Metabolic pathways analysis in PHAs production by Pseudomonas with 13C-labeling experiments

Riascos, Carlos A.M.; Gombert, Andreas; Silva, Luiziane F.; Taciro, Marilda Keico; Gomez, José Gregório Cabrera; Roux, G.

doi:10.1016/b978-0-444-63234-0.50021-x

Cited by 10 publications

(8 citation statements)

References 7 publications

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“…LFM046 was isolated from sugarcane soil in Brazil as a polyhydroxyalkanoate (PHA MCL ) producer ( 1 ), presenting outstanding performance in producing such polymers from carbohydrates ( 2 , 3 ) or plant oils ( 4 ) when compared to the reference strain Pseudomonas putida KT2440. Previous flux analysis of central metabolism using [U- 13 C]glucose revealed an atypical behavior ( 5 ), suggesting that Pseudomonas sp. LFM046 could be a new platform for the synthesis of other biobased products.…”

Section: Genome Announcementmentioning

confidence: 99%

Draft Genome Sequence of Pseudomonas sp. Strain LFM046, a Producer of Medium-Chain-Length Polyhydroxyalkanoate

et al. 2015

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Section: Genome Announcementmentioning

confidence: 99%

Draft Genome Sequence of Pseudomonas sp. Strain LFM046, a Producer of Medium-Chain-Length Polyhydroxyalkanoate

et al. 2015

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“…Mathematical models, which lay at the core of this paradigm, have benefited a lot from the combination of several disciplines, ranging from biotechnology, chemical engineering and data science [14,15]. Mechanistic models that describe the metabolic behavior of the microorganism are available for several PHA producing strains [16][17][18][19][20]. Unfortunately, these models involve systems of equations with several parameters that may require almost complete knowledge of the metabolic network and complex analytical methods (e.g., omic measurement) to estimate them.…”

Section: Introductionmentioning

confidence: 99%

Modeling of Continuous PHA Production by a Hybrid Approach Based on First Principles and Machine Learning

et al. 2021

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Polyhydroxyalkanoates (PHA) are renewable alternatives to traditional oil-derived polymers. PHA can be produced by different microorganisms in continuous culture under specific media composition, which makes the production process both promising and challenging. In order to achieve large productivities while maintaining high yield and efficiency, the continuous culture needs to be operated in the so-called dual nutrient limitation condition, where both the nitrogen and carbon sources are kept at very low concentrations. Mathematical models can greatly assist both design and operation of the bioprocess, but are challenged by the complexity of the system, in particular by the dual nutrient-limited growth phenomenon, where the cells undergo a metabolic shift that abruptly changes their behavior. Traditional, non-structured mechanistic models based on Monod uptake kinetics can be used to describe the bioreactor operation under specific process conditions. However, in the absence of a model description of the metabolic phenomena inside the cell, the extrapolation to a broader operation domain (e.g., different feeding concentrations and dilution rates) may present mismatches between the predictions and the actual process outcomes. Such detailed models may require almost perfect knowledge of the cell metabolism and omic-level measurements, hampering their development. On the other hand, purely data-driven models that learn correlations from experimental data do not require any prior knowledge of the process and are therefore unbiased and flexible. However, many more data are required for their development and their extrapolation ability is limited to conditions that are similar to the ones used for training. An attractive alternative is the combination of the extrapolation power of first principles knowledge with the flexibility of machine learning methods. This approach results in a hybrid model for the growth and uptake rates that can be used to predict the dynamic operation of the bioreactor. Here we develop a hybrid model to describe the continuous production of PHA by Pseudomonas putida GPo1 culture. After training, the model with experimental data gained under different dilution rates and medium compositions, we demonstrate how the model can describe the process in a wide range of operating conditions, including both single and dual nutrient-limited growth.

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“…Usually, the 13 C-MFA method is applied under growth conditions and the labeling measurements of amino acids are available (SAUER, 2006). However, PHA and RHL production by P. aeruginosa mainly occurs under non-growth conditions and, in this case, only the measurements of 3HAs/3HAAs mass isotopomer are available (RIASCOS et al, 2013;OLIVEIRA et al, 2021b;OLIVEIRA et al, 2021c).…”

Section: Case Study: Glucose Metabolism Of Pseudomonas Aerugionosa Un...mentioning

confidence: 99%

“…In order to reduce the cost of the experiments to be performed, mixtures between Principal component based identifiability analysis Riascos et al (2013) estimated the metabolic fluxes ratios on P. aeruginosa using data from an experiment with 80% of [U-13 C]glucose and made the hypotheses that the ED pathway operates only in linear mode and the PP pathway operates without reversible reactions. However, those hypotheses may not necessarily valid and, in this case, these pathways would shuffle the labeling of PHA mass isotopomers (KOHLSTEDT; WITTMANN, 2019).…”

Section: Fisher Information Matrix Based Identifiability Analysismentioning

confidence: 99%

Métodos numéricos para modelos de redes metabólicas.

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Começo agradecendo ao meu orientador Prof. Galo Le Roux por todo o apoio e incentivo durante todos esses anos, e por entender que o trajeto de um doutorando vai muito além da pesquisa que ele realiza. Agradeço também ao Prof. José Gregório por todas as ótimas sugestões e discussões durante essa jornada. I would also like to thank Prof.Mahadevan and Prof. Johannes, for having me in their research groups and for contributing to my education as a researcher.Agradeço aos meus caros amigos de casa: Leonardo, Gustavo, Rafael (Vaca) e Gabriel (Vila) por todos os debates sobre política, futebol, filosofia e economia. Aos meus caros amigos que fiz nesses seis anos em São Paulo: Priscila (Jesus e Paz), Irena, Zé Otávio, Zé Eduardo, María, Javier, Dielle e Dondon pela amizade e pelo café de todo dia.À Carol pelo apoio em diversos aspectos desse trabalho e pela amizade. Thank to all my colleagues and friends from Toronto and Trondheim that made me feel at home. Eu gostaria de agradecer à agência de fomento Coordenação de Aperfeiçoamento de pessoal de Nível Superior (CAPES) pelo apoio financeiro nacional e para a realização do doutorado sanduíche.Agradeço à Tamires pela parceria e por me fazer conseguir superar todas as adversidades durante a elaboração dessa tese. Por fim, gostaria de agradecer à minha família por tornar possível a realização dessa tese e por acreditar em mim sempre.

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Metabolic pathways analysis in PHAs production by Pseudomonas with 13C-labeling experiments

Cited by 10 publications

References 7 publications

Draft Genome Sequence of Pseudomonas sp. Strain LFM046, a Producer of Medium-Chain-Length Polyhydroxyalkanoate

Draft Genome Sequence of Pseudomonas sp. Strain LFM046, a Producer of Medium-Chain-Length Polyhydroxyalkanoate

Modeling of Continuous PHA Production by a Hybrid Approach Based on First Principles and Machine Learning

Métodos numéricos para modelos de redes metabólicas.

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