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Modeling Living Cells Within Microfluidic Systems Using Cellular Automata Models

Several computational models, both continuum and discrete, allow for the simulation of collective cell behaviors in connection with challenges linked to disease modeling and understanding. Normally, discrete cell modelling employs quasi-infinite or boundary-less 2D lattices, hence modeling collectiv...

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Autores principales: Ballesteros Hernando, Julia, Ramos Gómez, Milagros, Díaz Lantada, Andrés
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6797720/
https://www.ncbi.nlm.nih.gov/pubmed/31624307
http://dx.doi.org/10.1038/s41598-019-51494-1
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author Ballesteros Hernando, Julia
Ramos Gómez, Milagros
Díaz Lantada, Andrés
author_facet Ballesteros Hernando, Julia
Ramos Gómez, Milagros
Díaz Lantada, Andrés
author_sort Ballesteros Hernando, Julia
collection PubMed
description Several computational models, both continuum and discrete, allow for the simulation of collective cell behaviors in connection with challenges linked to disease modeling and understanding. Normally, discrete cell modelling employs quasi-infinite or boundary-less 2D lattices, hence modeling collective cell behaviors in Petri dish-like environments. The advent of lab- and organ-on-a-chip devices proves that the information obtained from 2D cell cultures, upon Petri dishes, differs importantly from the results obtained in more biomimetic micro-fluidic environments, made of interconnected chambers and channels. However, discrete cell modelling within lab- and organ-on-a-chip devices, to our knowledge, is not yet found in the literature, although it may prove useful for designing and optimizing these types of systems. Consequently, in this study we focus on the establishment of a direct connection between the computer-aided designs (CAD) of microfluidic systems, especially labs- and organs-on-chips (and their multi-chamber and multi-channel structures), and the lattices for discrete cell modeling approaches aimed at the simulation of collective cell interactions, whose boundaries are defined directly from the CAD models. We illustrate the proposal using a quite straightforward cellular automata model, apply it to simulating cells with different growth rates, within a selected set of microsystem designs, and validate it by tuning the growth rates with the support of cell culture experiments and by checking the results with a real microfluidic system.
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spelling pubmed-67977202019-10-25 Modeling Living Cells Within Microfluidic Systems Using Cellular Automata Models Ballesteros Hernando, Julia Ramos Gómez, Milagros Díaz Lantada, Andrés Sci Rep Article Several computational models, both continuum and discrete, allow for the simulation of collective cell behaviors in connection with challenges linked to disease modeling and understanding. Normally, discrete cell modelling employs quasi-infinite or boundary-less 2D lattices, hence modeling collective cell behaviors in Petri dish-like environments. The advent of lab- and organ-on-a-chip devices proves that the information obtained from 2D cell cultures, upon Petri dishes, differs importantly from the results obtained in more biomimetic micro-fluidic environments, made of interconnected chambers and channels. However, discrete cell modelling within lab- and organ-on-a-chip devices, to our knowledge, is not yet found in the literature, although it may prove useful for designing and optimizing these types of systems. Consequently, in this study we focus on the establishment of a direct connection between the computer-aided designs (CAD) of microfluidic systems, especially labs- and organs-on-chips (and their multi-chamber and multi-channel structures), and the lattices for discrete cell modeling approaches aimed at the simulation of collective cell interactions, whose boundaries are defined directly from the CAD models. We illustrate the proposal using a quite straightforward cellular automata model, apply it to simulating cells with different growth rates, within a selected set of microsystem designs, and validate it by tuning the growth rates with the support of cell culture experiments and by checking the results with a real microfluidic system. Nature Publishing Group UK 2019-10-17 /pmc/articles/PMC6797720/ /pubmed/31624307 http://dx.doi.org/10.1038/s41598-019-51494-1 Text en © The Author(s) 2019 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Ballesteros Hernando, Julia
Ramos Gómez, Milagros
Díaz Lantada, Andrés
Modeling Living Cells Within Microfluidic Systems Using Cellular Automata Models
title Modeling Living Cells Within Microfluidic Systems Using Cellular Automata Models
title_full Modeling Living Cells Within Microfluidic Systems Using Cellular Automata Models
title_fullStr Modeling Living Cells Within Microfluidic Systems Using Cellular Automata Models
title_full_unstemmed Modeling Living Cells Within Microfluidic Systems Using Cellular Automata Models
title_short Modeling Living Cells Within Microfluidic Systems Using Cellular Automata Models
title_sort modeling living cells within microfluidic systems using cellular automata models
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6797720/
https://www.ncbi.nlm.nih.gov/pubmed/31624307
http://dx.doi.org/10.1038/s41598-019-51494-1
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