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

View Article: PubMed Central - PubMed

ABSTRACT

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.

No MeSH data available.


Distribute vs lumped parameter modelsTop: A 8-unit bioreactor configuration, showing details of a 2-unit example used for the development of the lumped parameter model (top panels). Bottom: A sketch of a multi-unit bioreactor configuration with heterogeneous unit design in a generic sequence of units, where different unit designs are denoted with letters A, B, C.
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pone.0162774.g004: Distribute vs lumped parameter modelsTop: A 8-unit bioreactor configuration, showing details of a 2-unit example used for the development of the lumped parameter model (top panels). Bottom: A sketch of a multi-unit bioreactor configuration with heterogeneous unit design in a generic sequence of units, where different unit designs are denoted with letters A, B, C.

Mentions: Another strategy for determining a lumped parameter model of a multi-unit configuration emerges observing that units are combined in sequence (see Fig 4). Consequently, we conjecture that the behavior of the n-unit bioreactor is the composition of n-unit models. As an example, for a sequence of two units we posit that the input/output relation for flow rates is/Q′out2Q′out1/=M˜/Qin2Qin1/;/Q′out2Q′out1/=M˜1/Q′in2Q′in1/;/Q′in2Q′in1/=M˜2/Qin2Qin1/(22)


Distributed and Lumped Parameter Models for the Characterization of High Throughput Bioreactors
Distribute vs lumped parameter modelsTop: A 8-unit bioreactor configuration, showing details of a 2-unit example used for the development of the lumped parameter model (top panels). Bottom: A sketch of a multi-unit bioreactor configuration with heterogeneous unit design in a generic sequence of units, where different unit designs are denoted with letters A, B, C.
© Copyright Policy
Related In: Results  -  Collection

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Show All Figures
getmorefigures.php?uid=PMC5036894&req=5

pone.0162774.g004: Distribute vs lumped parameter modelsTop: A 8-unit bioreactor configuration, showing details of a 2-unit example used for the development of the lumped parameter model (top panels). Bottom: A sketch of a multi-unit bioreactor configuration with heterogeneous unit design in a generic sequence of units, where different unit designs are denoted with letters A, B, C.
Mentions: Another strategy for determining a lumped parameter model of a multi-unit configuration emerges observing that units are combined in sequence (see Fig 4). Consequently, we conjecture that the behavior of the n-unit bioreactor is the composition of n-unit models. As an example, for a sequence of two units we posit that the input/output relation for flow rates is/Q′out2Q′out1/=M˜/Qin2Qin1/;/Q′out2Q′out1/=M˜1/Q′in2Q′in1/;/Q′in2Q′in1/=M˜2/Qin2Qin1/(22)

View Article: PubMed Central - PubMed

ABSTRACT

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.

No MeSH data available.