IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v486y2017icp587-594.html

Analysis of the fluctuations of the tumour/host interface

Author

Listed:
  • Milotti, Edoardo
  • Vyshemirsky, Vladislav
  • Stella, Sabrina
  • Dogo, Federico
  • Chignola, Roberto

Abstract

In a recent analysis of metabolic scaling in solid tumours we found a scaling law that interpolates between the power laws μ∝V and μ∝V2∕3, where μ is the metabolic rate expressed as the glucose absorption rate and V is the tumour volume. The scaling law fits quite well both in vitro and in vivo data, however we also observed marked fluctuations that are associated with the specific biological properties of individual tumours. Here we analyse these fluctuations, in an attempt to find the population-wide distribution of an important parameter (A) which expresses the total extent of the interface between the solid tumour and the non-cancerous environment. Heuristic considerations suggest that the values of the A parameter follow a lognormal distribution, and, allowing for the large uncertainties of the experimental data, our statistical analysis confirms this.

Suggested Citation

  • Milotti, Edoardo & Vyshemirsky, Vladislav & Stella, Sabrina & Dogo, Federico & Chignola, Roberto, 2017. "Analysis of the fluctuations of the tumour/host interface," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 486(C), pages 587-594.
    • Handle: RePEc:eee:phsmap:v:486:y:2017:i:c:p:587-594
    DOI: 10.1016/j.physa.2017.06.005
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437117306441
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2017.06.005?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
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Tom Kolokotrones & Van Savage & Eric J. Deeds & Walter Fontana, 2010. "Curvature in metabolic scaling," Nature, Nature, vol. 464(7289), pages 753-756, April.
    2. Geoffrey B. West & James H. Brown & Brian J. Enquist, 1997. "A General Model for the Origin of Allometric Scaling Laws in Biology," Working Papers 97-03-019, Santa Fe Institute.
    3. Alexandre Albanese & Alan K. Lam & Edward A. Sykes & Jonathan V. Rocheleau & Warren C.W. Chan, 2013. "Tumour-on-a-chip provides an optical window into nanoparticle tissue transport," Nature Communications, Nature, vol. 4(1), pages 1-8, December.
    4. N. Friel & A. N. Pettitt, 2008. "Marginal likelihood estimation via power posteriors," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(3), pages 589-607, 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. Xu, Meng & Jiang, Mengke & Wang, Hua-Feng, 2021. "Integrating metabolic scaling variation into the maximum entropy theory of ecology explains Taylor's law for individual metabolic rate in tropical forests," Ecological Modelling, Elsevier, vol. 455(C).
    2. Michail Fragkias & José Lobo & Deborah Strumsky & Karen C Seto, 2013. "Does Size Matter? Scaling of CO 2 Emissions and U.S. Urban Areas," PLOS ONE, Public Library of Science, vol. 8(6), pages 1-8, June.
    3. Chen, Shi & Bao, Forrest Sheng, 2015. "Linking body size and energetics with predation strategies: A game theoretic modeling framework," Ecological Modelling, Elsevier, vol. 316(C), pages 81-86.
    4. Mitchell G Newberry & Daniel B Ennis & Van M Savage, 2015. "Testing Foundations of Biological Scaling Theory Using Automated Measurements of Vascular Networks," PLOS Computational Biology, Public Library of Science, vol. 11(8), pages 1-18, August.
    5. Witting, Lars, 2017. "The natural selection of metabolism and mass selects allometric transitions from prokaryotes to mammals," Theoretical Population Biology, Elsevier, vol. 117(C), pages 23-42.
    6. Elif Tekin & David Hunt & Mitchell G Newberry & Van M Savage, 2016. "Do Vascular Networks Branch Optimally or Randomly across Spatial Scales?," PLOS Computational Biology, Public Library of Science, vol. 12(11), pages 1-28, November.
    7. Hsiehchen, David & Espinoza, Magdalena & Hsieh, Antony, 2016. "Hypoallometric scaling in international collaborations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 444(C), pages 188-193.
    8. Elliott, Robert J.R. & Sun, Puyang & Xu, Qiqin, 2015. "Energy distribution and economic growth: An empirical test for China," Energy Economics, Elsevier, vol. 48(C), pages 24-31.
    9. Chen, Yanguang, 2014. "An allometric scaling relation based on logistic growth of cities," Chaos, Solitons & Fractals, Elsevier, vol. 65(C), pages 65-77.
    10. Giannetti, Biagio F. & Marcilio, Maria De Fatima D.F.B. & Coscieme, Luca & Agostinho, Feni & Liu, Gengyuan & Almeida, Cecilia M.V.B., 2019. "Howard Odum’s “Self-organization, transformity and information”: Three decades of empirical evidence," Ecological Modelling, Elsevier, vol. 407(C), pages 1-1.
    11. Xing Ju Lee & Christopher C. Drovandi & Anthony N. Pettitt, 2015. "Model choice problems using approximate Bayesian computation with applications to pathogen transmission data sets," Biometrics, The International Biometric Society, vol. 71(1), pages 198-207, March.
    12. Jeong Eun Lee & Christian Robert, 2013. "Imortance Sampling Schemes for Evidence Approximation in Mixture Models," Working Papers 2013-42, Center for Research in Economics and Statistics.
    13. Inês Figueira & Rayan Succar & Roni Barak Ventura & Maurizio Porfiri, 2025. "Urban scaling with censored data," PLOS Complex Systems, Public Library of Science, vol. 2(1), pages 1-22, January.
    14. repec:plo:pcbi00:1000486 is not listed on IDEAS
    15. Wang, Cheng-Jun & Wu, Lingfei, 2016. "The scaling of attention networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 448(C), pages 196-204.
    16. Will Penny & Biswa Sengupta, 2016. "Annealed Importance Sampling for Neural Mass Models," PLOS Computational Biology, Public Library of Science, vol. 12(3), pages 1-25, March.
    17. He, Ji-Huan & Liu, Jun-Fang, 2009. "Allometric scaling laws in biology and physics," Chaos, Solitons & Fractals, Elsevier, vol. 41(4), pages 1836-1838.
    18. Spezia, L. & Cooksley, S.L. & Brewer, M.J. & Donnelly, D. & Tree, A., 2014. "Modelling species abundance in a river by Negative Binomial hidden Markov models," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 599-614.
    19. Christos Makriyannis, 2023. "How the Biophysical Paradigm Impedes the Scientific Advancement of Ecological Economics: A Transdisciplinary Analysis," Sustainability, MDPI, vol. 15(23), pages 1-24, November.
    20. J. Sylvan Katz & Guillermo Armando Ronda-Pupo, 2019. "Cooperation, scale-invariance and complex innovation systems: a generalization," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(2), pages 1045-1065, November.
    21. Vitoratou, Silia & Ntzoufras, Ioannis & Moustaki, Irini, 2016. "Explaining the behavior of joint and marginal Monte Carlo estimators in latent variable models with independence assumptions," LSE Research Online Documents on Economics 57685, London School of Economics and Political Science, LSE Library.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    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:eee:phsmap:v:486:y:2017:i:c:p:587-594. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    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.