IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v393y2014icp320-326.html
   My bibliography  Save this article

Modeling mutant distribution in a stressed Escherichia coli bacteria population using experimental data

Author

Listed:
  • Bazzani, Armando
  • Fani, Renato
  • Freguglia, Paolo

Abstract

In this paper we propose a statistical physics approach to experimental results on bacterial mutations (Escherichia coli). We get scaling laws that describe some generic traits and suggest some features of the underlying dynamical structure for the considered evolution process. Our main assumption is that the evolution dynamics could be visualized as a random walk on a fitness landscape whose topological structure is analogous to the structure of energy landscape potentials used in Physics and Chemistry. Then we relate the generic distribution of local minima attraction basins to the number of bacterial mutations and we discuss the comparison with experimental results.

Suggested Citation

  • Bazzani, Armando & Fani, Renato & Freguglia, Paolo, 2014. "Modeling mutant distribution in a stressed Escherichia coli bacteria population using experimental data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 393(C), pages 320-326.
  • Handle: RePEc:eee:phsmap:v:393:y:2014:i:c:p:320-326
    DOI: 10.1016/j.physa.2013.08.049
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437113007991
    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.2013.08.049?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 search for a different version of it.

    References listed on IDEAS

    as
    1. Parisi, Giorgio, 1999. "Complex systems: a physicist's viewpoint," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 263(1), pages 557-564.
    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. Kiran Sharma & Subhradeep Das & Anirban Chakraborti, 2017. "Global Income Inequality and Savings: A Data Science Perspective," Papers 1801.00253, arXiv.org, revised Aug 2018.
    2. Anirban Chakraborti & Ioane Muni Toke & Marco Patriarca & Frederic Abergel, 2011. "Econophysics review: I. Empirical facts," Quantitative Finance, Taylor & Francis Journals, vol. 11(7), pages 991-1012.
    3. Imre Kondor & István Csabai & Gábor Papp & Enys Mones & Gábor Czimbalmos & Máté Sándor, 2014. "Strong random correlations in networks of heterogeneous agents," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 9(2), pages 203-232, October.
    4. Aksentijevic, A. & Mihailović, D.T. & Kapor, D. & Crvenković, S. & Nikolic-Djorić, E. & Mihailović, A., 2020. "Complementarity of information obtained by Kolmogorov and Aksentijevic–Gibson complexities in the analysis of binary time series," Chaos, Solitons & Fractals, Elsevier, vol. 130(C).
    5. Anirban Chakraborti & Ioane Muni Toke & Marco Patriarca & Frédéric Abergel, 2011. "Econophysics review: I. Empirical facts," Post-Print hal-00621058, HAL.
    6. R. Kenna & B. Berche, 2011. "Critical mass and the dependency of research quality on group size," Scientometrics, Springer;Akadémiai Kiadó, vol. 86(2), pages 527-540, February.
    7. Anirban Chakraborti & Ioane Muni Toke & Marco Patriarca & Frederic Abergel, 2011. "Econophysics review: II. Agent-based models," Quantitative Finance, Taylor & Francis Journals, vol. 11(7), pages 1013-1041.

    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:393:y:2014:i:c:p:320-326. 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.