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Stem cell biomanufacturing under uncertainty: A case study in optimizing red blood cell production.


ABSTRACT: As breakthrough cellular therapy discoveries are translated into reliable, commercializable applications, effective stem cell biomanufacturing requires systematically developing and optimizing bioprocess design and operation. This article proposes a rigorous computational framework for stem cell biomanufacturing under uncertainty. Our mathematical tool kit incorporates: high-fidelity modeling, single variate and multivariate sensitivity analysis, global topological superstructure optimization, and robust optimization. The advantages of the proposed bioprocess optimization framework using, as a case study, a dual hollow fiber bioreactor producing red blood cells from progenitor cells were quantitatively demonstrated. The optimization phase reduces the cost by a factor of 4, and the price of insuring process performance against uncertainty is approximately 15% over the nominal optimal solution. Mathematical modeling and optimization can guide decision making; the possible commercial impact of this cellular therapy using the disruptive technology paradigm was quantitatively evaluated.

SUBMITTER: Misener R 

PROVIDER: S-EPMC6108044 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature

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Stem cell biomanufacturing under uncertainty: A case study in optimizing red blood cell production.

Misener Ruth R   Allenby Mark C MC   Fuentes-Garí María M   Gupta Karan K   Wiggins Thomas T   Panoskaltsis Nicki N   Pistikopoulos Efstratios N EN   Mantalaris Athanasios A  

AIChE journal. American Institute of Chemical Engineers 20171207 8


As breakthrough cellular therapy discoveries are translated into reliable, commercializable applications, effective stem cell biomanufacturing requires systematically developing and optimizing bioprocess design and operation. This article proposes a rigorous computational framework for stem cell biomanufacturing under uncertainty. Our mathematical tool kit incorporates: high-fidelity modeling, single variate and multivariate sensitivity analysis, global topological superstructure optimization, a  ...[more]

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