Maximizing neotissue growth kinetics in a perfusion bioreactor: An in silico strategy using model reduction and Bayesian optimization

Mehrian, Mohammad; Guyot, Y.; Papantoniou, I.; Olofsson, S.; Sonnaert, M.; Misener, R.; Geris, Liesbet

doi:10.1002/bit.26500

Article (Scientific journals)

Maximizing neotissue growth kinetics in a perfusion bioreactor: An in silico strategy using model reduction and Bayesian optimization

Mehrian, Mohammad; Guyot, Y.; Papantoniou, I. et al.

2017 • In Biotechnology and Bioengineering

Peer Reviewed verified by ORBi

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https://hdl.handle.net/2268/218960

DOI
10.1002/bit.26500

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29205280

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Keywords :

Bayesian optimization; Bone tissue engineering; Computational model; Shear stress; Three dimensional computer graphics; Tissue engineering; Bioprocess optimization; Extracellular matrices; Optimization techniques

Abstract :

[en] In regenerative medicine, computer models describing bioreactor processes can assist in designing optimal process conditions leading to robust and economically viable products. In this study, we started from a (3D) mechanistic model describing the growth of neotissue, comprised of cells, and extracellular matrix, in a perfusion bioreactor set-up influenced by the scaffold geometry, flow-induced shear stress, and a number of metabolic factors. Subsequently, we applied model reduction by reformulating the problem from a set of partial differential equations into a set of ordinary differential equations. Comparing the reduced model results to the mechanistic model results and to dedicated experimental results assesses the reduction step quality. The obtained homogenized model is 105 fold faster than the 3D version, allowing the application of rigorous optimization techniques. Bayesian optimization was applied to find the medium refreshment regime in terms of frequency and percentage of medium replaced that would maximize neotissue growth kinetics during 21 days of culture. The simulation results indicated that maximum neotissue growth will occur for a high frequency and medium replacement percentage, a finding that is corroborated by reports in the literature. This study demonstrates an in silico strategy for bioprocess optimization paying particular attention to the reduction of the associated computational cost. © 2017 Wiley Periodicals, Inc.

Disciplines :

Engineering, computing & technology: Multidisciplinary, general & others

Author, co-author :

Mehrian, Mohammad ; Université de Liège - ULiège > Département d'aérospatiale et mécanique > Génie biomécanique

Guyot, Y.; Biomechanics Research UnitGIGA In Silico MedicineUniversity of LiègeLiègeBelgium, PrometheusThe Division of Skeletal Tissue EngineeringKU LeuvenLeuvenBelgium

Papantoniou, I.; PrometheusThe Division of Skeletal Tissue EngineeringKU LeuvenLeuvenBelgium, Skeletal Biology and Engineering Research CenterKU LeuvenLeuvenBelgium

Olofsson, S.; Department of ComputingImperial College LondonLondonUnited Kingdom

Sonnaert, M.; PrometheusThe Division of Skeletal Tissue EngineeringKU LeuvenLeuvenBelgium, Department of Metallurgy and Materials EngineeringKU LeuvenLeuvenBelgium

Misener, R.; Department of ComputingImperial College LondonLondonUnited Kingdom

Geris, Liesbet ; Université de Liège - ULiège > Département d'aérospatiale et mécanique > Génie biomécanique

Language :

English

Title :

Maximizing neotissue growth kinetics in a perfusion bioreactor: An in silico strategy using model reduction and Bayesian optimization

Publication date :

04 December 2017

Journal title :

Biotechnology and Bioengineering

ISSN :

0006-3592

eISSN :

1097-0290

Publisher :

John Wiley and Sons Inc.

Peer reviewed :

Peer Reviewed verified by ORBi

Available on ORBi :

since 23 January 2018

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