Computational Modelling and Big Data Analysis of Flow and Drug Transport in Microfluidic Systems: A Spheroid-on-a-Chip Study

Microfluidic tumour spheroid-on-a-chip platforms enable control of spheroid size and their microenvironment and offer the capability of high-throughput drug screening, but drug supply to spheroids is a complex process that depends on a combination of mechanical, biochemical, and biophysical factors....

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Autores principales:	Sina Kheiri, Eugenia Kumacheva, Edmond W.K. Young
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	organ-on-a-chip cancer spheroids drug delivery full-factorial experiments microfluidic design space exploration hierarchical clustering Biotechnology TP248.13-248.65
Acceso en línea:	https://doaj.org/article/23498f24b5724a42837b7ee65db7a973
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spelling	oai:doaj.org-article:23498f24b5724a42837b7ee65db7a9732021-11-30T12:47:18ZComputational Modelling and Big Data Analysis of Flow and Drug Transport in Microfluidic Systems: A Spheroid-on-a-Chip Study2296-418510.3389/fbioe.2021.781566https://doaj.org/article/23498f24b5724a42837b7ee65db7a9732021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fbioe.2021.781566/fullhttps://doaj.org/toc/2296-4185Microfluidic tumour spheroid-on-a-chip platforms enable control of spheroid size and their microenvironment and offer the capability of high-throughput drug screening, but drug supply to spheroids is a complex process that depends on a combination of mechanical, biochemical, and biophysical factors. To account for these coupled effects, many microfluidic device designs and operating conditions must be considered and optimized in a time- and labour-intensive trial-and-error process. Computational modelling facilitates a systematic exploration of a large design parameter space via in silico simulations, but the majority of in silico models apply only a small set of conditions or parametric levels. Novel approaches to computational modelling are needed to explore large parameter spaces and accelerate the optimization of spheroid-on-a-chip and other organ-on-a-chip designs. Here, we report an efficient computational approach for simulating fluid flow and transport of drugs in a high-throughput arrayed cancer spheroid-on-a-chip platform. Our strategy combines four key factors: i) governing physical equations; ii) parametric sweeping; iii) parallel computing; and iv) extensive dataset analysis, thereby enabling a complete “full-factorial” exploration of the design parameter space in combinatorial fashion. The simulations were conducted in a time-efficient manner without requiring massive computational time. As a case study, we simulated >15,000 microfluidic device designs and flow conditions for a representative multicellular spheroids-on-a-chip arrayed device, thus acquiring a single dataset consisting of ∼10 billion datapoints in ∼95 GBs. To validate our computational model, we performed physical experiments in a representative spheroid-on-a-chip device that showed excellent agreement between experimental and simulated data. This study offers a computational strategy to accelerate the optimization of microfluidic device designs and provide insight on the flow and drug transport in spheroid-on-a-chip and other biomicrofluidic platforms.Sina KheiriEugenia KumachevaEugenia KumachevaEdmond W.K. YoungEdmond W.K. YoungFrontiers Media S.A.articleorgan-on-a-chipcancer spheroidsdrug deliveryfull-factorial experimentsmicrofluidic design space explorationhierarchical clusteringBiotechnologyTP248.13-248.65ENFrontiers in Bioengineering and Biotechnology, Vol 9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	organ-on-a-chip cancer spheroids drug delivery full-factorial experiments microfluidic design space exploration hierarchical clustering Biotechnology TP248.13-248.65
spellingShingle	organ-on-a-chip cancer spheroids drug delivery full-factorial experiments microfluidic design space exploration hierarchical clustering Biotechnology TP248.13-248.65 Sina Kheiri Eugenia Kumacheva Eugenia Kumacheva Edmond W.K. Young Edmond W.K. Young Computational Modelling and Big Data Analysis of Flow and Drug Transport in Microfluidic Systems: A Spheroid-on-a-Chip Study
description	Microfluidic tumour spheroid-on-a-chip platforms enable control of spheroid size and their microenvironment and offer the capability of high-throughput drug screening, but drug supply to spheroids is a complex process that depends on a combination of mechanical, biochemical, and biophysical factors. To account for these coupled effects, many microfluidic device designs and operating conditions must be considered and optimized in a time- and labour-intensive trial-and-error process. Computational modelling facilitates a systematic exploration of a large design parameter space via in silico simulations, but the majority of in silico models apply only a small set of conditions or parametric levels. Novel approaches to computational modelling are needed to explore large parameter spaces and accelerate the optimization of spheroid-on-a-chip and other organ-on-a-chip designs. Here, we report an efficient computational approach for simulating fluid flow and transport of drugs in a high-throughput arrayed cancer spheroid-on-a-chip platform. Our strategy combines four key factors: i) governing physical equations; ii) parametric sweeping; iii) parallel computing; and iv) extensive dataset analysis, thereby enabling a complete “full-factorial” exploration of the design parameter space in combinatorial fashion. The simulations were conducted in a time-efficient manner without requiring massive computational time. As a case study, we simulated >15,000 microfluidic device designs and flow conditions for a representative multicellular spheroids-on-a-chip arrayed device, thus acquiring a single dataset consisting of ∼10 billion datapoints in ∼95 GBs. To validate our computational model, we performed physical experiments in a representative spheroid-on-a-chip device that showed excellent agreement between experimental and simulated data. This study offers a computational strategy to accelerate the optimization of microfluidic device designs and provide insight on the flow and drug transport in spheroid-on-a-chip and other biomicrofluidic platforms.
format	article
author	Sina Kheiri Eugenia Kumacheva Eugenia Kumacheva Edmond W.K. Young Edmond W.K. Young
author_facet	Sina Kheiri Eugenia Kumacheva Eugenia Kumacheva Edmond W.K. Young Edmond W.K. Young
author_sort	Sina Kheiri
title	Computational Modelling and Big Data Analysis of Flow and Drug Transport in Microfluidic Systems: A Spheroid-on-a-Chip Study
title_short	Computational Modelling and Big Data Analysis of Flow and Drug Transport in Microfluidic Systems: A Spheroid-on-a-Chip Study
title_full	Computational Modelling and Big Data Analysis of Flow and Drug Transport in Microfluidic Systems: A Spheroid-on-a-Chip Study
title_fullStr	Computational Modelling and Big Data Analysis of Flow and Drug Transport in Microfluidic Systems: A Spheroid-on-a-Chip Study
title_full_unstemmed	Computational Modelling and Big Data Analysis of Flow and Drug Transport in Microfluidic Systems: A Spheroid-on-a-Chip Study
title_sort	computational modelling and big data analysis of flow and drug transport in microfluidic systems: a spheroid-on-a-chip study
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/23498f24b5724a42837b7ee65db7a973
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Computational Modelling and Big Data Analysis of Flow and Drug Transport in Microfluidic Systems: A Spheroid-on-a-Chip Study

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