IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v635y2024ics0378437124000190.html
   My bibliography  Save this article

Modeling the evolution of membrane during cell adhesion on the nanostructured substrate

Author

Listed:
  • Jin, Kun
  • Yuan, Fang
  • Wang, Fengting
  • Zhang, Bingqi
  • Li, Nanxin
  • Chen, Tongsheng
  • Li, Xinlei

Abstract

Using nanostructured substrates for enhancing cell adhesion is an important method. However, the physical mechanism of cell adhesion on nanostructured substrate is not well understood yet, particularly the evolution process of membrane during cell adhesion. Here, we propose an analytical model to study quantitatively the evolution of membrane during adhesion process on the nanostructured substrate. The effects of substrate surface topography, receptor-ligand binding, substrate property and membrane property were investigated. We found a small nanopillar radius, low nanopillar density, large binding rate, large receptor density, low non-specific energy, or large stretching modulus can result in a large adhesion rate and a large adhesion depth. Besides, the evolution of membrane on truncated nanocone arrays was also studied by the model. The theoretical results are in good agreement with the experimental observations, which implied that our studies can provide a useful guide to control cell adhesion process and cell motion on nanostructured substrates.

Suggested Citation

  • Jin, Kun & Yuan, Fang & Wang, Fengting & Zhang, Bingqi & Li, Nanxin & Chen, Tongsheng & Li, Xinlei, 2024. "Modeling the evolution of membrane during cell adhesion on the nanostructured substrate," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 635(C).
    • Handle: RePEc:eee:phsmap:v:635:y:2024:i:c:s0378437124000190
    DOI: 10.1016/j.physa.2024.129511
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437124000190
    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.2024.129511?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.

    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:635:y:2024:i:c:s0378437124000190. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.