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Distributed and Lumped Parameter Models for the Characterization of High Throughput Bioreactors

Next generation bioreactors are being developed to generate multiple human cell-based tissue analogs within the same fluidic system, to better recapitulate the complexity and interconnection of human physiology [1, 2]. The effective development of these devices requires a solid understanding of thei...

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Autores principales: Iannetti, Laura, D’Urso, Giovanna, Conoscenti, Gioacchino, Cutrì, Elena, Tuan, Rocky S., Raimondi, Manuela T., Gottardi, Riccardo, Zunino, Paolo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5036894/
https://www.ncbi.nlm.nih.gov/pubmed/27669413
http://dx.doi.org/10.1371/journal.pone.0162774
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author Iannetti, Laura
D’Urso, Giovanna
Conoscenti, Gioacchino
Cutrì, Elena
Tuan, Rocky S.
Raimondi, Manuela T.
Gottardi, Riccardo
Zunino, Paolo
author_facet Iannetti, Laura
D’Urso, Giovanna
Conoscenti, Gioacchino
Cutrì, Elena
Tuan, Rocky S.
Raimondi, Manuela T.
Gottardi, Riccardo
Zunino, Paolo
author_sort Iannetti, Laura
collection PubMed
description Next generation bioreactors are being developed to generate multiple human cell-based tissue analogs within the same fluidic system, to better recapitulate the complexity and interconnection of human physiology [1, 2]. The effective development of these devices requires a solid understanding of their interconnected fluidics, to predict the transport of nutrients and waste through the constructs and improve the design accordingly. In this work, we focus on a specific model of bioreactor, with multiple input/outputs, aimed at generating osteochondral constructs, i.e., a biphasic construct in which one side is cartilaginous in nature, while the other is osseous. We next develop a general computational approach to model the microfluidics of a multi-chamber, interconnected system that may be applied to human-on-chip devices. This objective requires overcoming several challenges at the level of computational modeling. The main one consists of addressing the multi-physics nature of the problem that combines free flow in channels with hindered flow in porous media. Fluid dynamics is also coupled with advection-diffusion-reaction equations that model the transport of biomolecules throughout the system and their interaction with living tissues and C constructs. Ultimately, we aim at providing a predictive approach useful for the general organ-on-chip community. To this end, we have developed a lumped parameter approach that allows us to analyze the behavior of multi-unit bioreactor systems with modest computational effort, provided that the behavior of a single unit can be fully characterized.
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spelling pubmed-50368942016-10-27 Distributed and Lumped Parameter Models for the Characterization of High Throughput Bioreactors Iannetti, Laura D’Urso, Giovanna Conoscenti, Gioacchino Cutrì, Elena Tuan, Rocky S. Raimondi, Manuela T. Gottardi, Riccardo Zunino, Paolo PLoS One Research Article Next generation bioreactors are being developed to generate multiple human cell-based tissue analogs within the same fluidic system, to better recapitulate the complexity and interconnection of human physiology [1, 2]. The effective development of these devices requires a solid understanding of their interconnected fluidics, to predict the transport of nutrients and waste through the constructs and improve the design accordingly. In this work, we focus on a specific model of bioreactor, with multiple input/outputs, aimed at generating osteochondral constructs, i.e., a biphasic construct in which one side is cartilaginous in nature, while the other is osseous. We next develop a general computational approach to model the microfluidics of a multi-chamber, interconnected system that may be applied to human-on-chip devices. This objective requires overcoming several challenges at the level of computational modeling. The main one consists of addressing the multi-physics nature of the problem that combines free flow in channels with hindered flow in porous media. Fluid dynamics is also coupled with advection-diffusion-reaction equations that model the transport of biomolecules throughout the system and their interaction with living tissues and C constructs. Ultimately, we aim at providing a predictive approach useful for the general organ-on-chip community. To this end, we have developed a lumped parameter approach that allows us to analyze the behavior of multi-unit bioreactor systems with modest computational effort, provided that the behavior of a single unit can be fully characterized. Public Library of Science 2016-09-26 /pmc/articles/PMC5036894/ /pubmed/27669413 http://dx.doi.org/10.1371/journal.pone.0162774 Text en © 2016 Iannetti et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Iannetti, Laura
D’Urso, Giovanna
Conoscenti, Gioacchino
Cutrì, Elena
Tuan, Rocky S.
Raimondi, Manuela T.
Gottardi, Riccardo
Zunino, Paolo
Distributed and Lumped Parameter Models for the Characterization of High Throughput Bioreactors
title Distributed and Lumped Parameter Models for the Characterization of High Throughput Bioreactors
title_full Distributed and Lumped Parameter Models for the Characterization of High Throughput Bioreactors
title_fullStr Distributed and Lumped Parameter Models for the Characterization of High Throughput Bioreactors
title_full_unstemmed Distributed and Lumped Parameter Models for the Characterization of High Throughput Bioreactors
title_short Distributed and Lumped Parameter Models for the Characterization of High Throughput Bioreactors
title_sort distributed and lumped parameter models for the characterization of high throughput bioreactors
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5036894/
https://www.ncbi.nlm.nih.gov/pubmed/27669413
http://dx.doi.org/10.1371/journal.pone.0162774
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