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Your search keyword '"Sánchez, Benjamín J."' showing total 4 results
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4 results on '"Sánchez, Benjamín J."'

1. MEMOTE for standardized genome-scale metabolic model testing

Author

Research Council of Norway, Innovation Fund Denmark, European Commission, National Institutes of Health (US), German Research Foundation, Novo Nordisk Foundation, W. M. Keck Foundation, Ministerio de Economía y Competitividad (España), Knut and Alice Wallenberg Foundation, Federal Ministry of Education and Research (Germany), Bill & Melinda Gates Foundation, National Research Foundation of Korea, Rural Development Administration (South Korea), Swiss National Science Foundation, University of Oxford, European Research Council, Washington Research Foundation, National Institute of General Medical Sciences (US), Lieven, Christian, Beber, Moritz E., Olivier, Brett G., Bergmann, Frank T., Ataman, Meric, Babaei, Parizad, Bartell, Jennifer A., Blank, Lars M., Chauhan, Siddharth, Correia, Kevin, Diener, Christian, Dräger, Andreas, Ebert, Birgitta E., Edirisinghe, Janaka N., Faria, José P., Feist, Adam M., Fengos, Georgios, Fleming, Ronan M. T., García-Jiménez, Beatriz, Hatzimanikatis, Vassily, Van Helvoirt, Wout, Henry, Christopher S., Hermjakob, Henning, Herrgård, Markus J., Kaafarani, Ali, Kim, Hyun Uk, King, Zachary, Klamt, Steffen, Klipp, Edda, Koehorst, Jasper J., König, Matthias, Lakshmanan, Meiyappan, Lee, Dong-Yup, Lee, Sang Yup, Lee, Sunjae, Lewis, Nathan E., Liu, Filipe, Ma, Hongwu, Machado, Daniel, Mahadevan, Radhakrishnan, Maia, Paulo, Mardinoglu, Adil, Medlock, Gregory L., Monk, Jonathan M., Nielsen, Jens, Nielsen, Lars K., Nogales, Juan, Nookaew, Intawat, Palsson, Bernhard Ø, Papin, Jason A., Patil, Kiran R., Poolman, Mark, Price, Nathan D., Resendis-Antonio, Osbaldo, Richelle, Anne, Rocha, Isabel, Sánchez, Benjamín J., Schaap, Peter J., Malik Sheriff, Rahuman S., Shoaie, Saeed, Sonnenschein, Nikolaus, Teusink, Bas, Vilaça, Paulo, Vik, Jon Olav, Wodke, Judith A. H., Xavier, Joana C., Yuan, Qianqian, Zakhartsev, Maksim, Zhang, Cheng, Research Council of Norway, Innovation Fund Denmark, European Commission, National Institutes of Health (US), German Research Foundation, Novo Nordisk Foundation, W. M. Keck Foundation, Ministerio de Economía y Competitividad (España), Knut and Alice Wallenberg Foundation, Federal Ministry of Education and Research (Germany), Bill & Melinda Gates Foundation, National Research Foundation of Korea, Rural Development Administration (South Korea), Swiss National Science Foundation, University of Oxford, European Research Council, Washington Research Foundation, National Institute of General Medical Sciences (US), Lieven, Christian, Beber, Moritz E., Olivier, Brett G., Bergmann, Frank T., Ataman, Meric, Babaei, Parizad, Bartell, Jennifer A., Blank, Lars M., Chauhan, Siddharth, Correia, Kevin, Diener, Christian, Dräger, Andreas, Ebert, Birgitta E., Edirisinghe, Janaka N., Faria, José P., Feist, Adam M., Fengos, Georgios, Fleming, Ronan M. T., García-Jiménez, Beatriz, Hatzimanikatis, Vassily, Van Helvoirt, Wout, Henry, Christopher S., Hermjakob, Henning, Herrgård, Markus J., Kaafarani, Ali, Kim, Hyun Uk, King, Zachary, Klamt, Steffen, Klipp, Edda, Koehorst, Jasper J., König, Matthias, Lakshmanan, Meiyappan, Lee, Dong-Yup, Lee, Sang Yup, Lee, Sunjae, Lewis, Nathan E., Liu, Filipe, Ma, Hongwu, Machado, Daniel, Mahadevan, Radhakrishnan, Maia, Paulo, Mardinoglu, Adil, Medlock, Gregory L., Monk, Jonathan M., Nielsen, Jens, Nielsen, Lars K., Nogales, Juan, Nookaew, Intawat, Palsson, Bernhard Ø, Papin, Jason A., Patil, Kiran R., Poolman, Mark, Price, Nathan D., Resendis-Antonio, Osbaldo, Richelle, Anne, Rocha, Isabel, Sánchez, Benjamín J., Schaap, Peter J., Malik Sheriff, Rahuman S., Shoaie, Saeed, Sonnenschein, Nikolaus, Teusink, Bas, Vilaça, Paulo, Vik, Jon Olav, Wodke, Judith A. H., Xavier, Joana C., Yuan, Qianqian, Zakhartsev, Maksim, and Zhang, Cheng

Abstract

Reconstructing metabolic reaction networks enables the development of testable hypotheses of an organism’s metabolism under different conditions1. State-of-the-art genome-scale metabolic models (GEMs) can include thousands of metabolites and reactions that are assigned to subcellular locations. Gene–protein–reaction (GPR) rules and annotations using database information can add meta-information to GEMs. GEMs with metadata can be built using standard reconstruction protocols2, and guidelines have been put in place for tracking provenance and enabling interoperability, but a standardized means of quality control for GEMs is lacking3. Here we report a community effort to develop a test suite named MEMOTE (for metabolic model tests) to assess GEM quality.

Published
2020

2. Author Correction: A consensus S. cerevisiae metabolic model Yeast8 and its ecosystem for comprehensively probing cellular metabolism.

Author

Lu, Hongzhong, Li, Feiran, Sánchez, Benjamín J., Zhu, Zhengming, Li, Gang, Domenzain, Iván, Marcišauskas, Simonas, Anton, Petre Mihail, Lappa, Dimitra, Lieven, Christian, Beber, Moritz Emanuel, Sonnenschein, Nikolaus, Kerkhoven, Eduard J., and Nielsen, Jens

Subjects
METABOLIC models, METABOLISM, ECOSYSTEMS, AUTHORS
Abstract

An amendment to this paper has been published and can be accessed via a link at the top of the paper. [ABSTRACT FROM AUTHOR]

Published
2020
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3. Combining mechanistic and machine learning models for predictive engineering and optimization of tryptophan metabolism.

Author

Zhang, Jie, Petersen, Søren D., Radivojevic, Tijana, Ramirez, Andrés, Pérez-Manríquez, Andrés, Abeliuk, Eduardo, Sánchez, Benjamín J., Costello, Zak, Chen, Yu, Fero, Michael J., Martin, Hector Garcia, Nielsen, Jens, Keasling, Jay D., and Jensen, Michael K.

Subjects
MACHINE learning, ENGINEERING models, AMINO acid metabolism, PREDICTION models, SACCHAROMYCES cerevisiae, BACTERIAL metabolism
Abstract

Through advanced mechanistic modeling and the generation of large high-quality datasets, machine learning is becoming an integral part of understanding and engineering living systems. Here we show that mechanistic and machine learning models can be combined to enable accurate genotype-to-phenotype predictions. We use a genome-scale model to pinpoint engineering targets, efficient library construction of metabolic pathway designs, and high-throughput biosensor-enabled screening for training diverse machine learning algorithms. From a single data-generation cycle, this enables successful forward engineering of complex aromatic amino acid metabolism in yeast, with the best machine learning-guided design recommendations improving tryptophan titer and productivity by up to 74 and 43%, respectively, compared to the best designs used for algorithm training. Thus, this study highlights the power of combining mechanistic and machine learning models to effectively direct metabolic engineering efforts. In metabolic engineering, mechanistic models require prior metabolism knowledge of the chassis strain, whereas machine learning models need ample training data. Here, the authors combine the mechanistic and machine learning models to improve prediction performance of tryptophan metabolism in baker's yeast. [ABSTRACT FROM AUTHOR]

Published
2020
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4. A consensus S. cerevisiae metabolic model Yeast8 and its ecosystem for comprehensively probing cellular metabolism.

Author

Lu, Hongzhong, Li, Feiran, Sánchez, Benjamín J., Zhu, Zhengming, Li, Gang, Domenzain, Iván, Marcišauskas, Simonas, Anton, Petre Mihail, Lappa, Dimitra, Lieven, Christian, Beber, Moritz Emanuel, Sonnenschein, Nikolaus, Kerkhoven, Eduard J., and Nielsen, Jens

Subjects
SACCHAROMYCES cerevisiae, CELL metabolism, PROTEIN structure, YEAST, METABOLOMICS
Abstract

Genome-scale metabolic models (GEMs) represent extensive knowledgebases that provide a platform for model simulations and integrative analysis of omics data. This study introduces Yeast8 and an associated ecosystem of models that represent a comprehensive computational resource for performing simulations of the metabolism of Saccharomyces cerevisiae––an important model organism and widely used cell-factory. Yeast8 tracks community development with version control, setting a standard for how GEMs can be continuously updated in a simple and reproducible way. We use Yeast8 to develop the derived models panYeast8 and coreYeast8, which in turn enable the reconstruction of GEMs for 1,011 different yeast strains. Through integration with enzyme constraints (ecYeast8) and protein 3D structures (proYeast8DB), Yeast8 further facilitates the exploration of yeast metabolism at a multi-scale level, enabling prediction of how single nucleotide variations translate to phenotypic traits. Genome-scale metabolic models provide a platform to study metabolism through simulations and analysis of omics data. Here the authors introduce Yeast8 with its model ecosystem, a comprehensive computational resource for simulating the metabolism of Saccharomyces cerevisiae. [ABSTRACT FROM AUTHOR]

Published
2019
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