Data/hora: 10/06/2024 16:14:25

Provedor de dados: | 5
País: | France
Título: | Parameter Estimation for Dynamic Resource Allocation in Microorganisms: A Bi-level Optimization Problem
Autores: | Mairet, Francis; Bayen, Térence.
Data: | 2020
Ano: | 2020
Palavras-chave: | Bi-level optimization; Optimal control; Pontryagin’s principle; Chattering; Microbial growth; Microalgae.
Resumo: | Given their key roles in almost all ecosystems and in several industries, understanding and predicting microorganism growth is of utmost importance. In compliance with evolutionary principles, coarse-grained or genome-scale models of microbial growth can be used to determine optimal resource allocation scheme under dynamic environmental conditions. Resource allocation approaches have given important qualitative results, but it still remains a gap towards quantitiative predictions. The first step in this direction is parameter calibration with experimental data. But fitting these models results in a bi-level optimization problem, whose numerical resolution involves complex optimization issues. As a case study, we present here a coarse-grained model describing how microalgae acclimate to a change in light intensity. We first determine using the Pontryagin maximum principle and numerical simulations the optimal strategy, corresponding to a turnpike with a chattering arc. Then, a bi-level optimization problem is proposed to calibrate the model with experimental data. To solve it, a classical parameter identification routine is used, calling at each iteration the bocop solver to solve the optimal control problem (by a direct method). The calibrated model is able to represent the photoacclimation dynamics of the microalga Dunaliella tertiolecta facing a down-shift of light intensity.
Tipo: | Text
Idioma: | Inglês

Identificador:

https://archimer.ifremer.fr/doc/00690/80163/83228.pdf

DOI:10.1016/j.ifacol.2020.12.1163

https://archimer.ifremer.fr/doc/00690/80163/

Editor: | Elsevier BV
Formato: | application/pdf
Fonte: | IFAC-PapersOnLine (24058963) (Elsevier BV), 2020 , Vol. 53 , N. 2 , P. 16814-16819

Fonte:

info:eu-repo/semantics/openAccess

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