Browsing by Author "Saa, Pedro A."

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    Complex Kinetic Models Predict β-Carotene Production and Reveal Flux Limitations in Recombinant Saccharomyces cerevisiae StrainsClick to copy article link
    (2025) Elizondo, Benjamín R.; Saa, Pedro A.
    β-Carotene is a high-value compound with multiple commercial applications as a pigment and due to its antioxidant properties. For its industrial production, precision fermentation using engineered microorganisms has been proposed as an attractive alternative given consumer concerns and technical limitations of traditional production methods such as chemical synthesis and extraction from plants. However, the factors limiting microbial production are complex and remain poorly understood, hindering bioprocess scale-up. To tackle this limitation, we built and evaluated kinetic model ensembles of the native mevalonate and the heterologous β-carotene production pathways in recombinant Saccharomyces cerevisiae strains to identify bottlenecks limiting the production flux. For this task, flux and transcriptomic data from chemostat cultivations were generated and combined with literature information for simulating model structures capturing different degrees of kinetic detail and complexity within the ABC-GRASP framework. Our results showed that detailed kinetic models including both allosteric regulation and complex mechanistic descriptions (e.g., enzyme promiscuity) are necessary to explain the metabolic phenotype of recombinant strains in different conditions. Calculation of flux and concentration response coefficients of the detailed models revealed that the promiscuous CrtYB enzyme exerts the highest control over β-carotene production at different growth rates in the best producer. Simulation of various enzyme and metabolite perturbations confirmed the above result and discarded other seemingly intuitive targets for intervention, e.g., upregulation of ERG10. Overall, this work deepens our understanding about the factors limiting β-carotene production in yeast, providing mechanistic models for in silico metabolic prospection and rational design of genetic interventions.
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    Genome-scale metabolic modeling of the human milk oligosaccharide utilization by Bifidobacterium longum subsp. infantis
    (2024) Román Lagos, Loreto Andrea; Melis-Arcos, Felipe; Pröschle, Tomás; Saa, Pedro A.; Garrido, Daniel; Gilbert, Jack A.
    Bifidobacterium longum subsp. infantis is a representative and dominant species in the infant gut and is considered a beneficial microbe. This organism displays multiple adaptations to thrive in the infant gut, regarded as a model for human milk oligosaccharides (HMOs) utilization. These carbohydrates are abundant in breast milk and include different molecules based on lactose. They contain fucose, sialic acid, and N-acetylglucosamine. Bifidobacterium metabolism is complex, and a systems view of relevant metabolic pathways and exchange metabolites during HMO consumption is missing. To address this limitation, a refined genome-scale network reconstruction of this bacterium is presented using a previous reconstruction of B. infantis ATCC 15967 as a template. The latter was expanded based on an extensive revision of genome annotations, current literature, and transcriptomic data integration. The metabolic reconstruction (iLR578) accounted for 578 genes, 1,047 reactions, and 924 metabolites. Starting from this reconstruction, we built context-specific genome-scale metabolic models using RNA-seq data from cultures growing in lactose and three HMOs. The models revealed notable differences in HMO metabolism depending on the functional characteristics of the substrates. Particularly, fucosyl-lactose showed a divergent metabolism due to a fucose moiety. High yields of lactate and acetate were predicted under growth rate maximization in all conditions, whereas formate, ethanol, and 1,2-propanediol were substantially lower. Similar results were also obtained under near-optimal growth on each substrate when varying the empirically observed acetate-to-lactate production ratio. Model predictions displayed reasonable agreement between central carbon metabolism fluxes and expression data across all conditions. Flux coupling analysis revealed additional connections between succinate exchange and arginine and sulfate metabolism and a strong coupling between central carbon reactions and adenine metabolism. More importantly, specific networks of coupled reactions under each carbon source were derived and analyzed. Overall, the presented network reconstruction constitutes a valuable platform for probing the metabolism of this prominent infant gut bifidobacteria.
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    Improved screening of COVID-19 cases through a Bayesian network symptoms model and psychophysical olfactory test
    (2021) Eyheramendy, Susana ; Saa, Pedro A. ; Undurraga, Eduardo A. ; Valencia, Carlos ; López, Carolina ; Méndez, Luis ; Pizarro-Berdichevsky, Javier ; Finkelstein-Kulka, Andrés ; Solari, Sandra ; Salas, Nicolás ; Bahamondes, Pedro ; Ugarte, Martín ; Barceló, Pablo ; Arenas, Marcelo ; Agosin, Eduardo
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    Robust control of fed-batch high-cell density cultures: a simulation-based assessment
    (2021) Ibanez, Francisco; Saa, Pedro A.; Bárzaga Martell, Lisbel; Duarte-Mermoud, Manuel A.; Fernandez-Fernandez, Mario; Agosin, Eduardo; Perez Correa, Jose Ricardo
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    Screening of COVID-19 cases through a Bayesian network symptoms model and psychophysical olfactory test
    (CELL PRESS, 2021) Eyheramendy, Susana; Saa, Pedro A.; Undurraga, Eduardo A.; Valencia, Carlos; Lopez, Carolina; Mendez, Luis; Pizarro Berdichevsky, Javier; Finkelstein Kulka, Andres; Solari, Sandra; Salas, Nicolas; Bahamondes, Pedro; Ugarte, Martin; Barcelo, Pablo; Arenas, Marcelo; Agosin, Eduardo
    The sudden loss of smell is among the earliest and most prevalent symptoms of COVID-19 when measured with a clinical psychophysical test. Research has shown the potential impact of frequent screening for olfactory dysfunction, but existing tests are expensive and time consuming. We developed a low-cost ($0.50/test) rapid psychophysical olfactory test (KOR) for frequent testing and a model-based COVID-19 screening framework using a Bayes Network symptoms model. We trained and validated the model on two samples: suspected COVID-19 cases in five healthcare centers (n = 926; 33% prevalence, 309 RT-PCR confirmed) and healthy miners (n = 1,365; 1.1% prevalence, 15 RT-PCR confirmed). The model predicted COVID-19 status with 76% and 96% accuracy in the healthcare and miners samples, respectively (healthcare: AUC = 0.79 [0.75-0.82], sensitivity: 59%, specificity: 87%; miners: AUC = 0.71 [0.63-0.79], sensitivity: 40%, specificity: 97%, at 0.50 infection probability threshold). Our results highlight the potential for low-cost, frequent, accessible, routine COVID-19 testing to support society's reopening.