Optimal control of bioproduction in the presence of population heterogeneity

Autor: Davin Lunz, J. Frédéric Bonnans, Jakob Ruess
Přispěvatelé: Méthodes Expérimentales et Computationnelles pour la Modélisation des Processus Cellulaires / Experimental and Computational Methods for Modeling Cellular Processes (InBio ), Centre National de la Recherche Scientifique (CNRS)-Institut Pasteur [Paris]-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Institut Pasteur [Paris], Dynamical Interconnected Systems in COmplex Environments (DISCO), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire des signaux et systèmes (L2S), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Laboratoire des signaux et systèmes (L2S), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Controle, Optimisation, modèles, Méthodes et Applications pour les Systèmes Dynamiques non linéaires (COMMANDS), Centre de Mathématiques Appliquées - Ecole Polytechnique (CMAP), École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France, InBio - Méthodes Expérimentales et Computationnelles pour la Modélisation des Processus Cellulaires / Experimental and Computational Methods for Modeling Cellular Processes (INBIO), Institut Pasteur [Paris] (IP)-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université Paris Cité (UPCité), Institut Pasteur [Paris] (IP), Institut Pasteur [Paris]-Inria de Paris
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Zdroj: Journal of Mathematical Biology
Journal of Mathematical Biology, 2023, 86 (3), pp.43. ⟨10.1007/s00285-023-01876-x⟩
ISSN: 0303-6812
1432-1416
DOI: 10.1007/s00285-023-01876-x⟩
Popis: International audience; Cell-to-cell variability, born of stochastic chemical kinetics, persists even in large isogenic populations. In the study of single-cell dynamics this is typically accounted for. However, on the population level this source of heterogeneity is often sidelined to avoid the inevitable complexity it introduces. The homogeneous models used instead are more tractable but risk disagreeing with their heterogeneous counterparts and may thus lead to severely suboptimal control of bioproduction. In this work, we introduce a comprehensive mathematical framework for solving bioproduction optimal control problems in the presence of heterogeneity. We study population-level models in which such heterogeneity is retained, and propose order-reduction approximation techniques. The reduced-order models take forms typical of homogeneous bioproduction models, making them a useful benchmark by which to study the importance of heterogeneity. Moreover, the derivation from the heterogeneous setting sheds light on parameter selection in ways a direct homogeneous outlook cannot, and reveals the source of approximation error. With view to optimally controlling bioproduction in microbial communities, we ask the question: when does optimising the reduced-order models produce strategies that work well in the presence of population heterogeneity? We show that, in some cases, homogeneous approximations provide remarkably accurate surrogate models. Nevertheless, we also demonstrate that this is not uniformly true: overlooking the heterogeneity can lead to significantly suboptimal control strategies. In these cases, the heterogeneous tools and perspective are crucial to optimise bioproduction.
Databáze: OpenAIRE
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