL: https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1007889&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pcbi.1007889?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. Angélique O J Cramer & Claudia D van Borkulo & Erik J Giltay & Han L J van der Maas & Kenneth S Kendler & Marten Scheffer & Denny Borsboom, 2016. "Major Depression as a Complex Dynamic System," PLOS ONE, Public Library of Science, vol. 11(12), pages 1-20, December.
    2. Fabian Fröhlich & Barbara Kaltenbacher & Fabian J Theis & Jan Hasenauer, 2017. "Scalable Parameter Estimation for Genome-Scale Biochemical Reaction Networks," PLOS Computational Biology, Public Library of Science, vol. 13(1), pages 1-18, January.
    3. Ankit Gupta & Corentin Briat & Mustafa Khammash, 2014. "A Scalable Computational Framework for Establishing Long-Term Behavior of Stochastic Reaction Networks," PLOS Computational Biology, Public Library of Science, vol. 10(6), pages 1-16, June.
    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. Ankit Gupta & Mustafa Khammash, 2022. "Frequency spectra and the color of cellular noise," Nature Communications, Nature, vol. 13(1), pages 1-18, December.
    2. Zhou, Jianhua & Zhang, Lulu & Gong, Xue, 2023. "Longitudinal network relations between symptoms of problematic internet game use and internalizing and externalizing problems among Chinese early adolescents," Social Science & Medicine, Elsevier, vol. 333(C).
    3. Nadja Bodner & Laura Bringmann & Francis Tuerlinckx & Peter Jonge & Eva Ceulemans, 2022. "ConNEcT: A Novel Network Approach for Investigating the Co-occurrence of Binary Psychopathological Symptoms Over Time," Psychometrika, Springer;The Psychometric Society, vol. 87(1), pages 107-132, March.
    4. Sungho Shin & Ophelia S Venturelli & Victor M Zavala, 2019. "Scalable nonlinear programming framework for parameter estimation in dynamic biological system models," PLOS Computational Biology, Public Library of Science, vol. 15(3), pages 1-29, March.
    5. Cappelletti, Daniele & Pal Majumder, Abhishek & Wiuf, Carsten, 2021. "The dynamics of stochastic mono-molecular reaction systems in stochastic environments," Stochastic Processes and their Applications, Elsevier, vol. 137(C), pages 106-148.
    6. Miguel Ángel Castellanos & Berta Ausín & Sara Bestea & Clara González-Sanguino & Manuel Muñoz, 2020. "A Network Analysis of Major Depressive Disorder Symptoms and Age- and Gender-Related Differences in People over 65 in a Madrid Community Sample (Spain)," IJERPH, MDPI, vol. 17(23), pages 1-13, December.
    7. María Guillot-Valdés & Alejandro Guillén-Riquelme & Juan Carlos Sierra & Gualberto Buela-Casal, 2022. "Network and Exploratory Factorial Analysis of the Depression Clinical Evaluation Test," IJERPH, MDPI, vol. 19(17), pages 1-26, August.
    8. Inken Höller & Dajana Schreiber & Fionneke Bos & Thomas Forkmann & Tobias Teismann & Jürgen Margraf, 2022. "The Mereology of Depression—Networks of Depressive Symptoms during the Course of Psychotherapy," IJERPH, MDPI, vol. 19(12), pages 1-13, June.
    9. M. Marsman & K. Huth & L. J. Waldorp & I. Ntzoufras, 2022. "Objective Bayesian Edge Screening and Structure Selection for Ising Networks," Psychometrika, Springer;The Psychometric Society, vol. 87(1), pages 47-82, March.
    10. Srebrenka Letina & Tessa F. Blanken & Marie K. Deserno & Denny Borsboom, 2019. "Expanding Network Analysis Tools in Psychological Networks: Minimal Spanning Trees, Participation Coefficients, and Motif Analysis Applied to a Network of 26 Psychological Attributes," Complexity, Hindawi, vol. 2019, pages 1-27, February.
    11. Protzko, John & Colom, Roberto, 2021. "A new beginning of intelligence research. Designing the playground," Intelligence, Elsevier, vol. 87(C).
    12. Leonard Schmiester & Yannik Schälte & Frank T Bergmann & Tacio Camba & Erika Dudkin & Janine Egert & Fabian Fröhlich & Lara Fuhrmann & Adrian L Hauber & Svenja Kemmer & Polina Lakrisenko & Carolin Loo, 2021. "PEtab—Interoperable specification of parameter estimation problems in systems biology," PLOS Computational Biology, Public Library of Science, vol. 17(1), pages 1-10, January.
    13. Savi, Alexander O. & Marsman, Maarten & van der Maas, Han L.J., 2021. "Evolving networks of human intelligence," Intelligence, Elsevier, vol. 88(C).
    14. Daniel J Lugar & Ganesh Sriram, 2022. "Isotope-assisted metabolic flux analysis as an equality-constrained nonlinear program for improved scalability and robustness," PLOS Computational Biology, Public Library of Science, vol. 18(3), pages 1-26, March.
    15. Cemal Erdem & Arnab Mutsuddy & Ethan M. Bensman & William B. Dodd & Michael M. Saint-Antoine & Mehdi Bouhaddou & Robert C. Blake & Sean M. Gross & Laura M. Heiser & F. Alex Feltus & Marc R. Birtwistle, 2022. "A scalable, open-source implementation of a large-scale mechanistic model for single cell proliferation and death signaling," Nature Communications, Nature, vol. 13(1), pages 1-18, December.

    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:pcbi00:1007889. 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.