| Registro completo |
| Provedor de dados: |
ArchiMer
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| País: |
France
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| Título: |
Parameter Estimation for Dynamic Resource Allocation in Microorganisms: A Bi-level Optimization Problem
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| Autores: |
Mairet, Francis
Bayen, Térence
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| Data: |
2020
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| Ano: |
2020
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| Palavras-chave: |
Bi-level optimization
Optimal control
Pontryagin’s principle
Chattering
Microbial growth
Microalgae
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| 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.
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| Tipo: |
Text
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| Idioma: |
Inglês
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| 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/
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| Editor: |
Elsevier BV
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| Formato: |
application/pdf
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| Fonte: |
IFAC-PapersOnLine (24058963) (Elsevier BV), 2020 , Vol. 53 , N. 2 , P. 16814-16819
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| Direitos: |
info:eu-repo/semantics/openAccess
restricted use
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