The production of cyanophycin by photosynthetic microorganisms, as a high-value bio-based compound, is getting increasing interest. The aim of this work is to maximize the production of this compound by the cyanobacterium Synechocystis sp. in semi-batch cultivation systems, by applying a data-driven modeling approach based on the Design of Dynamic Experiments (DoDE) and Response Surface Model (RSM) methodologies. A first set of experiments, carried out inside an initially defined domain, was used to find a preliminary RSM model describing cyanophycin concentration as a function of incident light intensity profile, temperature, and phosphorus supply profile. The model was then improved, according to an evolutionary optimization approach, by carrying out additional experiments in a modified domain, exploiting information derived by the initial model. The updated model was used to identify the optimal operating conditions resulting in maximum cyanophycin concentration at the end of the batch. The cyanophycin concentration found experimentally (228.2 ± 20.0 mg/L) in these conditions fell within the confidence interval of the model prediction. Remarkably, this experimentally obtained value represents a significant (about 20 %) increase in the cyanophycin production with respect to the highest value found in the experiments before the optimization step (184.3 ± 0.8 mg/L).

Using the design of dynamic experiments to optimize photosynthetic cyanophycin production by Synechocystis sp

Trentin G.;Bertucco A.;Sforza E.;Barbera E.
2023

Abstract

The production of cyanophycin by photosynthetic microorganisms, as a high-value bio-based compound, is getting increasing interest. The aim of this work is to maximize the production of this compound by the cyanobacterium Synechocystis sp. in semi-batch cultivation systems, by applying a data-driven modeling approach based on the Design of Dynamic Experiments (DoDE) and Response Surface Model (RSM) methodologies. A first set of experiments, carried out inside an initially defined domain, was used to find a preliminary RSM model describing cyanophycin concentration as a function of incident light intensity profile, temperature, and phosphorus supply profile. The model was then improved, according to an evolutionary optimization approach, by carrying out additional experiments in a modified domain, exploiting information derived by the initial model. The updated model was used to identify the optimal operating conditions resulting in maximum cyanophycin concentration at the end of the batch. The cyanophycin concentration found experimentally (228.2 ± 20.0 mg/L) in these conditions fell within the confidence interval of the model prediction. Remarkably, this experimentally obtained value represents a significant (about 20 %) increase in the cyanophycin production with respect to the highest value found in the experiments before the optimization step (184.3 ± 0.8 mg/L).
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/3469594
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