# Graph Network-Based Simulation of Multicellular Dynamics Driven by Concentrated Polymer Brush-Modified Cellulose Nanofibers

https://mdr.nims.go.jp/datasets/7771ddaf-2dfc-4e5f-b4ff-b0a5abbc324e

## Files

- [Manuscript.pdf](https://mdr.nims.go.jp/filesets/d082ac50-c103-4650-8d05-fc0bfd129600/download) ([Detail](https://mdr.nims.go.jp/filesets/d082ac50-c103-4650-8d05-fc0bfd129600.md))

## Id

7771ddaf-2dfc-4e5f-b4ff-b0a5abbc324e

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2024-08-26T06:27:11.953345Z

## Updated at

2024-08-26T23:30:31.159772Z

## Published at

2024-08-26T23:30:31.275191Z

## Doi

https://doi.org/10.48505/nims.4691

## First published url

https://doi.org/10.1021/acsbiomaterials.3c01888

## Date published

2024-04-08

## Recorded date published

2024-4-8

## Resource type

journal_article

## Manuscript type

authors_original

## Collection



## Title

- title: Graph Network-Based Simulation of Multicellular Dynamics Driven by Concentrated
    Polymer Brush-Modified Cellulose Nanofibers
  title_type: original
  lang: en

## Description

- description: Manipulating the three-dimensional (3D) structures of cells is important
    for facilitating to repair or regenerate tissues. A self-assembly system of cells
    with cellulose nanofibers (CNFs) and concentrated polymer brushes (CPBs) has been
    developed to fabricate various cell 3D structures. To further generate tissues
    at an implantable level, it is necessary to carry out a large number of experiments
    using different cell culture conditions and material properties; however this
    is practically intractable. To address this issue, we present a graph neural network-based
    simulator (GNS) that can be trained by using assembly process images to predict
    the assembly status of future time steps. A total of 24 (25 steps) time-series
    images were recorded (four repeats for each of six different conditions), and
    each image was transformed into a graph by regarding the cells as nodes and the
    connecting neighboring cells as edges. Using the obtained data, the performances
    of the GNS were examined under three scenarios (i.e. changing a pair of the training
    and testing data) to verify the possibility of using the GNS as a predictor for
    further time steps. It was confirmed that the GNS could reasonably reproduce the
    assembly process, even under the toughest scenario, in which the experimental
    conditions differed between the training and testing data. Practically, this means
    that the GNS trained by the first 24 hour images could predict the cell types
    obtained three weeks later. This result could reduce the number of experiments
    required to find the optimal conditions for generating cells with desired 3D structures.
    Ultimately, our approach could accelerate progress in regenerative medicine.
  description_type: abstract
  lang: und

## Creator

- name: Chiaki Yoshikawa
  role: author
  orcid: https://orcid.org/0000-0002-6589-387X
  organization: National Institute for Materials Science
- name: Duc Anh Nguyen
  role: author
- name: Tadashi Nakaji-Hirabayashi
  role: author
- name: Ichigaku Takigawa
  role: author
- name: Hiroshi Mamitsuka
  role: author

## Contact agent



## Publisher

organization: American Chemical Society (ACS)

## Managing organization



## Keyword

- subject: cellulose nanofiber
  schema: not_defined
- subject: concentrated polymer brush
  schema: not_defined
- subject: hMSC
  schema: not_defined
- subject: self-assembly
  schema: not_defined
- subject: machine learning
  schema: not_defined

## Rights

- description: This document is the unedited Author’s version of a Submitted Work
    that was subsequently accepted for publication in ACS Biomaterials Science & Engineering,
    copyright ©  2024 American Chemical Society] after peer review. To access the
    final edited and published work see https://doi.org/10.1021/acsbiomaterials.3c01888
  identifier: http://rightsstatements.org/vocab/InC/1.0/

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## Journal

- title: ACS Biomaterials Science & Engineering
  issn: '23739878'
  volume: '10'
  issue: '4'
  start_page: 2165
  end_page: 2176

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## Related item



## Funding

- identifier: JPMJCR21N7
  funder_name: Core Research for Evolutional Science and Technology
- identifier: 19H04169
  funder_name: Japan Society for the Promotion of Science
- identifier: 20F20809
  funder_name: Japan Society for the Promotion of Science
- identifier: 21H05027
  funder_name: Japan Society for the Promotion of Science
- identifier: 22H02133
  funder_name: Japan Society for the Promotion of Science
- identifier: 22H03645
  funder_name: Japan Society for the Promotion of Science
- funder_name: NIMS Joint Research Hub Program

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## Fileset

- id: d082ac50-c103-4650-8d05-fc0bfd129600
  filename: Manuscript.pdf
  content_type: application/pdf
  size: 2527184
  md5: 6ebc2cdae815dd1366e71da8d8426cf7

## Thumbnail

fileset_id: d082ac50-c103-4650-8d05-fc0bfd129600
filename: Manuscript.pdf